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sandersaarond

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Grafana-Community-Dashboards

like 2

pisugar

PiSugar

Dashboard to monitor PiSugar battery

2022-11-13 22:16:03 UTC

2022-11-13 22:23:03 UTC

Dashboard to monitor PiSugar battery

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2022-11-13 22:16:03 UTC

metricbeat-system

metricbeat-system

System metrics from metricbeat to elasticsearch by nasuyun

2022-11-21 10:38:37 UTC

2022-11-25 13:26:34 UTC

metricbeat for nasuyun cloud elasticsearch by nasuyun

Beats

System metrics from metricbeat to elasticsearch

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"type": "elasticsearch", "uid": "${DS_METRICBEAT}" }, "dsType": "elasticsearch", "hide": false, "metrics": [ { "field": "system.diskio.iostat.write.request.per_sec", "id": "1", "meta": {}, "pipelineAgg": "select metric", "settings": {}, "type": "avg" } ], "query": "metricset.module:system AND metricset.name:diskio AND beat.name:$hostname", "refId": "B", "timeField": "@timestamp" } ], "title": "Diskio", "type": "timeseries" }, { "datasource": { "type": "elasticsearch", "uid": "${DS_METRICBEAT}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": true, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": 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"hide": 0, "name": "Interval", "options": [ { "selected": false, "text": "5s", "value": "5s" }, { "selected": false, "text": "10s", "value": "10s" }, { "selected": true, "text": "1m", "value": "1m" }, { "selected": false, "text": "2m", "value": "2m" } ], "query": "5s,10s,1m,2m", "queryValue": "", "refresh": 2, "skipUrlSync": false, "type": "interval" }, { "datasource": { "uid": "metricbeat" }, "filters": [], "hide": 0, "name": "Filter", "skipUrlSync": false, "type": "adhoc" } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "browser", "title": "metricbeat-system", "uid": "ubFKKpdVz3", "version": 3, "weekStart": "" }

2022-11-21 10:38:37 UTC

2022-11-25 13:26:19 UTC

vmware-overview

VMware Overview

Uses prometheus queries via pryorda/vmware_exporter

2023-02-01 02:58:17 UTC

2023-02-02 05:56:35 UTC

Referenced from：https://grafana.com/grafana/dashboards/11243-vmware-stats/ How to instan vmware_exporter: ## 1.vim docker-comose.yml ``` version: '3' services: vmware_exporter: image: pryorda/vmware_exporter:latest restart: always hostname: vmware_exporter container_name: vmware_exporter environment: - VSPHERE_USER=administrator@vsphere.local - VSPHERE_PASSWORD=123456 - VSPHERE_HOST=192.168.1.1 - VSPHERE_IGNORE_SSL=True - VSPHERE_SPECS_SIZE=2000 ports: - 9272:9272 ``` ## 2.pull image and run ``` docker-compose -f docker-compose.yml up -d ```

Uses prometheus queries via pryorda/vmware_exporter

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"refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ] }, "timezone": "browser", "title": "VMware Overview", "uid": "q1yCDNbWz", "version": 13, "weekStart": "" }

2023-02-01 02:58:17 UTC

geth-multiple

Geth: Multiple

Various metrics of multiple geth instances

2023-03-17 09:13:23 UTC

2023-03-17 09:15:23 UTC

This dashboard shows metrics for multiple [geth](https://geth.ethereum.org/) instances. Read the [metrics documentation](https://geth.ethereum.org/docs/monitoring/metrics) for further information on how to setup metrics.

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"timeRegions": [], "title": "Transaction slots", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "none", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } } ], "refresh": "1m", "schemaVersion": 37, "style": "dark", "tags": [ "geth" ], "templating": { "list": [ { "datasource": { "type": "prometheus", "uid": "UhcO3vy7z" }, "filters": [ { "condition": "", "key": "ingress_user", "operator": "=", "value": "bootnodes-mainnet" } ], "hide": 0, "name": "filter", "skipUrlSync": false, "type": "adhoc" } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ] }, "timezone": "", "title": "Geth: Multiple", "uid": "Q31ATs5Zz1", "version": 19, "weekStart": "", "gnetId": 18291, "description": "Various metrics of multiple geth instances" }

2023-03-17 09:13:23 UTC

2023-03-17 09:15:21 UTC

mirror-nodes-comparison-maciej

Mirror nodes comparison [Maciej]

Dashboard to track Filecoin Lotus node API endpoints.

2023-04-10 11:18:54 UTC

2023-04-10 11:19:37 UTC

Dashboard to track Filecoin Lotus node API endpoints.

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2023-04-10 11:18:54 UTC

kubernetes-events-custom-controller

Kubernetes Events (Custom Controller)

Kubernetes Events from custom Controller

2023-05-08 13:52:42 UTC

2023-05-08 13:58:48 UTC

Other

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2023-05-08 13:52:42 UTC

2023-05-08 13:56:58 UTC

cosmos-dashboard

Cosmos Dashboard

A Grafana dashboard compatible with all the cosmos-sdk and tendermint based blockchains.

2022-11-30 17:01:39 UTC

2022-12-01 20:31:04 UTC

collectd

A Grafana dashboard compatible with all the cosmos-sdk and tendermint based blockchains.

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2022-11-30 17:01:39 UTC

2022-12-01 02:32:45 UTC

pv-power-ahoy

PV Power Ahoy

Visualise power metrics coming from AHOY-DTU a

2023-01-10 21:36:14 UTC

2023-01-10 21:47:26 UTC

Visualise power metrics coming from AHOY-DTU and OpenWeatherMap

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[], "type": "last" } ] ], "tags": [ { "key": "topic", "operator": "=", "value": "inverter/Hoymiles/ch0/YieldDay" } ] } ], "timeFrom": "1w", "title": "Daily Production", "type": "barchart" }, { "collapsed": true, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 23 }, "id": 74, "panels": [ { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 2, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "blue", "value": null } ] }, "unit": "volt" }, "overrides": [] }, "gridPos": { "h": 3, "w": 3, "x": 0, "y": 24 }, "id": 65, "interval": "10s", "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "value" }, "pluginVersion": "9.1.2", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, 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"fields": "", "values": false }, "textMode": "value" }, "pluginVersion": "9.1.2", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "measurement": "mqtt_consumer", "orderByTime": "ASC", "policy": "default", "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "last" } ] ], "tags": [ { "key": "topic", "operator": "=", "value": "inverter/Hoymiles/ch0/Freq" } ] } ], "title": "Freq", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 2, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "blue", "value": null }, { "color": "green", "value": 60 }, { "color": "orange", "value": 70 } ] }, "unit": "celsius" }, "overrides": [] }, "gridPos": { "h": 3, "w": 3, "x": 6, "y": 24 }, "id": 34, "interval": "10s", "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "value" }, "pluginVersion": "9.1.2", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "measurement": "mqtt_consumer", "orderByTime": "ASC", "policy": "default", "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "last" } ] ], "tags": [ { "key": "topic", "operator": "=", "value": "inverter/Hoymiles/ch0/Temp" } ] } ], "title": "Inverter Temp", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 2, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "blue", "value": null }, { "color": "orange", "value": 85 }, { "color": "green", "value": 95 } ] }, "unit": "percent" }, "overrides": [] }, "gridPos": { "h": 3, "w": 3, "x": 9, "y": 24 }, "id": 67, "interval": "10s", "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "value" }, "pluginVersion": "9.1.2", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "measurement": "mqtt_consumer", "orderByTime": "ASC", "policy": "default", "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "last" } ] ], "tags": [ { "key": "topic", "operator": "=", "value": "inverter/Hoymiles/ch0/Efficiency" } ] } ], "title": "Efficiency", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 2, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "blue", "value": null }, { "color": "orange", "value": 85 }, { "color": "green", "value": 95 } ] }, "unit": "percent" }, "overrides": [] }, "gridPos": { "h": 3, "w": 3, "x": 12, "y": 24 }, "id": 70, "interval": "10s", "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "value" }, "pluginVersion": "9.1.2", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "measurement": "mqtt_consumer", "orderByTime": "ASC", "policy": "default", "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "last" } ] ], "tags": [ { "key": "topic", "operator": "=", "value": "inverter/Hoymiles/ch0/PowerLimit" } ] } ], "title": "Power Limit", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 2, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "blue", "value": null }, { "color": "orange", "value": 85 }, { "color": "green", "value": 95 } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 3, "w": 3, "x": 15, "y": 24 }, "id": 69, "interval": "10s", "options": { "colorMode": "value", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/^mqtt_consumer\\.last$/", "values": false }, "textMode": "value" }, "pluginVersion": "9.1.2", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "measurement": "mqtt_consumer", "orderByTime": "ASC", "policy": "default", "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "last" } ] ], "tags": [ { "key": "topic", "operator": "=", "value": "inverter/Hoymiles/ch0/ALARM_MES_ID" } ] } ], "title": "Alarm", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 20, "gradientMode": "opacity", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "decimals": 0, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "blue", "value": null }, { "color": "yellow", "value": 50 }, { "color": "orange", "value": 200 }, { "color": "red", "value": 600 } ] }, "unit": "watt" }, "overrides": [ { "matcher": { "id": "byName", "options": "Power Ost" }, "properties": [ { "id": "color", "value": { "fixedColor": "orange", "mode": "fixed", "seriesBy": "last" } }, { "id": "custom.fillOpacity", "value": 20 } ] }, { "matcher": { "id": "byName", "options": "Power West" }, "properties": [ { "id": "color", "value": { "fixedColor": "red", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "Irridation West" }, "properties": [ { "id": "color", "value": { "fixedColor": "#8AB8FF", "mode": "fixed" } }, { "id": "custom.axisPlacement", "value": "right" }, { "id": "unit", "value": "percent" }, { "id": "custom.lineInterpolation", "value": "smooth" }, { "id": "custom.axisSoftMin", "value": 0 }, { "id": "custom.axisSoftMax", "value": 100 } ] }, { "matcher": { "id": "byName", "options": "Irridation Ost" }, "properties": [ { "id": "color", "value": { "fixedColor": "purple", "mode": "fixed" } }, { "id": "custom.axisPlacement", "value": "right" }, { "id": "unit", "value": "percent" }, { "id": "custom.lineInterpolation", "value": "smooth" }, { "id": "custom.axisSoftMin", "value": 0 }, { "id": "custom.axisSoftMax", "value": 100 } ] } ] }, "gridPos": { "h": 19, "w": 24, "x": 0, "y": 27 }, "id": 64, "interval": "10s", "options": { "legend": { "calcs": [ "max" ], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "targets": [ { "alias": "Power West", "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "hide": false, "measurement": "mqtt_consumer", "orderByTime": "ASC", "policy": "default", "refId": "B", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "mean" } ] ], "tags": [ { "key": "topic", "operator": "=", "value": "inverter/Hoymiles/ch1/P_DC" } ] }, { "alias": "Power Ost", "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "hide": false, "measurement": "mqtt_consumer", "orderByTime": "ASC", "policy": "default", "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "mean" } ] ], "tags": [ { "key": "topic", "operator": "=", "value": "inverter/Hoymiles/ch2/P_DC" } ] }, { "alias": "Irridation West", "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "hide": false, "measurement": "mqtt_consumer", "orderByTime": "ASC", "policy": "default", "refId": "D", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "mean" } ] ], "tags": [ { "key": "topic", "operator": "=", "value": "inverter/Hoymiles/ch1/Irradiation" } ] }, { "alias": "Irridation Ost", "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "hide": false, "measurement": "mqtt_consumer", "orderByTime": "ASC", "policy": "default", "refId": "C", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "mean" } ] ], "tags": [ { "key": "topic", "operator": "=", "value": "inverter/Hoymiles/ch2/Irradiation" } ] } ], "title": "Power", "transformations": [], "type": "timeseries" } ], "title": "Details", "type": "row" } ], "refresh": "1m", "schemaVersion": 37, "style": "dark", "tags": [], "templating": { "list": [ { "current": { "selected": false, "text": "0.23", "value": "0.23" }, "hide": 2, "includeAll": false, "multi": false, "name": "kwhPrice", "options": [ { "selected": true, "text": "0.23", "value": "0.23" } ], "query": "0.23", "skipUrlSync": false, "type": "custom" }, { "current": {}, "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "definition": "SHOW TAG VALUES FROM \"weather\" WITH KEY = \"country\"", "hide": 0, "includeAll": true, "label": "Country", "multi": true, "name": "country", "options": [], "query": "SHOW TAG VALUES FROM \"weather\" WITH KEY = \"country\"", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "type": "query" }, { "current": {}, "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "definition": "SHOW TAG VALUES FROM \"weather\" WITH KEY = \"city\"", "hide": 0, "includeAll": true, "label": "City", "multi": true, "name": "city", "options": [], "query": "SHOW TAG VALUES FROM \"weather\" WITH KEY = \"city\"", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "type": "query" } ] }, "time": { "from": "now/d", "to": "now/d" }, "timepicker": {}, "timezone": "", "title": "PV Power Ahoy", "uid": "opBBFjWVz", "version": 31, "weekStart": "", "gnetId": 17850, "description": "Visualise power metrics coming from AHOY-DTU a" }

2023-01-10 21:36:14 UTC

2023-01-10 21:47:26 UTC

traefik-dashboard

Traefik Dashboard

Visualizing Traefik Reverse Proxy Stats

2023-01-22 22:01:03 UTC

2023-01-22 22:05:10 UTC

Grafana dashboard to visualize various stats of a Traefik reverse proxy.

Visualizing Traefik Reverse Proxy Stats

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Grafana --" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "description": "Visualizing Traefik Reverse Proxy Stats", "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 0, "id": null, "links": [], "liveNow": false, "panels": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB-TRAEFIK}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "match": "null", "result": { "text": "N/A" } }, "type": "special" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 9, "w": 2, "x": 0, "y": 0 }, "id": 14, "links": [], "maxDataPoints": 100, "options": { "colorMode": "none", "graphMode": "none", "justifyMode": "auto", "orientation": "horizontal", 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"shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "decimals": 0, "format": "none", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": false } ], "yaxis": { "align": false } }, { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "fieldConfig": { "defaults": { "color": { "mode": "continuous-RdYlGr" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 25, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "smooth", "lineStyle": { "dash": [ 10, 10 ], "fill": "dash" }, "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "always", "spanNulls": true, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "semi-dark-red", "value": null } ] } }, "overrides": [] }, "gridPos": { "h": 7, "w": 12, "x": 0, "y": 9 }, "id": 4, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": false }, "timezone": [ "Europe/Berlin" ], "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "(count_over_time({job=\"traefiklogs\"}[$__interval]))", "queryType": "range", "refId": "A" } ], "title": "Log Volume", "type": "timeseries" }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB-TRAEFIK}" }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 7, "w": 12, "x": 12, "y": 9 }, "hiddenSeries": false, "id": 10, "legend": { "alignAsTable": true, "avg": true, "current": true, "max": true, "min": false, "show": true, "total": false, "values": true }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "9.3.2", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "alias": "$tag_backend", "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB-TRAEFIK}" }, "groupBy": [ { "params": [ "$sampling" ], "type": "time" }, { "params": [ "backend" ], "type": "tag" }, { "params": [ "0" ], "type": "fill" } ], "measurement": "traefik.service.request.duration", "orderByTime": "ASC", "policy": "default", "query": "SELECT mean(\"p99\") FROM \"traefik.service.request.duration\" WHERE (\"backend\" =~ /^$backend$/) AND $timeFilter GROUP BY time($sampling), \"backend\" fill(0)", "rawQuery": true, "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "p99" ], "type": "field" }, { "params": [], "type": "mean" } ] ], "tags": [ { "key": "backend", "operator": "=~", "value": "/^$backend$/" } ] } ], "thresholds": [], "timeRegions": [], "title": "Average response time", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "s", "label": "", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": true, "dashLength": 10, "dashes": false, "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB-TRAEFIK}" }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 7, "w": 12, "x": 0, "y": 16 }, "hiddenSeries": false, "id": 2, "legend": { "alignAsTable": true, "avg": false, "current": false, "max": false, "min": false, "show": false, "total": false, "values": false }, "lines": false, "linewidth": 1, "links": [], "nullPointMode": "connected", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "9.3.2", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "alias": "$tag_code", "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB-TRAEFIK}" }, "groupBy": [ { "params": [ "$interval" ], "type": "time" }, { "params": [ "code" ], "type": "tag" } ], "measurement": "traefik.router.requests.total", "orderByTime": "ASC", "policy": "default", "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "count" ], "type": "field" }, { "params": [], "type": "sum" } ] ], "tags": [] } ], "thresholds": [], "timeRegions": [], "title": "Total status code count", "tooltip": { "shared": false, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "series", "show": true, "values": [ "total" ] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB-TRAEFIK}" }, "fieldConfig": { "defaults": { "color": { "mode": "continuous-BlYlRd" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 32, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineStyle": { "fill": "solid" }, "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 7, "w": 12, "x": 12, "y": 16 }, "id": 6, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": false }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB-TRAEFIK}" }, "groupBy": [ { "params": [ "10m" ], "type": "time" }, { "params": [ "0" ], "type": "fill" } ], "measurement": "traefik.service.connections.open", "orderByTime": "ASC", "policy": "default", "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "count" } ] ], "tags": [] } ], "title": "Open Connections", "type": "timeseries" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "description": "", "gridPos": { "h": 12, "w": 24, "x": 0, "y": 23 }, "id": 8, "options": { "dedupStrategy": "exact", "enableLogDetails": true, "prettifyLogMessage": false, "showCommonLabels": false, "showLabels": false, "showTime": false, "sortOrder": "Descending", "wrapLogMessage": false }, "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "{job=\"traefiklogs\"} | json | line_format \"{{.OriginStatus}} | {{.time}} | {{.RequestAddr}} | {{.ClientHost}} | {{.RequestMethod}} {{.RequestPath}} {{.RequestProtocol}} | {{.request_User_Agent}}\" != \"192.168\" != \"172.\"", "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "maxLines": 800, "orderByTime": "ASC", "policy": "default", "queryType": "range", "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "mean" } ] ], "tags": [] } ], "timeFrom": "72h", "title": "Traefik JSON Logs", "type": "logs" } ], "schemaVersion": 37, "style": "dark", "tags": [], "templating": { "list": [ { "current": { "selected": false, "text": "1m", "value": "1m" }, "hide": 0, "includeAll": false, "label": "Sampling", "multi": false, "name": "sampling", "options": [ { "selected": true, "text": "1m", "value": "1m" }, { "selected": false, "text": "10m", "value": "10m" }, { "selected": false, "text": "60m", "value": "60m" } ], "query": "1m,10m,60m", "skipUrlSync": false, "type": "custom" }, { "current": {}, "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB-TRAEFIK}" }, "definition": "select * from \"traefik.backend.requests.total\"", "hide": 0, "includeAll": true, "label": "Backend", "multi": false, "name": "backend", "options": [], "query": "select * from \"traefik.backend.requests.total\"", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" } ] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": {}, "timezone": "browser", "title": "Traefik Dashboard", "uid": "4lvBTAo4k", "version": 4, "weekStart": "monday", "gnetId": 17931 }

2023-01-22 22:01:03 UTC

2023-01-22 22:03:14 UTC

redpanda-default-dashboard

Redpanda Default Dashboard

Redpanda Default Dashboard

2023-02-18 00:08:04 UTC

2023-03-06 13:49:52 UTC

This is the default dashboard for Redpanda. There are many charts on this page, so instead of listing each one here are the sections: - Redpanda summary - internal RPC latency - throughput - errors - IO queue - memory - others (build info, uptime, number of configured brokers, etc.) - raft - scheduler - storage

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"steppedLine": true, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "histogram_quantile(0.99, sum(rate(redpanda_kafka_request_latency_seconds_bucket{instance=~\"$node\", redpanda_request=\"produce\"}[$__rate_interval])) by (le, provider, region, instance, namespace, pod))", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}", "refId": "A", "step": 10 } ], "timeRegions": [], "title": "Latency of Kafka produce requests (p99) per broker", "tooltip": { "msResolution": true, "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "s", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ], "yaxis": { "align": false } }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "gridPos": { "h": 2, "w": 12, "x": 0, "y": 14 }, "id": 8, "options": { "code": { "language": "plaintext", "showLineNumbers": false, "showMiniMap": false }, "content": "<h1 style=\"color:#87CEEB; border-bottom: 3px solid #87CEEB;\">Internal RPC Latency</h1>", "mode": "html" }, "pluginVersion": "9.3.6", "span": 1, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "refId": "A" } ], "transparent": true, "type": "text" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "gridPos": { "h": 2, "w": 12, "x": 12, "y": 14 }, "id": 11, "options": { "code": { "language": "plaintext", "showLineNumbers": false, "showMiniMap": false }, "content": "<h1 style=\"color:#87CEEB; border-bottom: 3px solid #87CEEB;\">Throughput</h1>", "mode": "html" }, "pluginVersion": "9.3.6", "span": 1, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "refId": "A" } ], "transparent": true, "type": "text" }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "fillGradient": 0, "gridPos": { "h": 6, "w": 6, "x": 0, "y": 16 }, "hiddenSeries": false, "id": 9, "interval": "1m", "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null as zero", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "9.3.6", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "span": 4, "stack": false, "steppedLine": true, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "histogram_quantile(0.95, sum(rate(redpanda_rpc_request_latency_seconds_bucket{instance=~\"$node\",redpanda_server=\"internal\"}[$__rate_interval])) by (le, $aggr_criteria))", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}", "refId": "A", "step": 10 } ], "timeRegions": [], "title": "RPC latency (p95)", "tooltip": { "msResolution": true, "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "s", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "fillGradient": 0, "gridPos": { "h": 6, "w": 6, "x": 6, "y": 16 }, "hiddenSeries": false, "id": 10, "interval": "1m", "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null as zero", "options": { 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"${DS_PROMETHEUS}" }, "expr": "sum(redpanda_rest_proxy_request_latency_seconds{instance=~\"$node\"}) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}", "refId": "", "step": 10 } ], "title": "Internal latency of request for rest_proxy", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] }, { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": { "h": 6, "w": 8, "x": 8, "y": 31 }, "id": 52, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": true, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(redpanda_rpc_active_connections{instance=~\"$node\"}) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}, redpanda_server: {{redpanda_server}}", "refId": "", "step": 10 } ], "title": "Count of currently active connections", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] }, { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": { "h": 6, "w": 8, "x": 16, "y": 31 }, "id": 54, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": true, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(redpanda_rpc_request_latency_seconds{instance=~\"$node\"}) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}, redpanda_server: {{redpanda_server}}", "refId": "", "step": 10 } ], "title": "RPC latency", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] }, { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": { "h": 6, "w": 8, "x": 0, "y": 31 }, "id": 58, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": true, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(redpanda_schema_registry_request_latency_seconds{instance=~\"$node\"}) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}", "refId": "", "step": 10 } ], "title": "Internal latency of request for schema_registry", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] } ], "span": 0, "targets": [ { "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "refId": "A" } ], "title": "others", "type": "row" }, { "collapsed": true, "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "editable": true, "error": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 26 }, "id": 47, "panels": [ { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": { "h": 6, "w": 8, "x": 0, "y": 32 }, "id": 46, "interval": "1m", "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(rate(redpanda_raft_leadership_changes{instance=~\"$node\"}[$__rate_interval])) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}, redpanda_namespace: {{redpanda_namespace}}, redpanda_topic: {{redpanda_topic}}", "refId": "", "step": 10 } ], "title": "Rate - Number of leadership changes across all partitions of a given topic", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "ops", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] }, { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": { "h": 6, "w": 8, "x": 8, "y": 32 }, "id": 48, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": true, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(redpanda_raft_recovery_partition_movement_available_bandwidth{instance=~\"$node\"}) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}, shard: {{shard}}", "refId": "", "step": 10 } ], "title": "Bandwidth available for partition movement. bytes/sec", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] } ], "span": 0, "targets": [ { "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "refId": "A" } ], "title": "raft", "type": "row" }, { "collapsed": true, "datasource": { "type": "prometheus", "uid": 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false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": true, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(redpanda_storage_disk_free_bytes{instance=~\"$node\"}) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}", "refId": "", "step": 10 } ], "title": "Disk storage bytes free.", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] }, { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": 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false, "sort": 1, "type": "query" }, { "allFormat": "", "allValue": ".*", "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "", "hide": 0, "includeAll": true, "label": "Shard", "multi": true, "multiFormat": "", "name": "node_shard", "options": [], "query": { "query": "label_values(shard)", "refId": "Prometheus-node_shard-Variable-Query" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "type": "query" }, { "allFormat": "", "allValue": "", "current": { "selected": false, "text": "Cluster", "value": "cluster" }, "datasource": "${DS_PROMETHEUS}", "hide": 0, "includeAll": false, "label": "Aggregate by", "multi": false, "multiFormat": "", "name": "aggr_criteria", "options": [ { "selected": true, "text": "Cluster", "value": "cluster" }, { "selected": false, "text": "Instance", "value": "instance" }, { "selected": false, "text": "Instance,Shard", "value": "instance,shard" } ], "query": "Cluster : cluster,Instance : instance,Instance\\,Shard : instance\\,shard", "refresh": 1, "skipUrlSync": false, "sort": 1, "type": "custom" } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "utc", "title": "Redpanda Default Dashboard", "uid": "d9gY-aJ4k", "version": 1, "weekStart": "", "gnetId": 18134, "description": "Redpanda Default Dashboard" }

2023-02-18 00:08:04 UTC

2023-02-18 00:08:46 UTC

apc-ups

APC UPS

APS UPS monitoring with Prometheus and apcupsd exporter

2023-03-17 12:11:34 UTC

2023-03-17 12:18:59 UTC

Using docker https://hub.docker.com/r/jangrewe/apcupsd-exporter **docker-compose.yml** (to run apcupsd_exporter) ``` version: '3' services: apcupsd: image: jangrewe/apcupsd-exporter restart: unless-stopped network_mode: "host" ``` **prometheus.yml** ``` scrape_configs: - job_name: "apcupsd" static_configs: - targets: ["192.168.0.11:9162"] ```

Other

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"grid": {}, "gridPos": { "h": 7, "w": 8, "x": 16, "y": 0 }, "hiddenSeries": false, "id": 1, "isNew": true, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "links": [], "nullPointMode": "connected", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "9.4.3", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "apcupsd_battery_charge_percent", "intervalFactor": 2, "legendFormat": "{{model}}", "metric": "apcupsd_battery_charge_percent", "refId": "A", "step": 120 } ], "thresholds": [], "timeRegions": [], "title": "Battery Charge Percent", "tooltip": { "msResolution": false, "shared": true, "sort": 0, "value_type": "cumulative" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "percent", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "grid": {}, "gridPos": { "h": 7, "w": 8, "x": 0, "y": 7 }, "hiddenSeries": false, "id": 4, "isNew": true, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "links": [], "nullPointMode": "connected", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "9.4.3", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": 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}, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "volt", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "grid": {}, "gridPos": { "h": 7, "w": 8, "x": 16, "y": 7 }, "hiddenSeries": false, "id": 3, "isNew": true, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "links": [], "nullPointMode": "connected", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "9.4.3", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "apcupsd_line_nominal_volts", "interval": "", "intervalFactor": 2, "legendFormat": "nominal", "metric": "apcupsd_battery_nominal_volts", "refId": "A", "step": 120 }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "apcupsd_line_volts", "intervalFactor": 2, "legendFormat": "actual", "metric": "apcupsd_battery_volts", "refId": "B", "step": 120 } ], "thresholds": [], "timeRegions": [], "title": "AC Voltage", "tooltip": { "msResolution": false, "shared": true, "sort": 0, "value_type": "cumulative" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "volt", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } } ], "refresh": "1m", "revision": 1, "schemaVersion": 38, "style": "dark", "tags": [ "apcupsd" ], "templating": { "list": [] }, "time": { "from": "now-15m", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "browser", "title": "APC UPS", "uid": "TDi8TH8Wk", "version": 3, "weekStart": "", "gnetId": 18294, "description": "APS UPS monitoring with Prometheus and apcupsd exporter" }

2023-03-17 12:11:34 UTC

2023-03-17 12:18:58 UTC

jmeter-dashboard

JMeter Dashboard

Monitor your JMeter load test in real time with InfluxDB and Grafana.

2023-04-15 14:22:38 UTC

2023-04-15 14:22:45 UTC

Monitor your JMeter load test in real time with InfluxDB and Grafana.

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"options": [], "query": "SHOW TAG VALUES FROM \"jmeter\" WITH KEY = \"application\"", "refresh": 1, "regex": "", "sort": 0, "tagValuesQuery": null, "tags": [], "tagsQuery": null, "type": "query", "useTags": false }, { "allValue": null, "current": {}, "datasource": "$data_source", "hide": 0, "includeAll": false, "label": "", "multi": false, "name": "transaction", "options": [], "query": "SHOW TAG VALUES FROM \"jmeter\" WITH KEY = \"transaction\" WHERE \"application\" =~ /^$application$/ AND \"transaction\" != 'internal' AND \"transaction\" != 'all'", "refresh": 1, "regex": "", "sort": 0, "tagValuesQuery": null, "tags": [], "tagsQuery": null, "type": "query", "useTags": false }, { "current": { "value": "${VAR_MEASUREMENT_NAME}", "text": "${VAR_MEASUREMENT_NAME}" }, "hide": 2, "label": "Measurement name", "name": "measurement_name", "options": [ { "value": "${VAR_MEASUREMENT_NAME}", "text": "${VAR_MEASUREMENT_NAME}" } ], "query": "${VAR_MEASUREMENT_NAME}", "type": "constant" }, { "current": { "value": "${VAR_SEND_INTERVAL}", "text": "${VAR_SEND_INTERVAL}" }, "hide": 2, "label": "Backend send interval in second", "name": "send_interval", "options": [ { "value": "${VAR_SEND_INTERVAL}", "text": "${VAR_SEND_INTERVAL}" } ], "query": "${VAR_SEND_INTERVAL}", "type": "constant" } ] }, "time": { "from": "now-30m", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "browser", "title": "JMeter Dashboard", "version": 4 }

2023-04-15 14:22:38 UTC

avid-pay-slo-monitoring

Avid Pay SLO Monitoring

Testing import AvidPay SLO Monitoring dashboard

2023-04-17 17:58:03 UTC

2023-04-17 17:59:28 UTC

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https://dev.azure.com/avidxchange/technology-references/_wiki/wikis/tech-refs/37861/AvidPay-SLO-Dashboard <br/>\n<font size=\"3px\" color=\"green\"><b>CS-Creation-001 -</b></font> 100% of all check payments queued for batch processing are successfully transformed and corresponding output files are staged for delivery to the check printer(s) by 10PM each business day <br/>\n<font size=\"3px\" color=\"green\"><b>CS-Creation-002 -</b></font> 100% of all check payments queued for batch processing are successfully transformed and corresponding output files are staged for delivery to the check printer(s) by 10PM each business day <br/>\n<font size=\"3px\" color=\"green\"><b>CS-Creation-003 -</b></font> 100% of all check payments queued for batch processing are successfully transformed and corresponding output files are staged for delivery to the check printer(s) by 9PM each business day <br/>\n\n<font size=\"3px\" color=\"green\"><b>FS-Aggregation-001 -</b></font> ​​100% of all payments in ACH Requested that are eligible for Aggregation and require ONST approval are moved to the correct payment status by 5PM each business day <br/>\n<font size=\"3px\" color=\"green\"><b>FS-Aggregation-002 -</b></font> ​​100% of all payments in ACH Requested that are eligible for Aggregation and do not require ONST approval are moved to the correct payment status by 7PM each business day <br/>\n<font size=\"3px\" color=\"green\"><b>FS-Bai2-001 -</b></font> ​​100% of all BAI2 files are consumed by the BAI2 Parser by 6:30AM Monday - Friday <br/>\n<font size=\"3px\" color=\"green\"><b>FS-Bai2-002 -</b></font> 100% of all BAI2 files consumed by the BAI2 parser are sent to the Enterprise Reconciliation Tool by 7:00AM Tool Monday - Friday <br/>\n<font size=\"3px\" color=\"green\"><b>FS-Bai2-003 -</b></font> 100% of all ACH and Check Clearing requests are processed by 10AM Monday - Friday <br/>\n<font size=\"3px\" color=\"green\"><b>FS-Remittance-001 -</b></font> 100% of all remittance emails are successfully delivered to customers by 9AM each business day <br/>\n\n<font size=\"3px\" color=\"green\"><b>VCS-Creation-001 -</b></font> 100% of all virtual cards queued on each business day are created by midnight each business day <br/>\n<font size=\"3px\" color=\"green\"><b>VCS-Delivery-002 -</b></font> 100% of all virtual cards created on each business day are e-mailed to suppliers by 2PM the following business day <br/>\n<font size=\"3px\" color=\"green\"><b>VCS-WEXTransaction-001 -</b></font> 100% of WEX transaction are inserted into AvidPay database by 3 AM EST <br/>", "mode": "markdown" }, "pluginVersion": "9.3.2", "title": "AvidPay Daily Monitoring SLO Dashboard - Overview", "type": "text" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 4 }, "id": 12, "panels": [], "title": "Check Services", "type": "row" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 5, "x": 0, "y": 5 }, "id": 20, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": true }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n | where $__timeFilter(timestamp)\r\n | where isnotempty(customDimensions)\r\n //filter for custom events for CottonWood \r\n | where name has \"Cottonwood:BatchGenerating\" or name has \"Cottonwood:BatchCompleted\"\r\n | extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.EventTime),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n | summarize \r\n //Get the count of BatchGenerating events\r\n CountGenerating=countif(name has \"Cottonwood:BatchGenerating\"),\r\n\t//Get the count of BatchGenerating comppleted\r\n CountCompleted=countif(name has \"Cottonwood:BatchCompleted\"),\r\n\t//Get the count of BatchCompleted past 10 pm EST\r\n CountPast10PM=countif(name has \"Cottonwood:BatchCompleted\" and datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 22)\r\n\t//Case condition will check \r\n\t//Check for Count of BatchGenerating or BatchCompleted is not 0\r\n\t//BatchGenerating count is same as BatchCompleted\r\n\t//Check for any batch completed after 10pm EST \r\n | project PercentCompletedBefore10PM= case(CountPast10PM !=0, 1-(CountPast10PM/todouble(CountCompleted)), CountGenerating != CountCompleted, 1-(CountCompleted/todouble(CountGenerating)), CountGenerating ==0,0.0,CountCompleted==0,0.0, 1.0) ", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/Ax-Ae1-Payment-Legacy-Rg/providers/Microsoft.Insights/components/Ax-Ae1-Pr-CheckLegacy-Insights" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "CS-Creation-001 - %Files Delivered", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 5, "x": 5, "y": 5 }, "id": 40, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/^PercentCompletedAfter10PM$/", "values": true }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n | where $__timeFilter(timestamp)\r\n //filter for custom events for SunGard\r\n | where name has \"CheckBatchProcessor:CheckFileCreated\" or name has \"CheckBatchProcessor:CheckFileSent\" and customDimensions.CheckPrinterId == 1\r\n | extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.EventTime),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n | summarize \r\n //Get the count of check file created for SunGard\r\n CountQueued=countif(name has \"CheckBatchProcessor:CheckFileCreated\" and d.CheckPrinterId == 1),\r\n //Get the count of check file sent for SunGard \r\n CountSent=countif(name has \"CheckBatchProcessor:CheckFileSent\" and d.CheckPrinterId == 1),\r\n //Get the count of check file sent for SunGard post 10 PM EST\r\n CountPast10PM=countif(name has \"CheckBatchProcessor:CheckFileSent\" and d.CheckPrinterId == 1 and datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 22)\r\n //Case condition will check \r\n\t//Check for Count of Check File Created or Check File Sent is not 0\r\n\t//Check file created count is same as Check File sent\r\n\t//Check for any check file sent after 10pm EST \r\n| project PercentCompletedAfter10PM=case(CountPast10PM !=0, 1-(CountPast10PM/todouble(CountSent)), CountQueued != CountSent,(CountSent/todouble(CountQueued)),CountSent ==0,0.0,1.0)", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Legacy-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Legacy-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "CS-Creation-002 - %Files Delivered", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 5, "x": 10, "y": 5 }, "id": 41, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/^PercentCompletedAfter10PM$/", "values": true }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n | where $__timeFilter(timestamp)\r\n //filter for custom events for StrongRoom\r\n | where name has \"CheckBatchProcessor:CheckFileCreated\" or name has \"CheckBatchProcessor:CheckFileSent\" and customDimensions.CheckPrinterId == 2\r\n | extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.EventTime),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n | summarize \r\n //Get the count of check file created for StrongRoom\r\n CountQueued=countif(name has \"CheckBatchProcessor:CheckFileCreated\" and d.CheckPrinterId == 2),\r\n //Get the count of check file sent for StrongRoom\r\n CountSent=countif(name has \"CheckBatchProcessor:CheckFileSent\" and d.CheckPrinterId == 2),\r\n //Get the count of check file sent for SunGard post 9 PM EST\r\n CountPast9PM=countif(name has \"CheckBatchProcessor:CheckFileSent\" and d.CheckPrinterId == 2 and datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 21)\r\n //Case condition will check \r\n\t//Check for Count of Check File Created or Check File Sent is not 0\r\n\t//Check file created count is same as Check File sent\r\n\t//Check for any check file sent after 9pm EST \r\n| project PercentCompletedAfter9PM=case(CountPast9PM !=0, 1-(CountPast9PM/todouble(CountSent)), CountQueued != CountSent, 1-(CountSent/todouble(CountQueued)), CountSent ==0, 0.0, 1.0)", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Legacy-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Legacy-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "CS-Creation-003 - %Files Delivered", "type": "stat" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 8 }, "id": 4, "panels": [], "title": "Funding & Settlement", "type": "row" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "percentage", "steps": [ { "color": "red", "value": null } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "PercentBatchApprovalCompleted" }, "properties": [ { "id": "unit", "value": "percentunit" } ] } ] }, "gridPos": { "h": 3, "w": 8, "x": 0, "y": 9 }, "id": 31, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n| extend d = parse_json(customDimensions)\r\n //filter for custom events for Aggregation Batch Creation and Created. And filter custom dimensions for AggregationType Approval\r\n| where name has \"Aggregation:Batch:Creation:Created\" and d.AggregationType == \"Approval\"\r\n //Converting the UTC datetime to EST datetime\r\n| extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.Date),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n| summarize \r\n //Get the count of Aggregation Creation and Created events. AggregationType Approval custom dimensions events\r\n TotalBatchCreated = countif(name has \"Aggregation:Batch:Creation:Created\" and d.AggregationType == \"Approval\"),\r\n //Get the count of Aggregation Creation and Created events. AggregationType Approval custom dimensions events and ONST approval are moved to the correct payment status by 5PM \r\n TotalBatchCreatedPast5PM = countif(name has \"Aggregation:Batch:Creation:Created\" and d.AggregationType == \"Approval\" and datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 17)\r\n\t//Case condition will check \r\n\t//Check for Count of Batch Created and AggregationType Approval is not 0\r\n //Check for any batch created after 05pm EST\r\n| project PercentBatchApprovalCompleted = case(TotalBatchCreatedPast5PM != 0, 1-(TotalBatchCreatedPast5PM/todouble(TotalBatchCreated)), TotalBatchCreated == 0,0.0,1.0) ", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Legacy-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Legacy-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "FS-Aggregation-001 - %ApprovalACHRequestedAggregation", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "percentage", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": "" }, { "color": "#EAB839", "value": "" }, { "color": "green", "value": "" } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "PercentBatchAutoApprovalCompleted" }, "properties": [ { "id": "unit", "value": "percentunit" } ] } ] }, "gridPos": { "h": 3, "w": 9, "x": 8, "y": 9 }, "id": 33, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n| extend d = parse_json(customDimensions)\r\n //filter for custom events for Aggregation Batch Creation and Created. And filter custom dimensions for AggregationType Non Approval\r\n| where name has \"Aggregation:Batch:Creation:Created\" and d.AggregationType == \"Non Approval\"\r\n //Converting the UTC datetime to EST datetime\r\n| extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.Date),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n| summarize\r\n //Get the count of Aggregation Creation and Created events. AggregationType Non Approval custom dimensions events\r\n TotalBatchCreated = countif(name has \"Aggregation:Batch:Creation:Created\" and d.AggregationType == \"Non Approval\"),\r\n //Get the count of Aggregation Creation and Created events. AggregationType Approval custom dimensions events and ONST approval are moved to the correct payment status by 5PM\r\n TotalBatchCreatedPast7PM = countif(name has \"Aggregation:Batch:Creation:Created\" and d.AggregationType == \"Non Approval\" and datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 19)\r\n //Case condition will check \r\n\t//Check for Count of Batch Created and AggregationType Non Approval is not 0\r\n //Check for any batch created past 07pm EST\r\n| project PercentBatchAutoApprovalCompleted = case(TotalBatchCreatedPast7PM != 0, 1-(TotalBatchCreatedPast7PM/todouble(TotalBatchCreated)), TotalBatchCreated == 0, 0.0,1.0) ", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Legacy-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Legacy-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "FS-Aggregation-002 - %AutoApprovalACHRequestedAggregation", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "percentage", "steps": [ { "color": "green", "value": null } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Percent" }, "properties": [ { "id": "unit", "value": "percentunit" } ] } ] }, "gridPos": { "h": 3, "w": 5, "x": 0, "y": 12 }, "id": 6, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "app(\"AX-Ae1-Pr-Payment-Hub-INSIGHTS\").customEvents\r\n| where $__timeFilter(timestamp)\r\n //filter for custom events for BeginParsingFile. And filter custom dimensions for FileName: BOA_AVDC(Bank of america), AVDC_BAIPD(Fifth Third) and OldNSpd(KeyBank)\r\n| where name == \"Bai2Parser:BeginParsingFile\" and ((toupper(customDimensions.FileName) contains \"BOA_AVDC\") \r\nor (toupper(customDimensions.FileName) contains \"AVDC_BAIPD\") or (customDimensions.FileName contains \"OldNSpd\"))\r\n| extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.EventTime),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n //Converting the UTC datetime to EST datetime for timestamp\r\n | extend TransmissionDateTime = format_datetime(datetime_utc_to_local(timestamp, \"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n //get the Expected Date for all BAI2 files are consumed by the BAI2 Parser by 6:30AM\r\n | extend ExpectedDate = format_datetime(todatetime(strcat(format_datetime(todatetime(TransmissionDateTime), \"MM/dd/yyyy\"), \" \",\"06:30:00 AM\")),\"MM/dd/yyyy hh:mm:ss tt\")\r\n //get the Expected Date for BOA_AVDC files are consumed by the BAI2 Parser by 11:30AM ,\r\n // Note: need add more logic on this BOA_AVDC files for time because these files receiving multiple times in day.\r\n | extend BOA_ExpectedDate = format_datetime(todatetime(strcat(format_datetime(todatetime(TransmissionDateTime), \"MM/dd/yyyy\"), \" \",\"23:30:00 PM\")),\"MM/dd/yyyy hh:mm:ss tt\")\r\n //Get Status should be good if file is uploaded less than 6:30 AM EST and Delayed if greater than 6:30 AM\r\n | extend Status = iff(customDimensions.FileName contains \"BOA_AVDC\", \r\n (iff(todatetime(CustomDimensionsEventDateTime) > todatetime(BOA_ExpectedDate), \"Delayed\", \"Good\")),\r\n (iff(todatetime(CustomDimensionsEventDateTime) > todatetime(ExpectedDate), \"Delayed\", \"Good\")))\r\n// Get Total Files Uploaded for all BAI2 files\r\n| summarize TotalFilesUploaded = count(),\r\n//Get Percent should be good if file is uploaded less than 6:30 AM EST\r\n Percent = countif(Status == \"Good\")/todouble(count()) \r\n| project Percent", "resource": "/subscriptions/d5d4da15-d34e-4cf3-a14b-e565ec53a1e5/resourceGroups/ax-ae1-saas-pr-observability-rg/providers/microsoft.operationalinsights/workspaces/ax-ae1-pr-observability-la" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "FS-BAI2-001 - %Bai2Consumed", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Percent" }, "properties": [ { "id": "unit", "value": "percentunit" } ] } ] }, "gridPos": { "h": 3, "w": 4, "x": 5, "y": 12 }, "id": 8, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "app(\"AX-Ae1-Pr-Payment-Hub-INSIGHTS\").customEvents\r\n| where $__timeFilter(timestamp)\r\n//filter for custom events for UploadToReconStorage. And filter custom dimensions for FileName: BOA_AVDC(Bank of america), AVDC_BAIPD(Fifth Third) and OldNSpd(KeyBank)\r\n| where name == \"Bai2Parser:UploadToReconStorage\" and (toupper(customDimensions.FileName) contains \"BOA_AVDC\")\r\n or (toupper(customDimensions.FileName) contains \"AVDC_BAIPD\") or (customDimensions.FileName contains \"OldNSpd\") \r\n| extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.EventTime),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n //Converting the UTC datetime to EST datetime for timestamp\r\n | extend TransmissionDateTime = format_datetime(datetime_utc_to_local(timestamp, \"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n //get the Expected Date for all BAI2 files are consumed by the BAI2 Parser by 6:30AM\r\n | extend ExpectedDate = format_datetime(todatetime(strcat(format_datetime(todatetime(TransmissionDateTime), \"MM/dd/yyyy\"), \" \",\"06:30:00 AM\")),\"MM/dd/yyyy hh:mm:ss tt\")\r\n //Get Status should be good if file is uploaded less than 6:30 AM EST and Delayed if greater than 6:30 AM\r\n | extend Status = iff(todatetime(CustomDimensionsEventDateTime) > todatetime(ExpectedDate), \"Delayed\", \"Good\")\r\n// Get Total Files Uploaded for all BAI2 files\r\n| summarize TotalFilesUploaded = count(),\r\n//Get Percent should be good if file is uploaded less than 6:30 AM EST\r\n Percent = countif(Status == \"Good\")/todouble(count()) \r\n| project Percent", "resource": "/subscriptions/d5d4da15-d34e-4cf3-a14b-e565ec53a1e5/resourceGroups/ax-ae1-saas-pr-observability-rg/providers/microsoft.operationalinsights/workspaces/ax-ae1-pr-observability-la" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "FS-BAI2-002 - %Bai2Sent", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 5, "x": 9, "y": 12 }, "id": 18, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": true }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n | where $__timeFilter(timestamp)\r\n | where isnotempty(customDimensions)\r\n //filter for custom events for AchClearing Success and Exception\r\n | where name has \"Bai2Listener:AchClearing:Success\" or name has \"Bai2Listener:AchClearing:Exception\"\r\n | extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.EventTime),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n | summarize \r\n //Get the count of Ach Clearing Exception\r\n TotalACHClearingFailureCount = countif(name has \"Bai2Listener:AchClearing:Exception\"),\r\n //Get the count of Ach Clearing Success \r\n TotalACHClearingSuccessCount = countif(name has \"Bai2Listener:AchClearing:Success\"),\r\n //Get the count of Ach Clearing Success and Check Clearing requests are processed by 10AM\r\n TotalCountACHClearingSuccessPast10AM = countif(name has \"Bai2Listener:AchClearing:Success\" and datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 10) \r\n //Case condition will check \r\n //Check Total Count ACH Clearing by Success for Past 10AM is not zero then get Percent ACH Completed Success fully\r\n //Check Total ACH Clearing Failure Count is not zero \r\n | project PercentACHCompletedSuccessfully = case(TotalCountACHClearingSuccessPast10AM !=0, 1-(TotalCountACHClearingSuccessPast10AM/todouble(TotalACHClearingSuccessCount))\r\n ,TotalACHClearingFailureCount !=0, 1-(TotalACHClearingFailureCount/todouble(TotalACHClearingSuccessCount)), TotalACHClearingSuccessCount ==0,0.0, \r\n 1.0)", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Hub-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Hub-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "hide": false, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "FS-BAI2-003 -%ACH Clearing", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 6, "x": 0, "y": 15 }, "id": 19, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": true }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n | where $__timeFilter(timestamp)\r\n | where isnotempty(customDimensions)\r\n //filter for custom events for StdCheckClearing Success and Exception\r\n | where name has \"Bai2Listener:StdCheckClearing:Success\" or name has \"Bai2Listener:StdCheckClearing:Exception\"\r\n | extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.EventTime),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n | summarize \r\n //Get Total Std Check Clearing Failure(Exception) Count\r\n TotalStdCheckClearingFailureCount = countif(name has \"Bai2Listener:StdCheckClearing:Exception\"),\r\n //Get Total Std Check Clearing Success Count \r\n TotalStdCheckClearingSuccessCount = countif(name has \"Bai2Listener:StdCheckClearing:Success\"),\r\n //Get Total Std Check Clearing Success and Check Clearing requests are processed by 10AM count\r\n TotalCountStdCheckClearingSuccessPast10AM = countif(name has \"Bai2Listener:StdCheckClearing:Success\" and datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 10) \r\n | project \r\n //Case condition will check \r\n\t//Check for Count of StdCheck Clearing Success Past 10AM not 0 \r\n //then get Percent Std Check Completed Successfully\r\n //check TotalStdCheckClearingFailureCount not 0 then Total Std Check Clearing Failure Count\r\n PercentStdCheckCompletedSuccessfully = case(TotalCountStdCheckClearingSuccessPast10AM !=0,1-(TotalCountStdCheckClearingSuccessPast10AM/todouble(TotalStdCheckClearingSuccessCount)),\r\n TotalStdCheckClearingFailureCount !=0,1-(TotalStdCheckClearingFailureCount/todouble(TotalStdCheckClearingSuccessCount)),TotalStdCheckClearingSuccessCount ==0, 0.0,\r\n 1.0)\r\n \r\n //iif(TotalStdCheckClearingFailureCount ==0 and TotalCountStdCheckClearingSuccessPast10AM ==0, 1.0,1-(TotalCountStdCheckClearingSuccessPast10AM/todouble(TotalStdCheckClearingSuccessCount)))", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Hub-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Hub-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "hide": false, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "FS-BAI2-003 - %Check Clearing", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "percentage", "steps": [ { "color": "red", "value": null } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Percentage" }, "properties": [ { "id": "unit", "value": "percentunit" } ] } ] }, "gridPos": { "h": 3, "w": 6, "x": 6, "y": 15 }, "id": 35, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n| extend d = parse_json(customDimensions)\r\n| where $__timeFilter(timestamp)\r\n //filter custom events for Remittance 'Apd Credit','IA Apd Credit' and 'Apd Debit'\r\n| where name == \"Remittance:Apd Credit\" or name == \"Remittance:IA Apd Credit\" or name == \"Remittance:Apd Debit\" \r\n//filter custom events for 'Payment Identified', 'ayment Remittance Sent' and 'Missed'\r\nand ( d.Message contains \"Payment Identified\" or d.Message contains \"Payment Remittance Sent\" or d.Message contains \"Missed\")\r\n|summarize \r\n//Get email sent Total for Payment Remittance Sent\r\nTotalEmailSent = countif(d.Message contains \"Payment Remittance Sent\"),\r\n//Get Total for remittance emails are sent successfully delivered to customers by 9AM\r\n CountPast9AMEmailSent = countif( d.Message contains \"Payment Remittance Sent\" and datetime_part(\"hour\", timestamp) > 9)\r\n| project \r\n//Case condition will check for Count of Past9AMEmailSent 09AM not 0 \r\n//then get Percent remittance emails are sent successfully delivered to customers by 9AM\r\nPercentage = case(CountPast9AMEmailSent != 0, 1-(CountPast9AMEmailSent/todouble(TotalEmailSent)),TotalEmailSent == 0, 0.0,1.0)", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Legacy-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Legacy-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "FS-Remittance-001 - %RemittanceEmails", "type": "stat" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 18 }, "id": 25, "panels": [], "title": "Virtual Card", "type": "row" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 0, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 5, "x": 0, "y": 19 }, "id": 28, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": true }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents \r\n | where isnotempty(customDimensions) \r\n //filter for custom events for VCC cards\r\n | where name has \"HubCardCreation:CardQueued\" or name has \"HubVcRouting:cardCreated\" \r\n | extend d = parse_json(customDimensions) \r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.EventTime),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\") \r\n | summarize \r\n\t\t\t//Get total count of VCC cards created\r\n\t\t\tTotalCardCreatedCount = sumif(toint(itemCount),name has \"HubVcRouting:cardCreated\"), \r\n //Get total count of VCC cards queued\r\n\t\t\tTotalCardQueuedCount = sumif(toint(customDimensions.ProcessedTotal),name has \"HubCardCreation:CardQueued\"),\r\n\t\t\t//Get total count of cards that got sent past 12 AM EST\r\n\t\t\tTotalCountPast12AMCardCreated = sumif(toint(itemCount),name has \"HubVcRouting:cardCreated\" and datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 24)\r\n //Case condition checks for \r\n //Checks for VCC cards got sent out post 12 AM EST\r\n //Checks for Card queued count is 0, which means no cards are stuck\r\n //Checks for count of card sent is not 0\r\n //Checks for VCC Queued < Card Created count \r\n | project PercentCompletedAfter12AM= case(TotalCardCreatedCount ==0, 0.0, TotalCardQueuedCount==0,1.0,TotalCardQueuedCount < TotalCardCreatedCount,1.0, TotalCountPast12AMCardCreated !=0, 1-(TotalCountPast12AMCardCreated/todouble(TotalCardCreatedCount)), 1.0)", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Hub-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Hub-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "VCS-Creation-001 - %Card Created", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 0, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 7, "x": 5, "y": 19 }, "id": 36, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": true }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n | where $__timeFilter(timestamp) \r\n //filter for custom events for WEX transactions\r\n | where isnotempty(customDimensions) and name has \"WexETLService:FileProcessed\"\r\n | extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(timestamp),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n | summarize \r\n\t//Get the count of expected amount\r\n\tTotalExpectedTransaction = sum(toint(customDimensions[\"ExpectedTransactionCount\"])),\r\n\t//Get the count of actual amount processed\r\n\tTotalActualTransaction = sum(toint(customDimensions[\"ActualTransactionCount\"])),\r\n\t//Get the count of actual amount processed after 3 am EST\r\n TotalTransactionPast3AM = sumif(toint(customDimensions[\"ActualTransactionCount\"]), datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 3 )\r\n //Case condition checks for \r\n //Checks for transaction completed after 3 AM\r\n //Check of acutal and expected amounts are matching\r\n //Check for count of actual and expected amount is not 0 \r\n | project PercentWEXTransaction = case(TotalTransactionPast3AM != 0, 1-(TotalTransactionPast3AM/todouble(TotalActualTransaction)),TotalActualTransaction != TotalExpectedTransaction,1-(TotalActualTransaction/todouble(TotalExpectedTransaction)), TotalActualTransaction ==0,0.0,TotalExpectedTransaction ==0,0.0, 1.0)\r\n ", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Hub-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Hub-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "VCS-WEXCreation-001 - %WEXTransactions", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 0, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 12, "x": 0, "y": 22 }, "id": 37, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": true }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n //| where $__timeFilter(timestamp) \r\n //filter for custom events for Remittance emails\r\n | where name has \"HubCardDistribution:ProcessingSendMessage\" or name has \"HubCardDistribution:ProcessingSendingMessage\"\r\n | extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(timestamp),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n | summarize \r\n\t//Get count of email queued for sending\r\n\tTotalEmailsQueued = sumif(toint(itemCount),name has \"HubCardDistribution:ProcessingSendingMessage\"),\r\n //Get count of email sent\r\n\tTotalEmailSent = sumif(toint(itemCount),name has \"HubCardDistribution:ProcessingSendMessage\"),\r\n\t//Get count of email sent out after 2 pm EST\r\n TotalEmailSentPast2PM = sumif(toint(name has \"HubCardDistribution:ProcessingSendMessage\"), datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 14 )\r\n\t//case conditions checks for\r\n\t//Checks for email sent out after 2 pm EST\r\n\t//Checks for count of email sent and email queued are same\r\n\t//Checks for count of email queued > sent is same calculate the difference\r\n | project PercentEmailSent = case(TotalEmailSentPast2PM != 0,1-(TotalEmailSentPast2PM/todouble(TotalEmailSent)),TotalEmailsQueued > TotalEmailSent,1-(TotalEmailSent/todouble(TotalEmailsQueued)),TotalEmailsQueued==0,0.0,TotalEmailSent==0,0.0, 1.0)", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Hub-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Hub-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "VCS-Delivery-002 - %DeliveryRemittanceEmail", "type": "stat" } ], "refresh": false, "schemaVersion": 37, "style": "dark", "tags": [], "templating": { "list": [] }, "time": { "from": "now-30d", "to": "now" }, "timepicker": {}, "timezone": "America/New_York", "title": "Avid Pay SLO Monitoring", "uid": "iOxGpJx4k", "version": 311, "weekStart": "", "gnetId": 18548, "description": "Testing import AvidPay SLO Monitoring dashboard" }

2023-04-17 17:58:03 UTC

2023-04-17 17:59:28 UTC

medidores

MEDIDORES

test 1

2022-10-20 13:25:50 UTC

2022-10-20 13:27:30 UTC

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"color": { "mode": "thresholds" }, "decimals": 1, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] } }, "overrides": [] }, "gridPos": { "h": 5, "w": 9, "x": 0, "y": 4 }, "id": 15, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "value" }, "pluginVersion": "9.1.3", "targets": [ { "datasource": { "type": "mysql", "uid": "${DS_MYSQL}" }, "format": "table", "group": [], "metricColumn": "none", "rawQuery": true, "rawSql": "SELECT eaf FROM ${medidores:raw}a", "refId": "A", "select": [ [ { "params": [ "id" ], "type": "column" } ] ], "table": "c1ea", "timeColumn": "FH_T", "timeColumnType": "timestamp", "where": [ { "name": "$__timeFilter", "params": [], "type": "macro" } ] } ], "title": "ENERGIA ACTIVA [ kWh ]", "type": "stat" }, { "datasource": { "type": "mysql", "uid": "${DS_MYSQL}" }, 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'TURNO',\n MAX(ENERGIA_ACTIVA)-MIN(ENERGIA_ACTIVA) AS 'ENERGIA ACTIVA'\nFROM ${medidores:raw}e\nWHERE TURNO <> 'undefined' AND ENERGIA_ACTIVA > 0\nGROUP BY TURNO\nORDER BY id DESC\nLIMIT 24", "refId": "A", "select": [ [ { "params": [ "id" ], "type": "column" } ] ], "table": "c1fase1h", "timeColumn": "FH_T", "timeColumnType": "timestamp", "where": [ { "name": "$__timeFilter", "params": [], "type": "macro" } ] } ], "timeFrom": "24h", "title": "ENERGIA ACTIVA x TURNO [kWh]", "transformations": [], "type": "barchart" }, { "datasource": { "type": "mysql", "uid": "${DS_MYSQL}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisGridShow": true, "axisLabel": "", "axisPlacement": "left", "fillOpacity": 80, "gradientMode": "hue", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineWidth": 1, "scaleDistribution": { "type": "linear" } }, "decimals": 2, "mappings": [], "noValue": "0", 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"rawQuery": true, "rawSql": "SELECT\n YYYY AS 'AÑO',\n MAX(ENERGIA_ACTIVA)-MIN(ENERGIA_ACTIVA) AS 'ENERGIA ACTIVA'\nFROM ${medidores:raw}e\nWHERE ENERGIA_ACTIVA > 0\nGROUP BY YYYY\nORDER BY YYYY DESC\nLIMIT 24", "refId": "A", "select": [ [ { "params": [ "id" ], "type": "column" } ] ], "table": "c1fase1h", "timeColumn": "FH_T", "timeColumnType": "timestamp", "where": [ { "name": "$__timeFilter", "params": [], "type": "macro" } ] } ], "timeFrom": "24h", "title": "ENERGIA ACTIVA x AÑO [kWh]", "transformations": [], "type": "barchart" } ], "refresh": "5s", "schemaVersion": 37, "style": "dark", "tags": [ "General" ], "templating": { "list": [ { "current": {}, "datasource": { "type": "mysql", "uid": "${DS_MYSQL}" }, "definition": "SELECT DISTINCT area FROM pmstations1", "hide": 0, "includeAll": false, "label": "AREA", "multi": false, "name": "area", "options": [], "query": "SELECT DISTINCT area FROM pmstations1", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": { "type": "mysql", "uid": "${DS_MYSQL}" }, "definition": "SELECT nombre FROM pmstations1 WHERE area='$area'", "hide": 0, "includeAll": false, "label": "MAQUINA", "multi": false, "name": "pmstations", "options": [], "query": "SELECT nombre FROM pmstations1 WHERE area='$area'", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": { "type": "mysql", "uid": "${DS_MYSQL}" }, "definition": "SELECT station FROM pmstations1 WHERE nombre='$pmstations'", "hide": 2, "includeAll": false, "label": "", "multi": false, "name": "medidores", "options": [], "query": "SELECT station FROM pmstations1 WHERE nombre='$pmstations'", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" } ] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "MEDIDORES", "uid": "dHQcnxe7k", "version": 33, "weekStart": "", "gnetId": 17234, "description": "test 1" }

2022-10-20 13:25:50 UTC

2022-10-20 13:27:30 UTC

stable-diffusion-monitor

Stable Diffusion Monitor

A dashboard for monitoring a Windows Desktop/Gaming PC. Using Prometheus, windows_exporter, and OhmGraphite.

2022-11-09 03:47:05 UTC

2022-11-09 03:56:23 UTC

This dashboard uses the following exporters: windows_exporter: https://github.com/prometheus-community/windows_exporter OhmGraphite: https://github.com/nickbabcock/OhmGraphite Note: instances values are set manually due to same host, different ports.

Other

A dashboard for monitoring a Windows Desktop/Gaming PC. Using Prometheus, wmi_exporter, and OhmGraphite.

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}, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 10, "w": 7, "x": 7, "y": 40 }, "id": 37, "links": [], "options": { "legend": { "calcs": [ "mean", "min", "max", "last" ], "displayMode": "table", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "pluginVersion": "9.2.3", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "rate(windows_logical_disk_split_ios_total{volume=\"C:\"}[1m])", "format": "time_series", "hide": false, "intervalFactor": 1, "legendFormat": "{{volume}}", "range": true, "refId": "B" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "rate(windows_logical_disk_split_ios_total{volume=\"D:\"}[1m])", "format": "time_series", "hide": false, "intervalFactor": 1, "legendFormat": 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"${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "max": 90, "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 10, "w": 9, "x": 0, "y": 51 }, "id": 43, "links": [], "options": { "legend": { "calcs": [ "mean", "min", "max", "last" ], "displayMode": "table", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "pluginVersion": "9.2.3", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "ohm_cpu_celsius{sensor=\"Core (Tctl/Tdie)\"}", "format": "time_series", "hide": false, "intervalFactor": 1, "legendFormat": "Die Temp", "range": true, "refId": "B" } ], "title": "CPU Temperatures", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineStyle": { "fill": "solid" }, "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "max": 110, "min": 50, "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "celsius" }, "overrides": [] }, "gridPos": { "h": 10, "w": 6, "x": 9, "y": 51 }, "id": 45, "links": [], "options": { "legend": { "calcs": [ "mean", "min", "max", "last" ], "displayMode": "table", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.2.3", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "ohm_gpunvidia_celsius{sensor=\"GPU Core\"}", "format": "time_series", "intervalFactor": 1, "legendFormat": "GPU Core", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "ohm_gpunvidia_celsius{sensor=\"GPU Hot Spot\"}", "format": "time_series", "hide": false, "intervalFactor": 1, "legendFormat": "GPU Hotspot", "range": true, "refId": "B" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "ohm_gpunvidia_celsius{sensor=\"GPU Memory Junction\"}", "format": "time_series", "hide": false, "intervalFactor": 1, "legendFormat": "GPU Memory", "range": true, "refId": "C" } ], "title": "GPU Temperature", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "max": 70, "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 10, "w": 6, "x": 15, "y": 51 }, "id": 54, "links": [], "options": { "legend": { "calcs": [ "mean", "min", "max", "last" ], "displayMode": "table", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "pluginVersion": "9.2.3", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "exemplar": false, "expr": "ohm_hdd_celsius{hardware=\"Samsung SSD 970 EVO 1TB\",sensor=\"Temperature\"}", "format": "time_series", "instant": false, "intervalFactor": 1, "legendFormat": "Samsung NVMe (C:)", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "ohm_hdd_celsius{hardware=\"SPCC M.2 PCIe SSD\"}", "format": "time_series", "intervalFactor": 1, "legendFormat": "Silicon Power NVMe (H:)", "range": true, "refId": "B" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "ohm_hdd_celsius{hardware=\"Samsung SSD 860 EVO 1TB\"}", "format": "time_series", "hide": false, "intervalFactor": 1, "legendFormat": "Samsung SSD (E:)", "range": true, "refId": "C" } ], "title": "SSD Temperature", "type": "timeseries" }, { "collapsed": false, "datasource": { "type": "prometheus", "uid": "7_8f-5v4z" }, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 61 }, "id": 56, "panels": [], "targets": [ { "datasource": { "type": "prometheus", "uid": "7_8f-5v4z" }, "refId": "A" } ], "title": "Extended CPU Info", "type": "row" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "max": 120, "min": 0, "thresholds": { "mode": "percentage", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "watt" }, "overrides": [] }, "gridPos": { "h": 10, "w": 10, "x": 0, "y": 62 }, "id": 60, "links": [], "options": { "legend": { "calcs": [ "mean", "min", "max", "last" ], "displayMode": "table", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "pluginVersion": "9.2.3", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "ohm_cpu_watts{sensor=\"Package\"}", "format": "time_series", "intervalFactor": 1, "legendFormat": "CPU Package", "range": true, "refId": "A" } ], "title": "Power", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "rothz" }, "overrides": [] }, "gridPos": { "h": 10, "w": 11, "x": 10, "y": 62 }, "id": 72, "options": { "legend": { "calcs": [ "min", "max", "last" ], "displayMode": "table", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "windows_cpu_core_frequency_mhz{core=\"0,1\"}*1000000", "legendFormat": "CPU Clock", "range": true, "refId": "A" } ], "title": "CPU Clock", "type": "timeseries" }, { "collapsed": false, "datasource": { "type": "prometheus", "uid": "7_8f-5v4z" }, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 72 }, "id": 62, "panels": [], "targets": [ { "datasource": { "type": "prometheus", "uid": "7_8f-5v4z" }, "refId": "A" } ], "title": "Processes / Services", "type": "row" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 9, "w": 7, "x": 0, "y": 73 }, "id": 64, "links": [], "options": { "legend": { "calcs": [ "mean", "min", "max", "last" ], "displayMode": "table", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "pluginVersion": "9.2.3", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "windows_os_processes", "format": "time_series", "intervalFactor": 1, "legendFormat": "OS Processes", "range": true, "refId": "A" } ], "title": "# of Processes", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 9, "w": 7, "x": 7, "y": 73 }, "id": 66, "links": [], "options": { "legend": { "calcs": [ "last" ], "displayMode": "table", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "pluginVersion": "9.2.3", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(windows_service_state{state=\"running\"})", "format": "time_series", "intervalFactor": 1, "legendFormat": "State: Running", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(windows_service_state{state=\"stopped\"})", "format": "time_series", "hide": false, "intervalFactor": 1, "legendFormat": "State: Stopped", "range": true, "refId": "B" } ], "title": "Services", "type": "timeseries" } ], "refresh": "5s", "schemaVersion": 37, "style": "dark", "tags": [], "templating": { "list": [ { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(wmi_system_system_up_time, instance)", "hide": 0, "includeAll": false, "label": "Host", "multi": false, "name": "instance", "options": [], "query": { "query": "label_values(wmi_system_system_up_time, instance)", "refId": "Prometheus-instance-Variable-Query" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "browser", "title": "Stable Diffusion Monitor", "uid": "woRrCAriz", "version": 5, "weekStart": "" }

2022-11-09 03:47:05 UTC

2022-11-09 03:56:22 UTC

opnsense-ids-ips

OPNsense & IDS/IPS

Dashboards to monitor Firewall Rules & IDS/IPS potential Attacks.

2022-12-06 14:26:22 UTC

2022-12-06 14:49:07 UTC

## Monitoring OPNsense with Loki Prerequisites to monitor OPNsense Firewall- & IDS/IPS Logs with Loki: - **Loadbalancer** to create virtual IP and to publish syslog ports (for example [MetalLB](https://metallb.universe.tf/)) - **Promtail or Grafana Agent** to listen on these ports and ship the logs to Loki - **Loki** to index the logs - **Grafana** to visualize the logs (datasource Loki configured) - **OPNsense** syslog target configured ### Important! The Grafana Dashboard only works when your OPNsense IDS/IPS logs has a label `job: syslog-ids` and all other Firewall logs has a label `job: syslog`. Promtail Helm chart example values: ```yaml config: lokiAddress: "http://loki-gateway/loki/api/v1/push" snippets: extraScrapeConfigs: | # Add an additional scrape config for syslog - job_name: syslog syslog: listen_address: 0.0.0.0:514 idle_timeout: 60s label_structured_data: yes labels: job: "syslog" relabel_configs: - source_labels: ['__syslog_message_hostname'] target_label: 'host' # Add an additional scrape config for OPNsense IDS alerts shipped over syslog - job_name: syslog-ids syslog: listen_address: 0.0.0.0:1514 idle_timeout: 60s label_structured_data: yes labels: job: "syslog-ids" relabel_configs: - source_labels: ['__syslog_message_hostname'] target_label: 'host' extraPorts: # Add an additional port for syslog # serviceType "LoadBalancer" only works if you have deployed an Loadbalancer for example MetalLB syslog: name: tcp-syslog containerPort: 514 protocol: TCP service: type: LoadBalancer #clusterIP: null port: 514 #externalIPs: [] #nodePort: 32682 #annotations: {} #labels: {} #loadBalancerIP: null #loadBalancerSourceRanges: [] #externalTrafficPolicy: null # Add an additional port for OPNsense IDS alerts shipped over syslog # serviceType "LoadBalancer" only works if you have deployed an Loadbalancer for example MetalLB syslog-ids: name: tcp-syslog-ids containerPort: 1514 protocol: TCP service: type: LoadBalancer #clusterIP: null port: 1514 #externalIPs: [] #nodePort: 32682 #annotations: {} #labels: {} #loadBalancerIP: null #loadBalancerSourceRanges: [] #externalTrafficPolicy: null ```

Other

{ "__inputs": [ { "name": "DS_LOKI", "label": "loki", "description": "OPNsense IDS/IPS logs must have label `job: syslog-ids` and all other Firewall logs must habe label `job: syslog`.", "type": "datasource", "pluginId": "loki", "pluginName": "Loki" } ], "__elements": {}, "__requires": [ { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "9.2.4" }, { "type": "panel", "id": "logs", "name": "Logs", "version": "" }, { "type": "datasource", "id": "loki", "name": "Loki", "version": "1.0.0" }, { "type": "panel", "id": "piechart", "name": "Pie chart", "version": "" }, { "type": "panel", "id": "stat", "name": "Stat", "version": "" }, { "type": "panel", "id": "table", "name": "Table", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": { "type": "grafana", "uid": "-- Grafana --" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 0, "id": null, "links": [], "liveNow": false, "panels": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "gridPos": { "h": 1, "w": 12, "x": 0, "y": 0 }, "id": 23, "options": { "dedupStrategy": "none", "enableLogDetails": true, "prettifyLogMessage": false, "showCommonLabels": false, "showLabels": false, "showTime": false, "sortOrder": "Descending", "wrapLogMessage": false }, "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "{job=\"syslog\", host=\"$host\"}\n|~ \"block|pass\" # filter to get only firewall logs\n!= \"icmp\" # filter only not ICMP packages\n| pattern `<_>,<_>,<_>,<RuleID>,<Interface>,<Reason>,<Action>,<Direction>,<IPVersion>,<_>,<_>,<TTL>,<ID>,<Offset>,<IPFlags>,<ProtocolNr>,<Protocol>,<Length>,<SourceIP>,<DestinationIP>,<SourcePort>,<DestinationPort>,<_>,<TCPFlags>,<Seq>,<_>,<_>`", "hide": false, "queryType": "range", "refId": "A" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "{job=\"syslog\", host=\"$host\"}\n|~ \"block|pass\" # filter to get only firewall logs\n|= \"icmp\" # filter only ICMP packages\n| pattern `<_>,<_>,<_>,<RuleID>,<Interface>,<Reason>,<Action>,<Direction>,<IPVersion>,<_>,<_>,<TTL>,<ID>,<Offset>,<IPFlags>,<ProtocolNr>,<Protocol>,<Length>,<SourceIP>,<DestinationIP>,<_>`", "hide": false, "queryType": "range", "refId": "B" } ], "title": "Firewall Base Search (do not delete)", "transformations": [], "type": "logs" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "gridPos": { "h": 1, "w": 12, "x": 12, "y": 0 }, "id": 20, "options": { "dedupStrategy": "none", "enableLogDetails": true, "prettifyLogMessage": false, "showCommonLabels": false, "showLabels": false, "showTime": false, "sortOrder": "Descending", "wrapLogMessage": false }, "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "{job=\"syslog-ids\",host=\"$host\"} != \"Drop\" |~ \"$searchregex\"\n|= \"Priority\" # filter to exclude not IDS/IPS events\n| pattern `[<_>:<ruleId>:<_>] <rule> [Classification: <classification>] [Priority: <priority>] {<protocol>} <sourceIP>:<sourcePort> -> <destinationIP>:<destinationPort>`\n| protocol!~\"IPv6.*\" ", "hide": false, "queryType": "range", "refId": "IPv4-NoDrop-Query" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "{job=\"syslog-ids\",host=\"$host\"} |= \"Drop\" |~ \"$searchregex\"\n|= \"Priority\" # filter to exclude not IDS/IPS events\n| pattern `[<action>] [<_>:<ruleId>:<_>] <rule> [Classification: <classification>] [Priority: <priority>] {<protocol>} <sourceIP>:<sourcePort> -> <destinationIP>:<destinationPort>`\n| protocol!~\"IPv6.*\" ", "queryType": "range", "refId": "IPv4-Drop-Query" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "{job=\"syslog-ids\",host=\"$host\"} != \"Drop\" |~ \"$searchregex\"\n|= \"Priority\" # filter to exclude not IDS/IPS events\n| pattern `[<action>] [<_>:<ruleId>:<_>] <rule> [Classification: <classification>] [Priority: <priority>] {<protocol>} <_>`\n| protocol=~\"IPv6.*\" # filter only IPv6 logs because pattern() cannot easy handle IPv6 that's why the following regex is used\n| regexp `(?P<sourceIP>(([0-9a-fA-F]{1,4}:){7,7}[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,7}:|([0-9a-fA-F]{1,4}:){1,6}:[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,5}(:[0-9a-fA-F]{1,4}){1,2}|([0-9a-fA-F]{1,4}:){1,4}(:[0-9a-fA-F]{1,4}){1,3}|([0-9a-fA-F]{1,4}:){1,3}(:[0-9a-fA-F]{1,4}){1,4}|([0-9a-fA-F]{1,4}:){1,2}(:[0-9a-fA-F]{1,4}){1,5}|[0-9a-fA-F]{1,4}:((:[0-9a-fA-F]{1,4}){1,6})|:((:[0-9a-fA-F]{1,4}){1,7}|:)|fe80:(:[0-9a-fA-F]{0,4}){0,4}%[0-9a-zA-Z]{1,}|::(ffff(:0{1,4}){0,1}:){0,1}((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])|([0-9a-fA-F]{1,4}:){1,4}:((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9]))):(?P<sourcePort>\\d+) -> (?P<destinationIP>(([0-9a-fA-F]{1,4}:){7,7}[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,7}:|([0-9a-fA-F]{1,4}:){1,6}:[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,5}(:[0-9a-fA-F]{1,4}){1,2}|([0-9a-fA-F]{1,4}:){1,4}(:[0-9a-fA-F]{1,4}){1,3}|([0-9a-fA-F]{1,4}:){1,3}(:[0-9a-fA-F]{1,4}){1,4}|([0-9a-fA-F]{1,4}:){1,2}(:[0-9a-fA-F]{1,4}){1,5}|[0-9a-fA-F]{1,4}:((:[0-9a-fA-F]{1,4}){1,6})|:((:[0-9a-fA-F]{1,4}){1,7}|:)|fe80:(:[0-9a-fA-F]{0,4}){0,4}%[0-9a-zA-Z]{1,}|::(ffff(:0{1,4}){0,1}:){0,1}((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])|([0-9a-fA-F]{1,4}:){1,4}:((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9]))):(?P<destinationPort>\\d+)`\n| label_format sourceIP=`{{ regexReplaceAllLiteral \"\\\\b(?:0+:){2,}\" .sourceIP \":\"}}`\n| label_format sourceIP=`{{ regexReplaceAllLiteral \"(:0+){1}\" .sourceIP \":\"}}`\n| label_format destinationIP=`{{ regexReplaceAllLiteral \"\\\\b(?:0+:){2,}\" .destinationIP \":\"}}`\n| label_format destinationIP=`{{ regexReplaceAllLiteral \"(:0+){1}\" .destinationIP \":\"}}`", "hide": false, "queryType": "range", "refId": "IPv6-NoDrop-Query" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "{job=\"syslog-ids\",host=\"$host\"} |= \"Drop\" |~ \"$searchregex\"\n|= \"Priority\" # filter to exclude not IDS/IPS events\n| pattern `[<action>] [<_>:<ruleId>:<_>] <rule> [Classification: <classification>] [Priority: <priority>] {<protocol>} <_>`\n| protocol=~\"IPv6.*\" # filter only IPv6 logs because pattern() cannot easy handle IPv6 that's why the following regex is used\n| regexp `(?P<sourceIP>(([0-9a-fA-F]{1,4}:){7,7}[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,7}:|([0-9a-fA-F]{1,4}:){1,6}:[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,5}(:[0-9a-fA-F]{1,4}){1,2}|([0-9a-fA-F]{1,4}:){1,4}(:[0-9a-fA-F]{1,4}){1,3}|([0-9a-fA-F]{1,4}:){1,3}(:[0-9a-fA-F]{1,4}){1,4}|([0-9a-fA-F]{1,4}:){1,2}(:[0-9a-fA-F]{1,4}){1,5}|[0-9a-fA-F]{1,4}:((:[0-9a-fA-F]{1,4}){1,6})|:((:[0-9a-fA-F]{1,4}){1,7}|:)|fe80:(:[0-9a-fA-F]{0,4}){0,4}%[0-9a-zA-Z]{1,}|::(ffff(:0{1,4}){0,1}:){0,1}((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])|([0-9a-fA-F]{1,4}:){1,4}:((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9]))):(?P<sourcePort>\\d+) -> (?P<destinationIP>(([0-9a-fA-F]{1,4}:){7,7}[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,7}:|([0-9a-fA-F]{1,4}:){1,6}:[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,5}(:[0-9a-fA-F]{1,4}){1,2}|([0-9a-fA-F]{1,4}:){1,4}(:[0-9a-fA-F]{1,4}){1,3}|([0-9a-fA-F]{1,4}:){1,3}(:[0-9a-fA-F]{1,4}){1,4}|([0-9a-fA-F]{1,4}:){1,2}(:[0-9a-fA-F]{1,4}){1,5}|[0-9a-fA-F]{1,4}:((:[0-9a-fA-F]{1,4}){1,6})|:((:[0-9a-fA-F]{1,4}){1,7}|:)|fe80:(:[0-9a-fA-F]{0,4}){0,4}%[0-9a-zA-Z]{1,}|::(ffff(:0{1,4}){0,1}:){0,1}((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])|([0-9a-fA-F]{1,4}:){1,4}:((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9]))):(?P<destinationPort>\\d+)`\n| label_format sourceIP=`{{ regexReplaceAllLiteral \"\\\\b(?:0+:){2,}\" .sourceIP \":\"}}`\n| label_format sourceIP=`{{ regexReplaceAllLiteral \"(:0+){1}\" .sourceIP \":\"}}`\n| label_format destinationIP=`{{ regexReplaceAllLiteral \"\\\\b(?:0+:){2,}\" .destinationIP \":\"}}`\n| label_format destinationIP=`{{ regexReplaceAllLiteral \"(:0+){1}\" .destinationIP \":\"}}`", "hide": false, "queryType": "range", "refId": "IPv6-Drop-Query" } ], "title": "IDS Base Search (do not delete)", "transformations": [], "type": "logs" }, { "gridPos": { "h": 1, "w": 24, "x": 0, "y": 1 }, "id": 27, "title": "Overview", "type": "row" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 4, "w": 12, "x": 0, "y": 2 }, "id": 13, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.2.4", "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "count_over_time({job=\"syslog\",host=\"$host\"}[$__range])", "queryType": "range", "refId": "A" } ], "title": "Firewall Logs", "type": "stat" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 4, "w": 12, "x": 12, "y": 2 }, "id": 19, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.2.4", "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "count_over_time({job=\"syslog-ids\", host=\"$host\"} |~ \"$searchregex\" [$__range])", "queryType": "range", "refId": "A" } ], "title": "IDS Events", "type": "stat" }, { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "hideFrom": { "legend": false, "tooltip": false, "viz": false } }, "mappings": [] }, "overrides": [ { "matcher": { "id": "byName", "options": "block" }, "properties": [ { "id": "color", "value": { "fixedColor": "red", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 8, "w": 4, "x": 0, "y": 6 }, "id": 21, "options": { "displayLabels": [ "name" ], "legend": { "displayMode": "table", "placement": "right", "showLegend": true, "values": [ "value", "percent" ] }, "pieType": "pie", "reduceOptions": { "calcs": [ "sum" ], "fields": "", "values": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.1.7", "targets": [ { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "panelId": 23, "refId": "A" } ], "title": "Firewall Connections", "transformations": [ { "id": "merge", "options": {} }, { "id": "extractFields", "options": { "format": "kvp", "replace": false, "source": "labels" } }, { "id": "groupBy", "options": { "fields": { "Action": { "aggregations": [], "operation": "groupby" }, "Line": { "aggregations": [ "count" ], "operation": "aggregate" }, "destinationIP": { "aggregations": [], "operation": "groupby" }, "sourceIP": { "aggregations": [] }, "sourcePort": { "aggregations": [] } } } } ], "type": "piechart" }, { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "hideFrom": { "legend": false, "tooltip": false, "viz": false } }, "mappings": [] }, "overrides": [ { "matcher": { "id": "byName", "options": "block" }, "properties": [ { "id": "color", "value": { "fixedColor": "red", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 8, "w": 4, "x": 4, "y": 6 }, "id": 30, "options": { "displayLabels": [ "name" ], "legend": { "displayMode": "table", "placement": "right", "showLegend": false, "values": [ "value", "percent" ] }, "pieType": "pie", "reduceOptions": { "calcs": [ "sum" ], "fields": "", "values": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.1.7", "targets": [ { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "panelId": 23, "refId": "A" } ], "title": "Firewall Connections", "transformations": [ { "id": "merge", "options": {} }, { "id": "extractFields", "options": { "format": "kvp", "replace": false, "source": "labels" } }, { "disabled": true, "id": "filterByValue", "options": { "filters": [ { "config": { "id": "equal", "options": { "value": "Drop" } }, "fieldName": "action" } ], "match": "any", "type": "include" } }, { "id": "groupBy", "options": { "fields": { "Action": { "aggregations": [] }, "Direction": { "aggregations": [], "operation": "groupby" }, "Line": { "aggregations": [ "count" ], "operation": "aggregate" }, "destinationIP": { "aggregations": [], "operation": "groupby" }, "sourceIP": { "aggregations": [] }, "sourcePort": { "aggregations": [] } } } } ], "type": "piechart" }, { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "description": "Top 10 dropped inbound Firewall connections.", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "hideFrom": { "legend": false, "tooltip": false, "viz": false } }, "mappings": [] }, "overrides": [ { "matcher": { "id": "byName", "options": "block" }, "properties": [ { "id": "color", "value": { "fixedColor": "red", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 8, "w": 4, "x": 8, "y": 6 }, "id": 31, "options": { "displayLabels": [ "name" ], "legend": { "displayMode": "table", "placement": "right", "showLegend": false, "values": [ "value", "percent" ] }, "pieType": "pie", "reduceOptions": { "calcs": [ "sum" ], "fields": "", "limit": 10, "values": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.1.7", "targets": [ { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "panelId": 23, "refId": "A" } ], "title": "Firewall Connections by DestinationPort", "transformations": [ { "id": "merge", "options": {} }, { "id": "extractFields", "options": { "format": "kvp", "replace": false, "source": "labels" } }, { "id": "filterByValue", "options": { "filters": [ { "config": { "id": "equal", "options": { "value": "in" } }, "fieldName": "Direction" }, { "config": { "id": "equal", "options": { "value": "block" } }, "fieldName": "Action" }, { "config": { "id": "isNotNull", "options": {} }, "fieldName": "DestinationPort" } ], "match": "all", "type": "include" } }, { "id": "groupBy", "options": { "fields": { "Action": { "aggregations": [] }, "DestinationPort": { "aggregations": [], "operation": "groupby" }, "Direction": { "aggregations": [] }, "Line": { "aggregations": [ "count" ], "operation": "aggregate" }, "SourceIP": { "aggregations": [] }, "destinationIP": { "aggregations": [], "operation": "groupby" }, "sourceIP": { "aggregations": [] }, "sourcePort": { "aggregations": [] } } } }, { "id": "sortBy", "options": { "fields": {}, "sort": [ { "desc": true, "field": "Line (count)" } ] } } ], "type": "piechart" }, { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "hideFrom": { "legend": false, "tooltip": false, "viz": false } }, "mappings": [ { "options": { "match": "empty", "result": { "index": 0, "text": "Allow" } }, "type": "special" } ] }, "overrides": [ { "matcher": { "id": "byName", "options": "Drop" }, "properties": [ { "id": "color", "value": { "fixedColor": "red", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 8, "w": 4, "x": 12, "y": 6 }, "id": 24, "options": { "displayLabels": [ "name" ], "legend": { "displayMode": "table", "placement": "right", "showLegend": false }, "pieType": "pie", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.1.7", "targets": [ { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "panelId": 20, "refId": "A" } ], "title": "Potential Attacks", "transformations": [ { "id": "merge", "options": {} }, { "id": "extractFields", "options": { "format": "kvp", "replace": false, "source": "labels" } }, { "disabled": true, "id": "filterByValue", "options": { "filters": [ { "config": { "id": "isNull", "options": {} }, "fieldName": "action" }, { "config": { "id": "equal", "options": { "value": "drop" } }, "fieldName": "action" } ], "match": "any", "type": "include" } }, { "id": "groupBy", "options": { "fields": { "Count": { "aggregations": [] }, "Line": { "aggregations": [ "count" ], "operation": "aggregate" }, "action": { "aggregations": [], "operation": "groupby" }, "destinationIP": { "aggregations": [] }, "sourceIP": { "aggregations": [] }, "sourcePort": { "aggregations": [] } } } } ], "type": "piechart" }, { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "description": "Top 5 dropped potential attacks.", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "hideFrom": { "legend": false, "tooltip": false, "viz": false } }, "mappings": [] }, "overrides": [] }, "gridPos": { "h": 8, "w": 4, "x": 16, "y": 6 }, "id": 8, "options": { "displayLabels": [ "name" ], "legend": { "displayMode": "list", "placement": "bottom", "showLegend": false }, "pieType": "pie", "reduceOptions": { "calcs": [ "sum" ], "fields": "", "limit": 5, "values": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.1.7", "targets": [ { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "panelId": 20, "refId": "A" } ], "title": "Potential Attacks from", "transformations": [ { "id": "merge", "options": {} }, { "id": "extractFields", "options": { "format": "kvp", "replace": false, "source": "labels" } }, { "id": "filterByValue", "options": { "filters": [ { "config": { "id": "equal", "options": { "value": "Drop" } }, "fieldName": "action" } ], "match": "any", "type": "include" } }, { "id": "groupBy", "options": { "fields": { "Line": { "aggregations": [ "count" ], "operation": "aggregate" }, "sourceIP": { "aggregations": [], "operation": "groupby" }, "sourcePort": { "aggregations": [] } } } }, { "id": "sortBy", "options": { "fields": {}, "sort": [ { "desc": true, "field": "Line (count)" } ] } } ], "type": "piechart" }, { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "description": "Top 10 dropped potential attacks by port.", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "hideFrom": { "legend": false, "tooltip": false, "viz": false } }, "mappings": [] }, "overrides": [] }, "gridPos": { "h": 8, "w": 4, "x": 20, "y": 6 }, "id": 22, "options": { "displayLabels": [ "name" ], "legend": { "displayMode": "list", "placement": "bottom", "showLegend": false }, "pieType": "pie", "reduceOptions": { "calcs": [ "sum" ], "fields": "", "limit": 10, "values": true }, 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"selected": true, "text": "", "value": "" } ], "query": "", "skipUrlSync": false, "type": "textbox" } ] }, "time": { "from": "now-24h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "OPNsense & IDS/IPS", "uid": "5aBjqZKVk", "version": 1, "weekStart": "", "gnetId": 17547, "description": "Dashboards to monitor Firewall Rules & IDS/IPS potential Attacks." }

2022-12-06 14:26:22 UTC

2022-12-06 14:49:07 UTC

kubernetes-compute-resources-namespace-workloads

EKS Workloads by Namespace

Kubernetes / Compute Resources / Namespace (Workloads)

2022-10-28 09:41:45 UTC

2022-10-28 09:44:25 UTC

This dashboard is part of the [AWS Observability Accelerator](https://aws.amazon.com/blogs/mt/announcing-aws-observability-accelerator-to-configure-comprehensive-observability-for-amazon-eks/) and enables you to monitor EKS workloads by namespace based on Prometheus metrics. For an automated end-to-end setup based on Terraform as well as to provide feedback or report an issue with the dashboard, please visit the [AWS Observability Accelerator GitHub repo](https://github.com/aws-observability/terraform-aws-observability-accelerator/issues).

Other

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node_namespace_pod_container:container_cpu_usage_seconds_total:sum_irate{cluster=\"$cluster\", namespace=\"$namespace\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n/sum(\n kube_pod_container_resource_requests{job=\"kube-state-metrics\", cluster=\"$cluster\", namespace=\"$namespace\", resource=\"cpu\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "D", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n kube_pod_container_resource_limits{job=\"kube-state-metrics\", cluster=\"$cluster\", namespace=\"$namespace\", resource=\"cpu\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "E", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n node_namespace_pod_container:container_cpu_usage_seconds_total:sum_irate{cluster=\"$cluster\", namespace=\"$namespace\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n/sum(\n kube_pod_container_resource_limits{job=\"kube-state-metrics\", cluster=\"$cluster\", namespace=\"$namespace\", resource=\"cpu\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "F", "step": 10 } ], "thresholds": [], "title": "CPU Quota", "tooltip": { "shared": false, "sort": 2, "value_type": "individual" }, "transform": "table", "type": "table-old", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "show": false } ] }, { "collapsed": false, "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "gridPos": { "h": 1, "w": 24, "x": 0, "y": 16 }, "id": 16, "panels": [], "targets": [ { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "refId": "A" } ], "title": "Memory Usage", "type": "row" }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "fill": 10, "fillGradient": 0, "gridPos": { "h": 7, "w": 24, "x": 0, "y": 17 }, "hiddenSeries": false, "id": 3, "interval": "1m", "legend": { "alignAsTable": true, "avg": false, "current": false, "max": false, "min": false, "rightSide": true, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 0, "links": [], "nullPointMode": "null as zero", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "8.4.7", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "quota - requests", "color": "#F2495C", "dashes": true, "fill": 0, "hiddenSeries": true, "hideTooltip": true, "legend": true, "linewidth": 2, "stack": false }, { "alias": "quota - limits", "color": "#FF9830", "dashes": true, "fill": 0, "hiddenSeries": true, "hideTooltip": true, "legend": true, "linewidth": 2, "stack": false } ], "spaceLength": 10, "stack": true, "steppedLine": false, "targets": [ { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n container_memory_working_set_bytes{job=\"kubelet\", cluster=\"$cluster\", namespace=\"$namespace\", container!=\"\", image!=\"\"}\n * on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "time_series", "intervalFactor": 2, "legendFormat": "{{workload}} - {{workload_type}}", "refId": "A", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "scalar(kube_resourcequota{cluster=\"$cluster\", namespace=\"$namespace\", type=\"hard\",resource=\"requests.memory\"})", "format": "time_series", "intervalFactor": 2, "legendFormat": "quota - requests", "refId": "B", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "scalar(kube_resourcequota{cluster=\"$cluster\", namespace=\"$namespace\", type=\"hard\",resource=\"limits.memory\"})", "format": "time_series", "intervalFactor": 2, "legendFormat": "quota - limits", "refId": "C", "step": 10 } ], "thresholds": [], "timeRegions": [], "title": "Memory Usage", "tooltip": { "shared": false, "sort": 2, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "bytes", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "show": false } ], "yaxis": { "align": false } }, { "collapsed": false, "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "gridPos": { "h": 1, "w": 24, "x": 0, "y": 24 }, "id": 17, "panels": [], "targets": [ { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "refId": "A" } ], "title": "Memory Quota", "type": "row" }, { "aliasColors": {}, "bars": false, "columns": [], "dashLength": 10, "dashes": false, "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "fill": 1, "fontSize": "100%", "gridPos": { "h": 7, "w": 24, "x": 0, "y": 25 }, "id": 4, "interval": "1m", "legend": { "alignAsTable": true, "avg": false, "current": false, "max": false, "min": false, "rightSide": true, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null as zero", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "showHeader": true, "sort": { "col": 0, "desc": true }, "spaceLength": 10, "stack": false, "steppedLine": false, "styles": [ { "alias": "Time", "align": "auto", "dateFormat": "YYYY-MM-DD HH:mm:ss", "pattern": "Time", "type": "hidden" }, { "alias": "Running Pods", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 0, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #A", "thresholds": [], "type": "number", "unit": "short" }, { "alias": "Memory Usage", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #B", "thresholds": [], "type": "number", "unit": "bytes" }, { "alias": "Memory Requests", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #C", "thresholds": [], "type": "number", "unit": "bytes" }, { "alias": "Memory Requests %", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #D", "thresholds": [], "type": "number", "unit": "percentunit" }, { "alias": "Memory Limits", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #E", "thresholds": [], "type": "number", "unit": "bytes" }, { "alias": "Memory Limits %", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #F", "thresholds": [], "type": "number", "unit": "percentunit" }, { "alias": "Workload", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": true, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "/d/a164a7f0339f99e89cea5cb47e9be617/k8s-resources-workload?var-cluster=$cluster&var-namespace=$namespace&var-workload=$__cell&var-type=$__cell_2", "pattern": "workload", "thresholds": [], "type": "number", "unit": "short" }, { "alias": "Workload Type", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "workload_type", "thresholds": [], "type": "number", "unit": "short" }, { "alias": "", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "pattern": "/.*/", "thresholds": [], "type": "string", "unit": "short" } ], "targets": [ { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "count(namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}) by (workload, workload_type)", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "A", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n container_memory_working_set_bytes{job=\"kubelet\", cluster=\"$cluster\", namespace=\"$namespace\", container!=\"\", image!=\"\"}\n * on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "B", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n kube_pod_container_resource_requests{job=\"kube-state-metrics\", cluster=\"$cluster\", namespace=\"$namespace\", resource=\"memory\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "C", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n container_memory_working_set_bytes{job=\"kubelet\", cluster=\"$cluster\", namespace=\"$namespace\", container!=\"\", image!=\"\"}\n * on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n/sum(\n kube_pod_container_resource_requests{job=\"kube-state-metrics\", cluster=\"$cluster\", namespace=\"$namespace\", resource=\"memory\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "D", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n kube_pod_container_resource_limits{job=\"kube-state-metrics\", cluster=\"$cluster\", namespace=\"$namespace\", resource=\"memory\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "E", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n container_memory_working_set_bytes{job=\"kubelet\", cluster=\"$cluster\", namespace=\"$namespace\", container!=\"\", image!=\"\"}\n * on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n/sum(\n kube_pod_container_resource_limits{job=\"kube-state-metrics\", cluster=\"$cluster\", namespace=\"$namespace\", resource=\"memory\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "F", "step": 10 } ], "thresholds": [], "title": "Memory Quota", "tooltip": { "shared": false, "sort": 2, "value_type": "individual" }, "transform": "table", "type": "table-old", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "show": false } ] }, { "collapsed": false, "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "gridPos": { "h": 1, "w": 24, "x": 0, "y": 32 }, "id": 18, "panels": [], "targets": [ { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "refId": "A" } ], "title": "Current Network Usage", "type": "row" }, { "aliasColors": {}, "bars": false, "columns": [], "dashLength": 10, "dashes": false, "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "fill": 1, "fontSize": "100%", "gridPos": { "h": 7, "w": 24, "x": 0, "y": 33 }, "id": 5, "interval": "1m", "legend": { "alignAsTable": true, "avg": false, "current": false, "max": false, "min": false, "rightSide": true, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null as zero", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "showHeader": true, "sort": { "col": 0, "desc": true }, "spaceLength": 10, "stack": false, "steppedLine": false, "styles": [ { "alias": "Time", "align": "auto", "dateFormat": "YYYY-MM-DD HH:mm:ss", "pattern": "Time", "type": "hidden" }, { "alias": "Current Receive Bandwidth", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #A", "thresholds": [], "type": "number", "unit": "Bps" }, { "alias": "Current Transmit Bandwidth", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #B", "thresholds": [], "type": "number", "unit": "Bps" }, { "alias": "Rate of Received Packets", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #C", "thresholds": [], "type": "number", "unit": "pps" }, { "alias": "Rate of Transmitted Packets", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #D", "thresholds": [], "type": "number", "unit": "pps" }, { "alias": "Rate of Received Packets Dropped", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #E", "thresholds": [], "type": "number", "unit": "pps" }, { "alias": "Rate of Transmitted Packets Dropped", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #F", "thresholds": [], "type": "number", "unit": "pps" }, { "alias": "Workload", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": true, "linkTargetBlank": false, "linkTooltip": "Drill down to pods", "linkUrl": "/d/a164a7f0339f99e89cea5cb47e9be617/k8s-resources-workload?var-cluster=$cluster&var-namespace=$namespace&var-workload=$__cell&var-type=$type", "pattern": "workload", "thresholds": [], "type": "number", "unit": "short" }, { "alias": "Workload Type", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "workload_type", "thresholds": [], "type": "number", "unit": "short" }, { "alias": "", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "pattern": "/.*/", "thresholds": [], "type": "string", "unit": "short" } ], "targets": [ { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "(sum(irate(container_network_receive_bytes_total{job=\"kubelet\", cluster=\"$cluster\", namespace=\"$namespace\"}[$__rate_interval])\n* on (namespace,pod)\ngroup_left(workload,workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}) by (workload))\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "A", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "(sum(irate(container_network_transmit_bytes_total{job=\"kubelet\", cluster=\"$cluster\", namespace=\"$namespace\"}[$__rate_interval])\n* on (namespace,pod)\ngroup_left(workload,workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}) by (workload))\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "B", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "(sum(irate(container_network_receive_packets_total{job=\"kubelet\", cluster=\"$cluster\", namespace=\"$namespace\"}[$__rate_interval])\n* on (namespace,pod)\ngroup_left(workload,workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}) by (workload))\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "C", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "(sum(irate(container_network_transmit_packets_total{job=\"kubelet\", cluster=\"$cluster\", namespace=\"$namespace\"}[$__rate_interval])\n* on (namespace,pod)\ngroup_left(workload,workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}) by (workload))\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "D", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "(sum(irate(container_network_receive_packets_dropped_total{job=\"kubelet\", cluster=\"$cluster\", namespace=\"$namespace\"}[$__rate_interval])\n* on (namespace,pod)\ngroup_left(workload,workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}) by (workload))\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "E", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "(sum(irate(container_network_transmit_packets_dropped_total{job=\"kubelet\", cluster=\"$cluster\", namespace=\"$namespace\"}[$__rate_interval])\n* on (namespace,pod)\ngroup_left(workload,workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}) by (workload))\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "F", "step": 10 } ], "thresholds": [], "title": "Current Network Usage", "tooltip": { "shared": false, "sort": 2, "value_type": "individual" }, "transform": "table", "type": "table-old", "xaxis": { "mode": "time", 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2022-10-28 09:41:45 UTC

2022-10-28 09:44:25 UTC

nextcloud

Nextcloud

Dashboard for Nextcloud exporter

2023-03-01 13:24:39 UTC

2023-03-01 13:25:41 UTC

Needs node-exporter, cadvisor, and nextcloud-exporter.

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2023-03-01 13:24:39 UTC

2023-03-01 13:25:41 UTC

hello-observability

Hello Observability

Hello Observability

2023-03-17 09:35:42 UTC

2023-03-17 09:38:17 UTC

{ "__inputs": [ { "name": "DS_LOKI-ADAM", "label": "Loki-Adam", "description": "", "type": "datasource", "pluginId": "loki", "pluginName": "Loki" }, { "name": "DS_PROMETHEUS-ADAM", "label": "Prometheus-Adam", "description": "", "type": "datasource", "pluginId": "prometheus", "pluginName": "Prometheus" } ], "__elements": [], "__requires": [ { "type": "panel", "id": "gauge", "name": "Gauge", "version": "" }, { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "8.4.3" }, { "type": "panel", "id": "logs", "name": "Logs", "version": "" }, { "type": "datasource", "id": "loki", "name": "Loki", "version": "1.0.0" }, { "type": "datasource", "id": "prometheus", "name": "Prometheus", "version": "1.0.0" }, { "type": "panel", "id": "stat", "name": "Stat", "version": "" }, { "type": "panel", "id": "state-timeline", "name": "State timeline", "version": "" }, { "type": "panel", "id": "text", "name": "Text", "version": "" }, { "type": "panel", "id": "timeseries", "name": "Time series", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": "-- Grafana --", "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 0, "id": null, "links": [], "liveNow": false, "panels": [ { "description": "", "gridPos": { "h": 10, "w": 8, "x": 0, "y": 0 }, "id": 26, "options": { "content": "Dashboard for a simple [Spring Boot](https://spring.io/guides/gs/spring-boot) application, [Hello Observability](https://github.com/adamquan/hello-observability), to demonstrate observability by collecting and correlating logs, metrics and traces with:\n- [Prometheus](https://prometheus.io/)\n- [OpenTelemetry](https://opentelemetry.io/)\n- [Grafana Agent](https://grafana.com/docs/grafana-cloud/agent/)\n- [Grafana Cloud](https://grafana.com/products/cloud/)\n \n<img alt=\"architecture\" src=\"https://github.com/adamquan/hello-observability/raw/main/images/architecture.png\" width=\"1000\">", "mode": "markdown" }, "pluginVersion": "8.4.3", "title": "Hello Observability", "type": "text" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI-ADAM}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "purple", "value": null } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 5, "w": 7, "x": 8, "y": 0 }, "hideTimeOverride": false, "id": 19, "maxDataPoints": 1000, "options": { "colorMode": "background", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "sum" ], "fields": "", "values": false }, "text": {}, "textMode": "value_and_name" }, "pluginVersion": "8.4.3", "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI-ADAM}" }, "expr": "sum by(path) (count_over_time({job=\"tomcat-access\"} | pattern `<_> - 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- <_> \"<method> <path> <_>\" <status> <size>` != \"/metrics\" [$__interval]))", "legendFormat": "", "queryType": "range", "refId": "A", "resolution": 1 } ], "timeFrom": "5m", "title": "Visitors", "type": "stat" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI-ADAM}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "percentage", "steps": [ { "color": "light-blue", "value": null }, { "color": "light-blue", "value": 20 } ] } }, "overrides": [] }, "gridPos": { "h": 10, "w": 4, "x": 20, "y": 0 }, "id": 22, "options": { "colorMode": "background", "graphMode": "area", "justifyMode": "auto", "orientation": "horizontal", "reduceOptions": { "calcs": [ "sum" ], "fields": "", "values": false }, "textMode": "value_and_name" }, "pluginVersion": "8.4.3", "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI-ADAM}" }, "expr": "sum by (status) (count_over_time({job=\"tomcat-access\"} | pattern `<_> - - <_> \"<method> <path> <_>\" <status> <size>` != \"/metrics\" [$__interval]))", "legendFormat": "{{status}}", "refId": "A" } ], "title": "Requests by status code", "type": "stat" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI-ADAM}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "orange", "value": null } ] }, "unit": "decbytes" }, "overrides": [] }, "gridPos": { "h": 5, "w": 7, "x": 8, "y": 5 }, "hideTimeOverride": false, "id": 24, "options": { "colorMode": "background", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "sum" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "8.4.3", "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI-ADAM}" }, "expr": "sum by (path) (sum_over_time({job=\"tomcat-access\"} | pattern `<_> - - <_> \"<method> <path> <_>\" <status> <size>` |= \"/hello\" | unwrap size [1m]))", "legendFormat": "{{path}}", "refId": "A" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI-ADAM}" }, "expr": "sum by (path) (sum_over_time({job=\"tomcat-access\"} | pattern `<_> - - <_> \"<method> <path> <_>\" <status> <size>` |= \"/observability\" | unwrap size [1m]))", "hide": false, "legendFormat": "{{path}}", "refId": "B" } ], "timeFrom": "5m", "title": "Total Bytes Sent", "type": "stat" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI-ADAM}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 100, "gradientMode": "opacity", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "smooth", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": 600000, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "500" }, "properties": [ { "id": "color", "value": { "fixedColor": "red", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 5, "w": 5, "x": 15, "y": 5 }, "id": 28, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI-ADAM}" }, "expr": "sum by (status) (count_over_time( {job=\"tomcat-access\"} | pattern `<_> - - <_> \"<method> <path> <_>\" <status> <size>` != \"/metrics\" [1m]))", "legendFormat": "{{status}}", "refId": "A" } ], "title": "HTTP status codes over time, per minute", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS-ADAM}" }, "description": "", "fieldConfig": { "defaults": { "color": { "fixedColor": "green", "mode": "thresholds" }, "custom": { "fillOpacity": 90, "lineWidth": 0, "spanNulls": false }, "mappings": [ { "options": { "0": { "color": "red", "index": 0, "text": "Down" }, "1": { "color": "green", "index": 1, "text": "Up" } }, "type": "value" } ], "max": 1, "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "red", "value": null }, { "color": "green", "value": 1 } ] }, "unit": "bool_yes_no" }, "overrides": [] }, "gridPos": { "h": 5, "w": 6, "x": 0, "y": 10 }, "id": 10, "options": { "alignValue": "left", "legend": { "displayMode": "hidden", "placement": "bottom" }, "mergeValues": true, "rowHeight": 0.5, "showValue": "never", "tooltip": { "mode": "none", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS-ADAM}" }, "exemplar": true, "expr": "up{job=\"hello-observability\"}", "format": "time_series", "instant": false, "interval": "", "legendFormat": "{{job}}", "refId": "A" } ], "title": "Service Up/Down", "type": "state-timeline" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS-ADAM}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "max": 10, "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 8 } ] } }, "overrides": [] }, "gridPos": { "h": 5, "w": 5, "x": 6, "y": 10 }, "id": 12, "options": { "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "showThresholdLabels": true, "showThresholdMarkers": true }, "pluginVersion": "8.4.3", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS-ADAM}" }, "exemplar": true, "expr": "sum by (path) (rate (requests_total[1m])) * 60", "interval": "", "legendFormat": "{{path}}", "refId": "A" } ], "title": "Requests per Minute", "type": "gauge" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS-ADAM}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "max": 2, "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 0.5 } ] }, "unit": "ms" }, "overrides": [] }, "gridPos": { "h": 5, "w": 5, "x": 11, "y": 10 }, "id": 13, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": {}, "textMode": "auto" }, "pluginVersion": "8.4.3", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS-ADAM}" }, "exemplar": true, "expr": "sum by (path) (rate(request_duration_histogram_bucket{}[1m]))", "interval": "", "legendFormat": "{{path}}", "refId": "A" } ], "title": "Latency", "type": "stat" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI-ADAM}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "Bytes", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 41, "gradientMode": "opacity", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "smooth", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 5, "w": 8, "x": 16, "y": 10 }, "id": 17, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI-ADAM}" }, "expr": "quantile_over_time (0.90, {job=\"tomcat-access\"} | pattern `<_> - - <_> \"<method> <path> <_>\" <status> <size>` != \"/metrics\" | unwrap size [1m] ) by (path)", "legendFormat": "{{path}}", "refId": "A" } ], "title": "90% Percentile Response Size", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS-ADAM}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 100, "gradientMode": "opacity", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "smooth", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 7, "w": 8, "x": 0, "y": 15 }, "id": 8, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS-ADAM}" }, "exemplar": false, "expr": "sum by (path) (rate(requests_total{job=\"hello-observability\"} [1m]) * 60)", "interval": "", "legendFormat": "{{path}}", "refId": "A" } ], "title": "Requests per Minute", "type": "timeseries" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI-ADAM}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "%", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 71, "gradientMode": "opacity", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "smooth", "lineStyle": { "fill": "solid" }, "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "decimals": 0, "mappings": [], "max": 50, "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "/hello" }, "properties": [ { "id": "color", "value": { "fixedColor": "red", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "/observability" }, "properties": [ { "id": "color", "value": { "fixedColor": "semi-dark-purple", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 7, "w": 8, "x": 8, "y": 15 }, "id": 15, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI-ADAM}" }, "expr": "sum by (path) (rate( {job=\"tomcat-access\"} | pattern `<_> - - <_> \"<method> <path> <_>\" <status> <size>` != \"/metrics\" | status !=200 [1m])) / sum by (path) (rate( {job=\"tomcat-access\"} | pattern `<_> - - <_> \"<method> <path> <_>\" <status> <size>` != \"/metrics\" [1m])) * 100", "legendFormat": "{{path}}", "refId": "A" } ], "title": "Error Rate", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS-ADAM}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 80, "gradientMode": "opacity", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "smooth", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "area" } }, "decimals": 1, "mappings": [], "max": 2, "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 1.5 } ] }, "unit": "s" }, "overrides": [ { "matcher": { "id": "byName", "options": "{path=\"/god-of-fire\"}" }, "properties": [ { "id": "color", "value": { "fixedColor": "light-green", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "{path=\"/hello\"}" }, "properties": [ { "id": "color", "value": { "fixedColor": "light-red", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 7, "w": 8, "x": 16, "y": 15 }, "id": 2, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS-ADAM}" }, "exemplar": true, "expr": "sum by (path) (rate(request_duration_histogram_bucket{}[1m]))", "interval": "", "legendFormat": "{{path}}", "refId": "A" } ], "title": "Latency", "type": "timeseries" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI-ADAM}" }, "gridPos": { "h": 5, "w": 24, "x": 0, "y": 22 }, "id": 4, "options": { "dedupStrategy": "none", "enableLogDetails": true, "prettifyLogMessage": false, "showCommonLabels": false, "showLabels": false, "showTime": false, "sortOrder": "Descending", "wrapLogMessage": false }, "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI-ADAM}" }, "expr": "{job=\"hello-observability\"}", "refId": "A" } ], "title": "Logs", "type": "logs" } ], "schemaVersion": 35, "style": "dark", "tags": [], "templating": { "list": [] }, "time": { "from": "now-15m", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Hello Observability", "uid": "UjJzQ1L7k", "version": 10, "weekStart": "", "gnetId": 18293, "description": "Hello Observability" }

2023-03-17 09:35:42 UTC

2023-03-17 09:38:17 UTC

influxdb-2-x-telegraf-docker-dashboard

InfluxDB 2.x Telegraf Docker Dashboard

Basic docker dashboard for InfluxDB 2.x and telegraf collector.

2023-03-30 04:17:51 UTC

2023-03-30 04:22:06 UTC

# Overview. This dashboard is designed to visualize the sensor data collected using Telegraf `inputs.docker`.

Telegraf

Basic docker dashboard for InfluxDB 2.x and telegraf collector.

{ "__inputs": [ { "name": "DS_DROPET-INFLUXDB", "label": "", "description": "", "type": "datasource", "pluginId": "influxdb", "pluginName": "InfluxDB" } ], "__elements": {}, "__requires": [ { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "9.4.7" }, { "type": "datasource", "id": "influxdb", "name": "InfluxDB", "version": "1.0.0" }, { "type": "panel", "id": "stat", "name": "Stat", "version": "" }, { "type": "panel", "id": "timeseries", "name": "Time series", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": { "type": "grafana", "uid": "-- Grafana --" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "description": "Basic docker dashboard for InfluxDB 2.x and telegraf collector.", "editable": true, "fiscalYearStartMonth": 0, "gnetId": 18389, "graphTooltip": 1, "id": null, "links": [], "liveNow": false, "panels": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 5, "w": 3, "x": 0, "y": 0 }, "id": 8, "options": { "colorMode": "none", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.4.7", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker\")\n |> filter(fn: (r) => r[\"_field\"] == \"n_cpus\")\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")", "refId": "A" } ], "title": "Total Cpus", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 5, "w": 3, "x": 3, "y": 0 }, "id": 11, "options": { "colorMode": "none", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.4.7", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker\")\n |> filter(fn: (r) => r[\"_field\"] == \"n_images\")\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")", "refId": "A" } ], "title": "Images", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "decbytes" }, "overrides": [] }, "gridPos": { "h": 5, "w": 3, "x": 6, "y": 0 }, "id": 7, "options": { "colorMode": "none", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.4.7", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker\")\n |> filter(fn: (r) => r[\"_field\"] == \"memory_total\")\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")", "refId": "A" } ], "title": "Total Memory", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 5, "w": 3, "x": 9, "y": 0 }, "id": 4, "options": { "colorMode": "value", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.4.7", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker\")\n |> filter(fn: (r) => r[\"_field\"] == \"n_containers_running\")\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")", "refId": "A" } ], "title": "Running Containers", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "orange", "value": null } ] } }, "overrides": [] }, "gridPos": { "h": 5, "w": 3, "x": 12, "y": 0 }, "id": 5, "options": { "colorMode": "value", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.4.7", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker\")\n |> filter(fn: (r) => r[\"_field\"] == \"n_containers_paused\")\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")", "refId": "A" } ], "title": "Paused Containers", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "red", "value": null } ] } }, "overrides": [] }, "gridPos": { "h": 5, "w": 3, "x": 15, "y": 0 }, "id": 6, "options": { "colorMode": "value", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": {}, "textMode": "auto" }, "pluginVersion": "9.4.7", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker\")\n |> filter(fn: (r) => r[\"_field\"] == \"n_containers_stopped\")\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")", "refId": "A" } ], "title": "Paused Containers", "type": "stat" }, { "collapsed": true, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 5 }, "id": 14, "panels": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "displayName": "${__field.labels.container_name}", "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "percent" }, "overrides": [] }, "gridPos": { "h": 10, "w": 12, "x": 0, "y": 6 }, "id": 2, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker_container_cpu\")\n |> filter(fn: (r) => r[\"_field\"] == \"usage_percent\")\n |> filter(fn: (r) => r[\"container_name\"] =~ /${container:regex}/)\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")", "refId": "A" } ], "title": "Cpu Usage", "transparent": true, "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "displayName": "${__field.labels.container_name}", "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "bytes" }, "overrides": [] }, "gridPos": { "h": 10, "w": 12, "x": 12, "y": 6 }, "id": 10, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker_container_mem\")\n |> filter(fn: (r) => r[\"_field\"] == \"usage\")\n |> filter(fn: (r) => r[\"container_name\"] =~ /${container:regex}/)\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")", "refId": "A" } ], "title": "Memory Usage", "transparent": true, "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "displayName": "${__field.labels.container_name}:${__field.name}", "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "bytes" }, "overrides": [] }, "gridPos": { "h": 10, "w": 12, "x": 0, "y": 16 }, "id": 9, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker_container_blkio\")\n |> filter(fn: (r) => r[\"_field\"] == \"io_service_bytes_recursive_read\" or r[\"_field\"] == \"io_service_bytes_recursive_write\")\n |> filter(fn: (r) => r[\"container_name\"] =~ /${container:regex}/)\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")\n", "refId": "A" } ], "title": "Disk Usage", "transparent": true, "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "displayName": "${__field.labels.container_name}:${__field.name}", "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "bytes" }, "overrides": [] }, "gridPos": { "h": 10, "w": 12, "x": 12, "y": 16 }, "id": 12, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker_container_net\")\n |> filter(fn: (r) => r[\"_field\"] == \"rx_bytes\" or r[\"_field\"] == \"tx_bytes\")\n |> filter(fn: (r) => r[\"container_name\"] =~ /${container:regex}/)\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")\n", "refId": "A" } ], "title": "Network Usage", "transparent": true, "type": "timeseries" } ], "repeat": "container", "repeatDirection": "h", "title": "${container}", "type": "row" } ], "refresh": "", "revision": 1, "schemaVersion": 38, "style": "dark", "tags": [ "docker", "influxdb2.0", "telegraf" ], "templating": { "list": [ { "current": {}, "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "definition": "buckets()", "hide": 0, "includeAll": false, "multi": false, "name": "bucket", "options": [], "query": "buckets()", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "definition": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker_container_cpu\")\n |> keyValues(keyColumns: [\"container_name\"])\n |> group()\n |> keep(columns: [\"container_name\"])\n |> distinct(column: \"container_name\")", "hide": 0, "includeAll": true, "multi": true, "name": "container", "options": [], "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker_container_cpu\")\n |> keyValues(keyColumns: [\"container_name\"])\n |> group()\n |> keep(columns: [\"container_name\"])\n |> distinct(column: \"container_name\")", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" } ] }, "time": { "from": "now-24h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "InfluxDB 2.x Telegraf Docker Dashboard", "uid": "YIIzB274z", "version": 5, "weekStart": "" }

2023-03-30 04:17:51 UTC

2023-03-30 04:22:05 UTC

home-fortiswitches

Home FortiSwitches

I will display all the switches added to Zabbix... manually entry of each port if you want add more... so far this diaply the main interface and 10 ports

2023-04-12 20:59:43 UTC

2023-04-12 21:13:28 UTC

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"list": [ { "builtIn": 1, "datasource": { "type": "grafana", "uid": "-- Grafana --" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 0, "id": null, "links": [], "liveNow": false, "panels": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "gridPos": { "h": 5, "w": 5, "x": 0, "y": 0 }, "id": 47, "links": [ { "title": "", "url": "https://www.google.com/url?sa=i&url=https%3A%2F%2Ffortinetweb.s3.amazonaws.com%2Fdocs.fortinet.com%2Fv2%2Fattachments%2Fe83e2847-202f-11e9-b6f6-f8bc1258b856%2FFortiSwitch-108E-QSG.pdf&psig=AOvVaw2Dq_-XbuoiOLyXov-igT6Y&ust=1679616463721000&source=images&cd=vfe&ved=0CBAQjRxqFwoTCIC5zYfh8P0CFQAAAAAdAAAAABAL" } ], "options": { "code": { "language": "plaintext", "showLineNumbers": false, "showMiniMap": false }, "content": "<img 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"filter": "$Switch" }, "item": { "filter": "/.*/" }, "itemTag": { "filter": "Application: Fortinet General" }, "options": { "acknowledged": 2, "disableDataAlignment": false, "hostProxy": false, "hostsInMaintenance": false, "limit": 1001, "minSeverity": 0, "showDisabledItems": false, "skipEmptyValues": true, "sortProblems": "default", "useZabbixValueMapping": false }, "proxy": { "filter": "" }, "queryType": "2", "refId": "A", "resultFormat": "table", "showProblems": "problems", "slaInterval": "none", "slaProperty": { "name": "SLA", "property": "sla" }, "table": { "skipEmptyValues": false }, "tags": { "filter": "" }, "trigger": { "filter": "" }, "triggers": { "acknowledged": 2, "count": true, "minSeverity": 3 } } ], "type": "table" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 8 }, "id": 16, "panels": [], "title": "Health", "type": "row" }, { "datasource": { "type": "alexanderzobnin-zabbix-datasource", "uid": "${DS_ZABBIX}" }, "fieldConfig": { "defaults": { 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"item": { "filter": "Interface internal(primary): Inbound packets discarded" }, "itemTag": { "filter": "" }, "options": { "disableDataAlignment": false, "showDisabledItems": false, "skipEmptyValues": false, "useZabbixValueMapping": false }, "proxy": { "filter": "" }, "queryType": "0", "refId": "A", "resultFormat": "time_series", "table": { "skipEmptyValues": false }, "tags": { "filter": "" }, "trigger": { "filter": "" }, "triggers": { "acknowledged": 2, "count": true, "minSeverity": 3 } }, { "application": { "filter": "" }, "datasource": { "type": "alexanderzobnin-zabbix-datasource", "uid": "${DS_ZABBIX}" }, "functions": [], "group": { "filter": "Fortiswitch" }, "hide": false, "host": { "filter": "$Switch" }, "item": { "filter": "Interface internal(primary): Outbound packets discarded" }, "itemTag": { "filter": "" }, "options": { "disableDataAlignment": false, "showDisabledItems": false, "skipEmptyValues": false, "useZabbixValueMapping": false }, "proxy": { "filter": "" }, "queryType": "0", "refId": "B", "resultFormat": "time_series", "table": { "skipEmptyValues": false }, "tags": { "filter": "" }, "trigger": { "filter": "" }, "triggers": { "acknowledged": 2, "count": true, "minSeverity": 3 } } ], "title": "Primary - Packedt Discarded", "type": "timeseries" } ], "refresh": "5s", "revision": 1, "schemaVersion": 38, "style": "dark", "tags": [ "Fortiswitch", "Fortigate", "Zabbix Agent" ], "templating": { "list": [ { "current": {}, "datasource": { "type": "alexanderzobnin-zabbix-datasource", "uid": "${DS_ZABBIX}" }, "definition": "Zabbix - host", "hide": 0, "includeAll": false, "multi": false, "name": "Switch", "options": [], "query": { "application": "", "group": "Fortiswitch", "host": "/.*/", "item": "", "itemTag": "", "queryType": "host" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "type": "query" } ] }, "time": { "from": "now-12h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Home FortiSwitches", "uid": "fH3sKNf4z", "version": 26, "weekStart": "", "gnetId": 18507, "description": "I will display all the switches added to Zabbix... manually entry of each port if you want add more... so far this diaply the main interface and 10 ports" }

2023-04-12 20:59:43 UTC

2023-04-12 21:13:12 UTC

bitcoin-node

Bitcoin Node

Monitor your Bitcoin core node and aspects of the network.

2022-11-25 02:13:04 UTC

2022-11-25 02:13:26 UTC

Monitor your Bitcoin core node and aspects of the network.

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"timeRegions": [], "timeShift": null, "title": "Versions", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "decimals": null, "format": "none", "label": "protocol version", "logBase": 1, "max": null, "min": null, "show": true }, { "format": "none", "label": "server version", "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "Number of peers that have been banned and the reason for the ban.", "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 32 }, "id": 16, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", 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} ], "refresh": "5m", "schemaVersion": 20, "style": "dark", "tags": [ "bitcoin" ], "templating": { "list": [] }, "time": { "from": "now-1w", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "Bitcoin Node", "uid": "OTLC2jHWz", "version": 1, "description": "Monitor your Bitcoin core node and aspects of the network." }

2022-11-25 02:13:04 UTC

nyrr

NYRR

Visualization of NYRR.org race result export . Note, this is not an official NYRR product.

2023-01-08 21:22:12 UTC

2023-01-08 22:15:01 UTC

# About this dashboard: 1. You can search your [race search results](https://results.nyrr.org/home) and then: 1. Save them to Excel. 2. Then export to CSV (Make sure the dates are normalized to ISO format) 3. Make them available to Grafana CSV plugin as a file or from the file system You can make a very simple Docker/ PODMAN container to share your NYRR.org race results as follows. You could use the following Dockerfile to make a very simple container: ```Dockerfile # This is not a high performance HTTP server, also it is not recommended for production. # Please check: https://docs.python.org/3/library/http.server.html#http-server-security # Author Jose Vicente Nunez <kodegeek.com@protonmail.com> FROM fedora:37 ARG HTDOCS=/var/www/htdocs ARG UID=1000 ARG USER=www ARG GRP=www EXPOSE 8080/tcp RUN dnf install -y htop curl && \ dnf clean -y all RUN useradd --comment 'WebServer' --home-dir $HTDOCS --create-home --uid $UID --user-group $USER USER $USER:$GRP WORKDIR $HTDOCS CMD exec python3 -m http.server 8080 HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=1 CMD curl --fail --silent --show-error http://localhost:8080 || exit 1 LABEL HOW_TO_BUILD="BUILDKIT=y docker build --file Dockerfile --tag static_server ." LABEL HOW_TO_RUN="docker run --init --detach --name home_fitness --publish 8080:8080 --restart=always --volume /home/josevnz/fitness:/var/www/htdocs:ro static_server" ``` You can see a [demonstration video here](https://youtu.be/RQYhDJw9wtI)

Other

Visualization of NYRR.org race result export

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2023-01-08 21:22:12 UTC

2023-01-08 21:30:31 UTC

pg-overview

PG Overview

This dashboard works with postgres_exporter for prometheus

2023-03-20 02:55:29 UTC

2023-04-10 07:59:21 UTC

# PostgreSQL_exporter ## 1.下载和配置 ​ 下载文件：postgres_exporter-0.10.0.linux-amd64.tar.gz ​ 解压： ```shell tar -C /opt -xvf postgres_exporter-0.10.0.linux-amd64.tar.gz cd /opt && mv postgres_exporter-0.10.0.linux-amd64 postgres_exporter cd postgres_exporter ``` 添加脚本: start.sh ```shell sudo -u postgres DATA_SOURCE_NAME="user=postgres host=/var/run/postgresql/ sslmode=disable" postgres_exporter #需要通过用户postgres启动 ``` 添加至systemd： ```shell vim /usr/lib/systemd/system/postgres_exporter.service [Unit] Description=postgres_exporter After=network.target [Service] Type=simple ExecStart=/PATH/TO/start.sh Restart=on-failure [Install] WantedBy=multi-user.target ``` ​ 启动： ```shell systemctl daemon-reload systemctl start postgres_exporter systemctl status postgres_exporter systemctl enable postgres_exporte ``` ## 2.接入prometheus edit `prometheus.yml` ```yaml - job_name: "postgresql" file_sd_configs: - files: - targets/postgresql.yml refresh_interval: 10m ``` `targets/postgresql.yml` ```yaml - targets: ["10.1xx.1.1xx:9187"] labels: instance: 10.1xx.1.1xx_postgresql targets: "10.1xx.1.1xx:9187" ```

This dashboard works with postgres_exporter for prometheus

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2023-03-20 02:55:29 UTC

2023-04-10 07:59:21 UTC

kubernetes-cluster-monitoring-via-prometheus

Kubernetes cluster monitoring (via Prometheus)

Monitors Kubernetes cluster using Prometheus. Shows overall cluster CPU / Memory / Filesystem usage as well as individual pod, containers, systemd services statistics. Uses cAdvisor metrics only.

2023-03-29 08:22:52 UTC

2023-03-29 08:23:08 UTC

Monitors Kubernetes cluster using Prometheus. Shows overall cluster CPU / Memory / Filesystem usage as well as individual pod, containers, systemd services statistics. Uses cAdvisor metrics only.

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"pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "span": 12, "stack": false, "steppedLine": true, "targets": [ { "expr": "sum (container_memory_working_set_bytes{image!=\"\",name=~\"^k8s_.*\",container_name!=\"POD\",kubernetes_io_hostname=~\"^$Node$\"}) by (container_name, pod_name)", "interval": "10s", "intervalFactor": 1, "legendFormat": "pod: {{ pod_name }} | {{ container_name }}", "metric": "container_memory_usage:sort_desc", "refId": "A", "step": 10 }, { "expr": "sum (container_memory_working_set_bytes{image!=\"\",name!~\"^k8s_.*\",kubernetes_io_hostname=~\"^$Node$\"}) by (kubernetes_io_hostname, name, image)", "interval": "10s", "intervalFactor": 1, "legendFormat": "docker: {{ kubernetes_io_hostname }} | {{ image }} ({{ name }})", "metric": "container_memory_usage:sort_desc", "refId": "B", "step": 10 }, { "expr": "sum (container_memory_working_set_bytes{rkt_container_name!=\"\",kubernetes_io_hostname=~\"^$Node$\"}) by (kubernetes_io_hostname, 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null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": false } ] } ], "title": "All processes memory usage" }, { "collapse": false, "editable": true, "height": "250px", "panels": [ { "aliasColors": {}, "bars": false, "datasource": "${DS_PROMETHEUS}", "decimals": 2, "editable": true, "error": false, "fill": 1, "grid": { "threshold1": null, "threshold1Color": "rgba(216, 200, 27, 0.27)", "threshold2": null, "threshold2Color": "rgba(234, 112, 112, 0.22)" }, "id": 16, "isNew": true, "legend": { "alignAsTable": true, "avg": true, "current": true, "max": false, "min": false, "rightSide": true, "show": true, "sideWidth": 200, "sort": "current", "sortDesc": true, "total": false, "values": true }, "lines": true, "linewidth": 2, "links": [], "nullPointMode": "connected", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "span": 12, "stack": false, 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(container_name, pod_name)", "hide": false, "interval": "10s", "intervalFactor": 1, "legendFormat": "-> pod: {{ pod_name }} | {{ container_name }}", "metric": "network", "refId": "B", "step": 10 }, { "expr": "- sum (rate (container_network_transmit_bytes_total{image!=\"\",name=~\"^k8s_.*\",kubernetes_io_hostname=~\"^$Node$\"}[1m])) by (container_name, pod_name)", "hide": false, "interval": "10s", "intervalFactor": 1, "legendFormat": "<- pod: {{ pod_name }} | {{ container_name }}", "metric": "network", "refId": "D", "step": 10 }, { "expr": "sum (rate (container_network_receive_bytes_total{image!=\"\",name!~\"^k8s_.*\",kubernetes_io_hostname=~\"^$Node$\"}[1m])) by (kubernetes_io_hostname, name, image)", "hide": false, "interval": "10s", "intervalFactor": 1, "legendFormat": "-> docker: {{ kubernetes_io_hostname }} | {{ image }} ({{ name }})", "metric": "network", "refId": "A", "step": 10 }, { "expr": "- sum (rate (container_network_transmit_bytes_total{image!=\"\",name!~\"^k8s_.*\",kubernetes_io_hostname=~\"^$Node$\"}[1m])) by (kubernetes_io_hostname, name, image)", "hide": false, "interval": "10s", "intervalFactor": 1, "legendFormat": "<- docker: {{ kubernetes_io_hostname }} | {{ image }} ({{ name }})", "metric": "network", "refId": "C", "step": 10 }, { "expr": "sum (rate (container_network_transmit_bytes_total{rkt_container_name!=\"\",kubernetes_io_hostname=~\"^$Node$\"}[1m])) by (kubernetes_io_hostname, rkt_container_name)", "hide": false, "interval": "10s", "intervalFactor": 1, "legendFormat": "-> rkt: {{ kubernetes_io_hostname }} | {{ rkt_container_name }}", "metric": "network", "refId": "E", "step": 10 }, { "expr": "- sum (rate (container_network_transmit_bytes_total{rkt_container_name!=\"\",kubernetes_io_hostname=~\"^$Node$\"}[1m])) by (kubernetes_io_hostname, rkt_container_name)", "hide": false, "interval": "10s", "intervalFactor": 1, "legendFormat": "<- rkt: {{ kubernetes_io_hostname 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"value_type": "cumulative" }, "type": "graph", "xaxis": { "show": true }, "yaxes": [ { "format": "Bps", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": false } ] } ], "title": "All processes network I/O" } ], "time": { "from": "now-5m", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "templating": { "list": [ { "allValue": ".*", "current": {}, "datasource": "${DS_PROMETHEUS}", "hide": 0, "includeAll": true, "multi": false, "name": "Node", "options": [], "query": "label_values(kubernetes_io_hostname)", "refresh": 1, "type": "query" } ] }, "annotations": { "list": [] }, "refresh": "10s", "schemaVersion": 12, "version": 13, "links": [], "gnetId": 18379 }

2023-03-29 08:22:52 UTC

celestia-appd

Celestia Appd

Then most important metrics.

2023-04-07 10:01:55 UTC

2023-04-10 02:21:11 UTC

Set your Prometheus job (the one connect to celestia-appd) name to 'validator', then it should work.

Other

Golang internal statistics.

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"none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Self Power" }, "properties": [ { "id": "custom.axisPlacement", "value": "right" } ] } ] }, "gridPos": { "h": 7, "w": 10, "x": 4, "y": 8 }, "id": 8, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "tendermint_consensus_validators", "legendFormat": "Validators", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "tendermint_consensus_validator_power", "hide": false, "legendFormat": "Self Power", "range": true, "refId": "B" } ], "title": 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"semi-dark-orange", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "Validators" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-red", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 7, "w": 10, "x": 14, "y": 8 }, "id": 20, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "tendermint_consensus_byzantine_validators", "legendFormat": "Validators", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "tendermint_consensus_byzantine_validators_power", "hide": false, "legendFormat": "Power", "range": true, "refId": "B" } ], "title": "Fraud Validators", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "fixedColor": "red", "mode": "fixed" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] } }, "overrides": [] }, "gridPos": { "h": 9, "w": 4, "x": 0, "y": 10 }, "id": 34, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.4.3", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "exemplar": false, "expr": "tendermint_consensus_validator_missed_blocks{job=\"validator\"}", "instant": true, "legendFormat": "__auto", "range": false, "refId": "A" } ], "title": "Missed Blocks", "type": "stat" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 7, "w": 10, "x": 4, "y": 15 }, "id": 45, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": false }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "tendermint_p2p_peers", "legendFormat": "__auto", "range": true, "refId": "A" } ], "title": "P2P Peers", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": true, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 40, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "normal" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "bytes" }, "overrides": [ { "matcher": { "id": "byRegexp", "options": "BlockRequest - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-purple", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "BlockResponse - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "super-light-purple", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "StatusRequest - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-blue", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "StatusResponse - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "super-light-blue", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "BlockPart - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-green", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "HasVote - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "super-light-green", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "NewRoundStep - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-yellow", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "NewValidBlock - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "super-light-yellow", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "Proposal - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-orange", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "Vote - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "super-light-orange", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "VoteSetBits - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-red", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "VoteSetMaj23 - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "super-light-red", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "Txs - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "#808080", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "PexAddrs - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "#ffffff", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 7, "w": 10, "x": 14, "y": 15 }, "id": 43, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "right", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "label_replace(rate(tendermint_p2p_message_receive_bytes_total{job=\"validator\"}[$__rate_interval]), \"type\", \"$1 - received\", \"message_type\", \".*_(.*)\")", "interval": "", "legendFormat": "{{type}}", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "-label_replace(rate(tendermint_p2p_message_send_bytes_total{job=\"validator\"}[$__rate_interval]), \"type\", \"$1 - sent\", \"message_type\", \".*_(.*)\")", "hide": false, "legendFormat": "{{type}}", "range": true, "refId": "B" } ], "title": "P2P Message", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "description": "", "fieldConfig": { "defaults": { "color": { "fixedColor": "green", "mode": "fixed" }, "decimals": 3, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 9, "w": 4, "x": 0, "y": 19 }, "id": 36, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.4.3", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "exemplar": false, "expr": "sum(tendermint_consensus_validator_power{job=\"validator\"})/sum(tendermint_consensus_validators_power{job=\"validator\"})*10", "instant": true, "legendFormat": "__auto", "range": false, "refId": "A" } ], "title": "Power Share", "type": "stat" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 6, "w": 10, "x": 4, "y": 22 }, "id": 32, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": false }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "rate(tendermint_consensus_num_txs{job=\"validator\"}[$__rate_interval])", "legendFormat": "__auto", "range": true, "refId": "A" } ], "title": "Transactions", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Tx Size" }, "properties": [ { "id": "unit", "value": "bytes" } ] } ] }, "gridPos": { "h": 6, "w": 10, "x": 14, "y": 22 }, "id": 38, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "tendermint_mempool_size{job=\"validator\"}", "legendFormat": "Pool Size", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "increase(tendermint_mempool_tx_size_bytes_sum{job=\"validator\"}[$__rate_interval])", "hide": false, "legendFormat": "Tx Size", "range": true, "refId": "B" } ], "title": "Memory Pool", "type": "timeseries" }, { "gridPos": { "h": 1, "w": 24, "x": 0, "y": 28 }, "id": 10, "title": "Go", "type": "row" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "s" }, "overrides": [ { "matcher": { "id": "byName", "options": "Allocated Memory" }, "properties": [ { "id": "unit", "value": "bytes" } ] } ] }, "gridPos": { "h": 8, "w": 24, "x": 0, "y": 29 }, "id": 28, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "rate(process_cpu_seconds_total{job=\"validator\"}[$__rate_interval])", "legendFormat": "CPU Time", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "go_memstats_alloc_bytes{job=\"validator\"}", "hide": false, "legendFormat": "Allocated Memory", "range": true, "refId": "B" } ], "title": "CPU and Memory", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "axisSoftMin": 0, "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 37 }, "id": 2, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "exemplar": false, "expr": "go_gc_duration_seconds{job=\"validator\"}", "instant": false, "interval": "", "legendFormat": "quantile={{quantile}}", "range": true, "refId": "A" } ], "title": "GC Duration (s)", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "axisSoftMin": 0, "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Threads" }, "properties": [ { "id": "custom.axisPlacement", "value": "right" } ] } ] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 37 }, "id": 6, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "go_goroutines{job=\"validator\"}", "legendFormat": "Routines", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "go_threads{job=\"validator\"}", "hide": false, "legendFormat": "Threads", "range": true, "refId": "B" } ], "title": "Go Routines and Threads", "type": "timeseries" } ], "refresh": "10s", "revision": 1, "schemaVersion": 38, "style": "dark", "tags": [ "Celestia" ], "templating": { "list": [] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Celestia Appd", "uid": "q9cFcDenk", "version": 10, "weekStart": "", "gnetId": 18459 }

2023-04-07 10:01:55 UTC

2023-04-07 10:13:11 UTC

autometrics-function-explorer

Autometrics Function Explorer

Dig into the metrics for specific functions instrumented with Autometrics

2023-04-12 17:54:58 UTC

2023-04-12 18:00:54 UTC

This is a dashboard that will work for any project instrumented with [Autometrics](https://autometrics.dev).

Other

{ "__inputs": [ { "name": "DS_PROMETHEUS", "label": "Prometheus", "description": "", "type": "datasource", "pluginId": "prometheus", "pluginName": "Prometheus" } ], "__elements": {}, "__requires": [ { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "9.4.3" }, { "type": "datasource", "id": "prometheus", "name": "Prometheus", "version": "1.0.0" }, { "type": "panel", "id": "timeseries", "name": "Time series", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": { "type": "datasource", "uid": "grafana" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "description": "Dig into the metrics for specific functions instrumented with Autometrics", "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 0, "id": null, "links": [], "liveNow": false, "panels": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "description": "Calls per second is calculated as the average over a 5-minute window", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "Calls per Second", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 8, "w": 24, "x": 0, "y": 0 }, "id": 4, "options": { "legend": { "calcs": [ "lastNotNull", "max" ], "displayMode": "table", "placement": "right", "showLegend": true, "sortBy": "Max", "sortDesc": true }, "tooltip": { "mode": "multi", "sort": "desc" } }, "pluginVersion": "9.4.1", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum by (function, module) (rate(function_calls_count{function=~\"${function}\"}[5m])) > 0", "format": "time_series", "instant": false, "interval": "", "legendFormat": "", "refId": "A" } ], "title": "Request Rate", "transformations": [], "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic", "seriesBy": "max" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "Error Rate", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 8, "w": 24, "x": 0, "y": 8 }, "id": 2, "options": { "legend": { "calcs": [ "lastNotNull", "max" ], "displayMode": "table", "placement": "right", "showLegend": true, "sortBy": "Max", "sortDesc": true }, "tooltip": { "mode": "multi", "sort": "desc" } }, "pluginVersion": "9.4.1", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "(sum by(function, module) (rate(function_calls_count{result=\"error\", function=~\"${function}\"}[5m])) > 0)\n/ (sum by(function, module) (rate(function_calls_count{function=~\"${function}\"}[5m])) > 0)", "interval": "", "legendFormat": "", "range": true, "refId": "A" } ], "title": "Error Rate", "type": "timeseries" } ], "refresh": "5m", "revision": 1, "schemaVersion": 38, "style": "dark", "tags": [ "autometrics" ], "templating": { "list": [ { "allValue": "__none__", "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(function_calls_count, function)", "hide": 0, "includeAll": false, "label": "Show Function(s)", "multi": true, "name": "function", "options": [], "query": { "query": "label_values(function_calls_count, function)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Autometrics Function Explorer", "uid": "UmP_Puf4z", "version": 6, "weekStart": "", "gnetId": 18504 }

2023-04-12 17:54:58 UTC

2023-04-12 18:00:27 UTC

jmeter-test-results-influxdb2-standart-backend-listener

JMeter test results influxdb2 standart backend listener

Dashboard for visualizing JMeter metrics in influxdb2

2022-11-24 15:19:31 UTC

2022-11-28 06:58:20 UTC

The dashboard allows to visualize JMeter metrics stored in Influxdb2. Dependencies: - Grafana 9.0.3^ - InfluxDB 2.0^ - BackendListener: org.apache.jmeter.visualizers.backend.influxdb.InfluxdbBackendListenerClient *How to configure:* 1. Create new InfluxDB Bucket and Token. 2. Configure JMeter backend listener. ``` - Backend listener implementation: org.apache.jmeter.visualizers.backend.influxdb.InfluxdbBackendListenerClient - influxdbUrl: http://YOUR_INFLUXDB_IP:8086/api/v2/write?org=YOUR_ORG_NAME&bucket=YOUR_BUCKET_NAME - application: YOUR_APP_NAME - measurement: jmeter - summaryOnly: false - samplersRegex: .* - percentiles: 99;95;90;50 - testTitle: YOUR_TEST_ID (For example: ${__time(yyyyMMdd-HHmm)}-appname) - influxdbToken: YOUR_TOKEN (Note: it should be added manually using add button) ``` 3. OPTIONAL: Configure backend listener in jmeter.properties or user.properties files: Let's say we need to generate around 6000 rps in our test, we can set up sending the test result once every 10 seconds by changing the «backend_influxdb.send_interval» parameter. Then we need to change «QUEUE_SIZE» and «backend_metrics_large_window» to 60000 (6000 x 10 s). For good performance it better to use «timed» window mode. That completely cleans up the set of collected metrics after sending, while fixed constantly stores this queue, adds new values to the end and deletes the old ones. ``` QUEUE_SIZE=60000 //default: 5000 backend_metrics_window_mode=timed //default: fixed backend_metrics_large_window=60000 //default: 5000 backend_influxdb.send_interval=10 //default: 5 seconds ``` 4. Add InfluxDB as a data source in Grafana using your bucket and token. 5. Create a folder for your project if it doesn't already exist. 6. Import Grafana dashboard

Other

{ "__inputs": [ { "name": "DS_TEMPLATE-2", "label": "TEMPLATE-2", "description": "InfluxDB data source name", "type": "datasource", "pluginId": "influxdb", "pluginName": "InfluxDB" }, { "name": "VAR_SEND_INTERVAL", "type": "constant", "label": "send_interval", "value": "10", "description": "Send metrics in specified time range" } ], "__elements": {}, "__requires": [ { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "9.0.3" }, { "type": "datasource", "id": "influxdb", "name": "InfluxDB", "version": "1.0.0" }, { "type": "panel", "id": "stat", "name": "Stat", "version": "" }, { "type": "panel", "id": "table", "name": "Table", "version": "" }, { "type": "panel", "id": "timeseries", "name": "Time series", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": { "type": "grafana", "uid": "-- Grafana --" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 0, "id": null, "iteration": 1669302251189, "links": [], "liveNow": false, "panels": [ { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 0 }, "id": 24, "panels": [], "title": "Summary", "type": "row" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 0, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 4, "w": 4, "x": 0, "y": 1 }, "id": 12, "options": { "colorMode": "value", "graphMode": "none", "justifyMode": "auto", "orientation": "horizontal", "reduceOptions": { "calcs": [ "sum" ], "fields": "", "values": false }, "text": {}, "textMode": "auto" }, "pluginVersion": "9.0.3", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"count\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> sum()", "refId": "A" } ], "title": "Total Requests", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] } }, "overrides": [] }, "gridPos": { "h": 4, "w": 4, "x": 4, "y": 1 }, "id": 13, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "valueSize": 50 }, "textMode": "auto" }, "pluginVersion": "9.0.3", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"countError\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> group(columns: [\"_field\"])\r\n |> sum()", "refId": "A" } ], "title": "Total Errors", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 4, "w": 4, "x": 8, "y": 1 }, "id": 38, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "valueSize": 50 }, "textMode": "auto" }, "pluginVersion": "9.0.3", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "failuresPercentages = () => {\r\n\r\ncountResponseTime=from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"count\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"application\"])\r\n |> sum()\r\n |> keep(columns: [\"_value\", \"application\"])\r\n \r\nsumerrorCount=from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"countError\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"application\"])\r\n |> sum()\r\n |> keep(columns: [\"_value\", \"application\"])\r\n \r\n return join(\r\n tables:{countResponseTime:countResponseTime, sumerrorCount:sumerrorCount},\r\n on:[\"application\"]\r\n )\r\n |> map(fn:(r) => ({\r\n \r\n percentage: r._value_sumerrorCount / r._value_countResponseTime \r\n })) \r\n}\r\n\r\nfailuresPercentages()", "refId": "B" } ], "title": "Error Rate", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 4, "w": 4, "x": 12, "y": 1 }, "id": 39, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "valueSize": 50 }, "textMode": "auto" }, "pluginVersion": "9.0.3", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "successPercentage = () => {\r\n\r\nallCount=from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"count\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"application\"])\r\n |> sum()\r\n |> keep(columns: [\"_value\", \"application\"])\r\n \r\nokCount=from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"count\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"ok\")\r\n |> group(columns: [\"application\"])\r\n |> sum()\r\n |> keep(columns: [\"_value\", \"application\"])\r\n \r\n return join(\r\n tables:{allCount:allCount, okCount:okCount},\r\n on:[\"application\"]\r\n )\r\n |> map(fn:(r) => ({\r\n \r\n percentage: r._value_okCount / r._value_allCount \r\n })) \r\n}\r\n\r\nsuccessPercentage()", "refId": "B" } ], "title": "Success Rate", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "bytes" }, "overrides": [] }, "gridPos": { "h": 4, "w": 4, "x": 16, "y": 1 }, "id": 14, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "sum" ], "fields": "", "values": false }, "text": { "valueSize": 50 }, "textMode": "auto" }, "pluginVersion": "9.0.3", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"rb\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> sum()", "refId": "A" } ], "title": "Received Bytes", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "bytes" }, "overrides": [] }, "gridPos": { "h": 4, "w": 4, "x": 20, "y": 1 }, "id": 15, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "sum" ], "fields": "", "values": false }, "text": { "valueSize": 50 }, "textMode": "auto" }, "pluginVersion": "9.0.3", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"sb\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> sum()", "refId": "A" } ], "title": "Sent Bytes", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "left", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 4, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineStyle": { "fill": "solid" }, "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "reqps" }, "overrides": [ { "matcher": { "id": "byFrameRefID", "options": "C" }, "properties": [ { "id": "custom.drawStyle", "value": "bars" }, { "id": "custom.lineWidth", "value": 0 }, { "id": "custom.barAlignment", "value": 0 }, { "id": "custom.showPoints", "value": "auto" }, { "id": "custom.fillOpacity", "value": 76 } ] }, { "matcher": { "id": "byName", "options": "Errors Per Second" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-red", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "Transations Per Second" }, "properties": [ { "id": "color", "value": { "fixedColor": "blue", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "Active Threads" }, "properties": [ { "id": "color", "value": { "fixedColor": "green", "mode": "fixed" } }, { "id": "custom.axisPlacement", "value": "right" }, { "id": "unit", "value": "threads" } ] } ] }, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 5 }, "id": 2, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"maxAT\")\r\n |> keep(columns: [\"_field\", \"_value\", \"_time\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: last, createEmpty: true)\r\n |> set(key: \"_field\", value: \"Active Threads\")", "refId": "B" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"count\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> keep(columns: [\"_field\", \"_value\", \"_time\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: sum, createEmpty: true)\r\n |> map(fn: (r) => ({ r with _value: float(v: r._value / float(v: ${send_interval}))}))\r\n |> set(key: \"_field\", value: \"Transations Per Second\")", "refId": "D" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"countError\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: sum, createEmpty: true)\r\n |> map(fn: (r) => ({ r with _value: float(v: r._value / float(v: ${send_interval}))}))\r\n |> set(key: \"_field\", value: \"Errors Per Second\")", "refId": "C" } ], "title": "Total Throughput", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "binBps" }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 5 }, "id": 25, "options": { "legend": { "calcs": [ "mean" ], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"sb\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: sum, createEmpty: true)\r\n |> map(fn: (r) => ({ r with _value: float(v: r._value / float(v: ${send_interval}))}))\r\n |> set(key: \"_field\", value: \"Sent Bytes\")", "refId": "A" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"rb\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: sum, createEmpty: true)\r\n |> map(fn: (r) => ({ r with _value: float(v: r._value / float(v: ${send_interval}))}))\r\n |> set(key: \"_field\", value: \"Received Bytes\")", "refId": "B" } ], "title": "Network Traffic", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "ms" }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 13 }, "id": 7, "options": { "legend": { "calcs": [ "mean" ], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"avg\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: mean, createEmpty: true)", "refId": "A" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"pct50.0\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: median, createEmpty: true)", "refId": "B" } ], "title": "Avg Response time", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "ms" }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 13 }, "id": 20, "options": { "legend": { "calcs": [ "mean" ], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"pct90.0\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: mean, createEmpty: true)", "refId": "A" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"pct95.0\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: mean, createEmpty: true)", "refId": "B" } ], "title": "pct90 Response time", "type": "timeseries" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 21 }, "id": 22, "panels": [], "title": "Per sample", "type": "row" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "displayName": "${__field.labels.transaction}", "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "ms" }, "overrides": [] }, "gridPos": { "h": 9, "w": 24, "x": 0, "y": 22 }, "id": 19, "options": { "legend": { "calcs": [ "mean" ], "displayMode": "list", "placement": "right" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"avg\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> group(columns: [\"transaction\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: sum, createEmpty: true)", "refId": "A" } ], "title": "Avg Response time", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "displayName": "${__field.labels.transaction}", "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "ms" }, "overrides": [] }, "gridPos": { "h": 9, "w": 24, "x": 0, "y": 31 }, "id": 17, "options": { "legend": { "calcs": [ "mean" ], "displayMode": "list", "placement": "right" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"pct90.0\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> group(columns: [\"transaction\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: mean, createEmpty: true)", "refId": "A" } ], "title": "pct90 Response time", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green" } ] }, "unit": "reqps" }, "overrides": [] }, "gridPos": { "h": 11, "w": 24, "x": 0, "y": 40 }, "id": 18, "options": { "legend": { "calcs": [ "mean" ], "displayMode": "list", "placement": "right" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"count\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> group(columns: [\"transaction\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: mean, createEmpty: true)\r\n |> map(fn: (r) => ({ r with _value: float(v: r._value / float(v: ${send_interval}))}))", "refId": "A" } ], "title": "Total Throughput Per Sample", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "displayMode": "auto", "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "ms" }, "overrides": [ { "matcher": { "id": "byName", "options": "Count" }, "properties": [ { "id": "unit", "value": "none" } ] }, { "matcher": { "id": "byName", "options": "Error" }, "properties": [ { "id": "unit", "value": "none" } ] }, { "matcher": { "id": "byName", "options": "ErrorRate" }, "properties": [ { "id": "unit", "value": "percentunit" }, { "id": "decimals", "value": 2 }, { "id": "noValue", "value": "0 %" } ] } ] }, "gridPos": { "h": 11, "w": 24, "x": 0, "y": 51 }, "id": 10, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "frameIndex": 0, "showHeader": true }, "pluginVersion": "9.0.3", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"avg\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n |> group(columns: [\"transaction\"])\r\n |> mean()\r\n |> group()\r\n |> rename(columns: {\"_value\": \"Avg RT\"})", "refId": "B" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"pct50.0\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n |> group(columns: [\"transaction\"])\r\n |> median()\r\n |> group()\r\n |> rename(columns: {\"_value\": \"Median RT\"})", "refId": "H" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"min\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n |> group(columns: [\"transaction\"])\r\n |> min()\r\n |> group()\r\n |> rename(columns: {\"_value\": \"Min RT\"})", "refId": "A" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"max\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n |> group(columns: [\"transaction\"])\r\n |> max()\r\n |> group()\r\n |> rename(columns: {\"_value\": \"Max RT\"})", "refId": "C" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"pct90.0\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n |> group(columns: [\"transaction\"])\r\n |> quantile(q: 0.90, method: \"exact_mean\")\r\n |> group()\r\n |> rename(columns: {\"_value\": \"pct90\"})", "refId": "D" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"pct95.0\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n |> group(columns: [\"transaction\"])\r\n |> quantile(q: 0.95, method: \"exact_mean\")\r\n |> group()\r\n |> rename(columns: {\"_value\": \"pct95\"})", "refId": "I" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"count\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n |> group(columns: [\"transaction\"])\r\n |> sum()\r\n |> group()\r\n |> rename(columns: {\"_value\": \"Count\"})", "refId": "F" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "countAll = from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"count\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> group(columns: [\"transaction\"])\r\n |> sum()\r\n |> group()\r\n |> toFloat()\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n\r\ncountError = from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"count\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"ko\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> group(columns: [\"transaction\"])\r\n |> sum()\r\n |> group()\r\n |> toFloat()\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n\r\njoin(\r\n tables:{countAll:countAll, countError:countError},\r\n on:[\"transaction\"]\r\n )\r\n |> map(fn:(r) => ({\r\n transaction: r.transaction,\r\n ErrorRate: r._value_countError / r._value_countAll \r\n})) ", "refId": "G" } ], "title": "Metrics", "transformations": [ { "id": "merge", "options": {} } ], "type": "table" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 62 }, "id": 28, "panels": [], "title": "Errors", "type": "row" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green" } ] }, "unit": "errors" }, "overrides": [] }, "gridPos": { "h": 11, "w": 24, "x": 0, "y": 63 }, "id": 26, "options": { "legend": { "calcs": [ "sum" ], "displayMode": "list", "placement": "right" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"count\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"ko\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> group(columns: [\"transaction\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: sum, createEmpty: true)", "refId": "A" } ], "title": "Errors Per Sample", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "displayMode": "auto", "inspect": false }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green" } ] } }, "overrides": [] }, "gridPos": { "h": 11, "w": 24, "x": 0, "y": 74 }, "id": 29, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": true }, "showHeader": true, "sortBy": [ { "desc": true, "displayName": "Count" } ] }, "pluginVersion": "9.0.3", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"count\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> filter(fn: (r) => r[\"responseCode\"] != \"\")\r\n |> filter(fn: (r) => r[\"responseCode\"] != \"0\")\r\n |> group(columns: [\"transaction\", \"responseCode\", \"responseMessage\"])\r\n |> keep(columns: [\"_value\", \"transaction\", \"responseCode\", \"responseMessage\"])\r\n |> sum()\r\n |> group()\r\n |> rename(columns: {\"_value\": \"Count\"})", "refId": "A" } ], "title": "Error Info", "type": "table" } ], "refresh": false, "schemaVersion": 36, "style": "dark", "tags": [], "templating": { "list": [ { "hide": 2, "name": "send_interval", "query": "${VAR_SEND_INTERVAL}", "skipUrlSync": false, "type": "constant", "current": { "value": "${VAR_SEND_INTERVAL}", "text": "${VAR_SEND_INTERVAL}", "selected": false }, "options": [ { "value": "${VAR_SEND_INTERVAL}", "text": "${VAR_SEND_INTERVAL}", "selected": false } ] }, { "current": {}, "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "definition": "import \"influxdata/influxdb/schema\"\r\n\r\nschema.tagValues(\r\n bucket: v.bucket,\r\n tag: \"application\",\r\n predicate: (r) => r._measurement == \"events\",\r\n start: -1000d\r\n )", "hide": 0, "includeAll": false, "label": "application", "multi": false, "name": "application", "options": [], "query": "import \"influxdata/influxdb/schema\"\r\n\r\nschema.tagValues(\r\n bucket: v.bucket,\r\n tag: \"application\",\r\n predicate: (r) => r._measurement == \"events\",\r\n start: -1000d\r\n )", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "JMeter test results influxdb2 standart backend listener", "uid": "JMeter-test-results-influxdb2", "version": 3, "weekStart": "", "gnetId": 17472, "description": "Dashboard for visualizing JMeter metrics in influxdb2" }

2022-11-24 15:19:31 UTC

2022-11-25 08:45:51 UTC

live-agent-adapter

Live Agent Adapter

Live Agent Adapter Built-in Dashboard

2023-02-22 06:09:49 UTC

2023-02-22 06:11:34 UTC

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false, "panels": [ { "collapsed": true, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 0 }, "id": 4, "panels": [], "title": "UpstreamWorks API", "type": "row" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 1 }, "id": 13, "options": { "displayMode": "gradient", "minVizHeight": 10, "minVizWidth": 0, "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "showUnfilled": true }, "pluginVersion": "9.3.6", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(laa_uw_chat_session_requests_total) by (job)", "legendFormat": "Requested", "range": true, "refId": "A" }, { "datasource": { "type": 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2023-02-22 06:09:49 UTC

2023-02-22 06:11:35 UTC

1-windows-exporter-for-prometheus-dashboard-cn-v20201012

windows_exporter_add_process

fork 1 windows_exporter for Prometheus Dashboard CN v20201012

2023-03-08 07:58:02 UTC

2023-03-08 08:02:27 UTC

增加了进程监控

Windows的Prometheus监控看板展示，增加了资源汇总展示，优化了明细展示。更新支持windows_exporter 0.13.0。

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false }, { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS_203}" }, "definition": "label_values(windows_cs_hostname{job=~\"$job\",hostname=~\"$hostname\"}, instance)", "hide": 0, "includeAll": false, "label": "instance", "multi": false, "name": "instance", "options": [], "query": { "query": "label_values(windows_cs_hostname{job=~\"$job\",hostname=~\"$hostname\"}, instance)", "refId": "Prometheus_203-instance-Variable-Query" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 5, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS_203}" }, "definition": "label_values(windows_cs_hostname{job=~\"$job\",instance=~\"$instance\"}, hostname)", "hide": 2, "includeAll": false, "label": "展示使用的主机名", "multi": false, "name": "show_hostname", "options": [], "query": { "query": "label_values(windows_cs_hostname{job=~\"$job\",instance=~\"$instance\"}, hostname)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS_203}" }, "definition": "label_values(windows_process_handles, process)", "hide": 0, "includeAll": false, "multi": false, "name": "process", "options": [], "query": { "query": "label_values(windows_process_handles, process)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" } ] }, "time": { "from": "now-3h", "to": "now" }, "timepicker": { "refresh_intervals": [ "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "windows_exporter_add_process", "uid": "Kdh0OoSGz", "version": 14, "weekStart": "" }

2023-03-08 07:58:02 UTC

2023-03-08 08:02:27 UTC

ra-l-s-dashboard-node-sync

Raúl's dashboard -- NODE SYNC

Dashboard to track Filecoin Lotus node API endpoints.

2023-03-27 07:16:35 UTC

2023-03-27 07:17:50 UTC

Dashboard to track Filecoin Lotus node API endpoints.

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2023-03-27 07:16:36 UTC

pint-dashboard

Pint Dashboard

Lint your Prometheus

2023-04-05 06:57:33 UTC

2023-04-05 06:58:18 UTC

Lint your Prometheus

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2023-04-05 06:57:33 UTC

2023-04-05 06:58:17 UTC

tableaux-de-bord-mongodb

Tableaux de bord MongoDB

MongoDB -> Mongodb_Exporter -> Prometheus -> Grafana.

2023-05-29 11:03:51 UTC

2023-05-29 14:20:51 UTC

# Environnement * Docker : Mongodb 6.0.5 * Docker : percona/mongodb_exporter 2.37.0 * Docker : prometheus 2.44.0 * Docker : grafana 9.5.2 Attention le tableau de bord ne fonctionne qu'avec la version 2.x de mongodb_exporter. ## Mongodb * Version 6.0.5 ; * Authentification désactivée pour les tests avec mongod_exporter ; * Une instance, 160 collections. ## Mongodb_exporter La configuration du docker compose : ```yml mongodb_exporter: platform: "linux" container_name: mongodb_exporter image: percona/mongodb_exporter:${mongo_exporter_version} command: - '--mongodb.global-conn-pool' - '--collector.diagnosticdata' - '--discovering-mode' - '--mongodb.uri=mongodb://mongodb:17001/admin?ssl=false' ``` Pour l'authentification : - Il faut ajouter `--auth` comme paramètre à l'image docker de MongoDB. - `--mongodb.uri=mongodb://{login}:{password}@mongodb:27001/admin?ssl=false L'utilisation de `- '--collector.diagnosticdata'` et de `--discovering-mode` remonte 5936 indicateurs, c'est déjà trop ! ## Prometheus Pour la configuration, il ne faudra pas oublier d'ajouter dans le fichier `prometheus.yml` la source pour la collecte des données. Pour une configuratyion docker, il faudra ajouter soit de façon statique l'adresse Ip du container ou utiliser la résolution DNS interne pour docker. ```yml # MONGODB - job_name: Mongodb_exporter scrape_interval: 5s tls_config: insecure_skip_verify: true dns_sd_configs: - names: - mongodb_exporter type: A port: 9216 ``` `mongodb_exporter`est le nom de l'image docker du service mongodb_exporter. ## Grafana J'utilise deux variables : `$interval`et `$instance`. ## Auteur Laurent HADJADJ - CC 4.0 BY-NC-SA

Other

MongoDB -> Mongodb_Exporter -> Prometheus -> Grafana.

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"current": { "selected": false, "text": "auto", "value": "$__auto_interval_interval" }, "hide": 0, "name": "interval", "options": [ { "selected": true, "text": "auto", "value": "$__auto_interval_interval" }, { "selected": false, "text": "1m", "value": "1m" }, { "selected": false, "text": "10m", "value": "10m" }, { "selected": false, "text": "30m", "value": "30m" }, { "selected": false, "text": "1h", "value": "1h" }, { "selected": false, "text": "6h", "value": "6h" }, { "selected": false, "text": "12h", "value": "12h" }, { "selected": false, "text": "1d", "value": "1d" }, { "selected": false, "text": "7d", "value": "7d" }, { "selected": false, "text": "14d", "value": "14d" }, { "selected": false, "text": "30d", "value": "30d" } ], "query": "1m,10m,30m,1h,6h,12h,1d,7d,14d,30d", "queryValue": "", "refresh": 2, "skipUrlSync": false, "type": "interval" } ] }, "time": { "from": "2023-05-28T06:26:53.032Z", "to": "2023-05-28T09:06:53.032Z" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "Tableaux de bord MongoDB", "uid": "AyWQt9jWk", "version": 62, "weekStart": "monday" }

2023-05-29 11:03:51 UTC

2023-05-29 14:20:51 UTC

rabbitmq-queue

RabbitMQ / Queue

A dashboard that monitors RabbitMQ. It is created using the (rabbitmq-mixin)[https://github.com/adinhodovic/rabbitmq-mixin]. Focuses on per-queue visualization based on detailed metrics.

2022-10-30 22:23:04 UTC

2022-10-30 22:55:41 UTC

A dashboard that monitors RabbitMQ. It is created using the (rabbitmq-mixin)[https://github.com/adinhodovic/rabbitmq-mixin]. Focuses on per-queue visualization based on detailed metrics. Combines well with the official dashboard for RabbitMQ. I've written a blog post [on RabbiqMQ per-queue monitoring](https://hodovi.cc/blog/rabbitmq-per-queue-monitoring/).

A dashboard that monitors RabbitMQ. It is created using the (rabbitmq-mixin)[https://github.com/adinhodovic/rabbitmq-mixin].

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2022-10-30 22:23:04 UTC

2022-10-30 22:25:58 UTC

k8s-memory-namespace

k8s Memory namespace

Showing memory in namespace, showing memory in nodes, showing memory all cluster pods and memory/cpu limits/requests

2022-11-10 14:26:44 UTC

2022-11-10 14:29:27 UTC

Showing memory in namespace, showing memory in nodes, showing memory all cluster pods and memory/cpu limits/requests

Showing memory in namespace, showing memory in nodes, and showing memory all cluster pods

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2022-11-10 14:26:44 UTC

2022-11-10 14:28:25 UTC

besu-overview

Besu Overview

Provides an overview of Besu nodes

2022-11-17 08:26:58 UTC

2022-11-17 12:20:32 UTC

nodeExporter

Provides an overview of Besu nodes

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"query_result(ethereum_blockchain_height or besu_blockchain_height)", "hide": 0, "includeAll": true, "label": "System", "multi": true, "name": "system", "options": [], "query": "query_result(ethereum_blockchain_height or besu_blockchain_height)", "refresh": 2, "regex": "/instance=\"([^\"]*)\"/", "skipUrlSync": false, "sort": 5, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-12h", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "Besu Overview", "uid": "5S-6O8VZk", "version": 4 }

2022-11-17 08:26:58 UTC

tado

Tado

This dashboard uses a Prometheus exporter for the Tado thermostat to simply show the temperature, humidity and heating settings.

2022-12-18 13:28:59 UTC

2022-12-18 13:29:16 UTC

This dashboard uses a Prometheus exporter for the Tado thermostat to simply show the temperature, humidity and heating settings.

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2022-12-18 13:28:59 UTC

new-trade-dashboard

New Trade Dashboard DGRG

Monitoring for trading team

2023-02-14 14:25:54 UTC

2023-02-14 15:01:01 UTC

Monitoring for trading team

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2023-02-14 14:25:54 UTC

2023-02-14 15:01:01 UTC

redpanda-ops-dashboard

Redpanda Ops Dashboard

Redpanda Operations Dashboard

2023-02-18 00:12:36 UTC

2023-03-06 13:54:54 UTC

A dashboard focused on Redpanda operations, with the following charts: - nodes up (count) - node uptime - topics (count) - partitions (count) - throughput (total) - leadership transfers per 5min - partition balance (percentage) - under-replicated partitions (count) - leaderless partitions (count) - storage used (percentage) - storage health - allocated memory (total, percentage) - CPU utilization (total, percentage) - Kafka RPC - cluster info

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2023-02-18 00:12:36 UTC

2023-02-18 00:13:01 UTC

sap-netweaver

SAP NetWeaver

Shows stats of a SAP NetWeaver cluster. Brought to you by SUSE.

2022-09-26 20:19:35 UTC

2022-09-26 20:19:48 UTC

Shows stats of a SAP NetWeaver cluster. Brought to you by SUSE.

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2022-09-26 20:19:35 UTC

sql-servers

SQL Servers

MSSQL Server performance metrics by Instances

2022-11-09 16:14:21 UTC

2022-11-09 16:14:27 UTC

MSSQL Server performance metrics by Instances

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2022-11-09 16:14:21 UTC

podman

Podman Dashboard 中文版(zh_CN)

Podman仪表盘，由冰雪殇璃陌梦基于Prometheus-podman-exporter制作

2022-12-16 13:52:06 UTC

2023-02-05 13:58:11 UTC

# Grafana Podman Dashboard 通过 Grafana 监控 Podman 容器和镜像运行状态,基于`prometheus-podman-exporter`。 [English](https://grafana.com/grafana/dashboards/17639) ## 食用方法 1. 安装并运行[prometheus-podman-exporter](https://github.com/containers/prometheus-podman-exporter),记得添加参数`-i`以启用镜像信息收集 2. 导入仪表盘: - 使用`grafana.com`: > Link: [Podman Dashboard 中文版(zh_CN)](https://grafana.com/grafana/dashboards/17638) > Dashboard ID: `17638` - 或者手动导入 json 文件: [dashboard-json](https://ghproxy.com/https://raw.githubusercontent.com/DreamOfIce/grafana-podman-dashboard/main/dashboards/zh-cn.json) 3. 打开仪表盘看看吧ヾ(≧▽≦\*)o ## License MIT

Other

Podman仪表盘，由冰雪殇璃陌梦基于Prometheus-podman-exporter制作

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2022-12-16 13:52:06 UTC

2022-12-16 15:13:09 UTC

victoriametrics-operator

VictoriaMetrics - operator

Overview for operator VictoriaMetrics v0.25.0 or higher

2023-01-12 10:34:36 UTC

2023-01-12 10:39:02 UTC

# VictoriaMetrics operator overview ## Requirements VictoriaMetrics operator: each revision may have different VictoriaMetrics operator version requirements. Grafana: each revision may have different Grafana version requirements. Configure metrics scraping with Prometheus text exposition format by the compatible collector, such as vmagent, single VictoriaMetrics, Prometheus or any other collector. You can also use [VMServiceScrape](https://github.com/VictoriaMetrics/helm-charts/blob/master/charts/victoria-metrics-k8s-stack/templates/victoria-metrics-operator/servicescrape.yaml) Use Prometheus datasource with Prometheus or VictoriaMetrics URL. See more details about how to configure monitoring here. ## Description Dashboard contains visualization of the most useful metrics displaying the current state and performance of the service. It covers such aspects as working queue depth, number of managed CRDs, resource utilization and others. If you have suggestions, improvements or found a bug - feel free to add an issue or add a review to the dashboard. More information about [operator](https://docs.victoriametrics.com/operator/) and [VictoriaMetrics](https://docs.victoriametrics.com/). New [releases](https://github.com/VictoriaMetrics/operator/releases).

Overview for operator VictoriaMetrics v0.25.0 or higher

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}, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 0 }, "id": 8, "panels": [], "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "refId": "A" } ], "title": "Overview", "type": "row" }, { "datasource": { "type": "prometheus", "uid": "$ds" }, "gridPos": { "h": 3, "w": 4, "x": 0, "y": 1 }, "id": 24, "options": { "code": { "language": "plaintext", "showLineNumbers": false, "showMiniMap": false }, "content": "<div style=\"text-align: center;\">$version</div>", "mode": "markdown" }, "pluginVersion": "9.2.2", "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "refId": "A" } ], "title": "Version", "type": "text" }, { "datasource": { "type": "prometheus", "uid": "$ds" }, "description": "Number of objects at kubernetes cluster per each controller", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 7, "w": 20, "x": 4, "y": 1 }, "id": 14, "options": { "colorMode": "none", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": {}, "textMode": "auto" }, "pluginVersion": "9.2.2", "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "max(operator_controller_objects_count{job=~\"$job\",instance=~\"$instance\"}) by (controller)", "legendFormat": "{{controller}}", "range": true, "refId": "A" } ], "title": "CRD Objects count by controller", "type": "stat" }, { "datasource": { "type": "prometheus", "uid": "$ds" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "s" }, "overrides": [] }, "gridPos": { "h": 4, "w": 4, "x": 0, "y": 4 }, "id": 22, "options": { 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"type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "collapsed": false, "datasource": { "type": "prometheus", "uid": "$ds" }, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 21 }, "id": 6, "panels": [], "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "refId": "A" } ], "title": "Troubleshooting", "type": "row" }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "$ds" }, "description": "Non zero metrics indicates about error with CR object definition (typos or incorrect values) or errors with kubernetes API connection.", "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 22 }, "hiddenSeries": false, "id": 10, "legend": { "alignAsTable": true, "avg": false, "current": true, "max": true, "min": false, "show": true, 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"reconcile errors by controller", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "$ds" }, "description": "Operator limits number of reconcilation events to 5 events per 2 seconds.\n For now, this limit is applied only for vmalert and vmagent controllers.\n It should reduce load at kubernetes cluster and increase operator performance.", "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 22 }, "hiddenSeries": false, "id": 18, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "8.3.2", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "sum(rate(operator_reconcile_throttled_events_total[$__rate_interval])) by(controller)", "legendFormat": "{{controller}}", "range": true, "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "throttled reconcilation events", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "$ds" }, "description": "Number of objects waiting in the queue for reconciliation. Non-zero values indicate that operator cannot process CR objects changes with the given resources.", "fill": 1, "fillGradient": 0, "gridPos": { "h": 11, "w": 12, "x": 0, "y": 30 }, "hiddenSeries": false, "id": 20, "legend": { "alignAsTable": true, "avg": false, "current": true, "max": true, "min": false, "show": true, "total": false, "values": true }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "8.3.2", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "max(workqueue_depth{job=~\"$job\",instance=~\"$instance\"}) by (name)", "legendFormat": "{{label_name}}", "range": true, "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "Wokring queue depth", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "$ds" }, "description": " For controllers with StatefulSet it's ok to see latency greater then 3 seconds. It could be vmalertmanager,vmcluster or vmagent in statefulMode.\n\n For other controllers, latency greater then 1 second may indicate issues with kubernetes cluster or operator's performance.\n ", "fieldConfig": { "defaults": { "unit": "s" }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 11, "w": 12, "x": 12, "y": 30 }, "hiddenSeries": false, "id": 26, "legend": { "alignAsTable": true, "avg": true, "current": false, "max": true, "min": false, "rightSide": false, "show": true, "total": false, "values": true }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "8.3.2", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": 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"legendFormat": "heap inuse", "range": true, "refId": "B" }, { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "sum(go_memstats_stack_inuse_bytes{job=~\"$job\", instance=~\"$instance\"})", "hide": false, "legendFormat": "stack inuse", "range": true, "refId": "C" }, { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "sum(process_resident_memory_bytes{job=~\"$job\", instance=~\"$instance\"})", "hide": false, "legendFormat": "resident", "range": true, "refId": "D" } ], "thresholds": [], "timeRegions": [], "title": "Memory usage ($instance)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "bytes", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "$ds" }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 42 }, "hiddenSeries": false, "id": 30, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "8.3.2", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "rate(process_cpu_seconds_total{job=~\"$job\", instance=~\"$instance\"}[$__rate_interval])", "legendFormat": "CPU cores used", "range": true, "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "CPU ($instance)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "$ds" }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 50 }, "hiddenSeries": false, "id": 32, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "8.3.2", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "sum(go_goroutines{job=~\"$job\", instance=~\"$instance\"})", "legendFormat": "goroutines", "range": true, "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "Goroutines ($instance)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "$ds" }, "fieldConfig": { "defaults": { "unit": "s" }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 50 }, "hiddenSeries": false, "id": 34, "legend": { "alignAsTable": true, "avg": true, "current": false, "max": true, "min": false, "show": true, "total": false, "values": true }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "8.3.2", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "sum(rate(go_gc_duration_seconds_sum{job=~\"$job\", instance=~\"$instance\"}[$__rate_interval]))\n/\nsum(rate(go_gc_duration_seconds_count{job=~\"$job\", instance=~\"$instance\"}[$__rate_interval]))", "legendFormat": "avg gc duration", "range": true, "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "GC duration ($instance)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "s", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } } ], "refresh": "", "schemaVersion": 37, "style": "dark", "tags": [ "operator", "VictoriaMetrics" ], "templating": { "list": [ { "current": { "selected": false, "text": "cloud-c15", "value": "cloud-c15" }, "hide": 0, "includeAll": false, "multi": false, "name": "ds", "options": [], "query": "prometheus", "queryValue": "", "refresh": 1, "regex": "", "skipUrlSync": false, "type": "datasource" }, { "current": {}, "datasource": { "type": "prometheus", "uid": "$ds" }, "definition": "label_values(operator_log_messages_total,job)", "hide": 0, "includeAll": false, "multi": false, "name": "job", "options": [], "query": { "query": "label_values(operator_log_messages_total,job)", "refId": "StandardVariableQuery" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": { "type": "prometheus", "uid": "$ds" }, "definition": "label_values(operator_log_messages_total{job=~\"$job\"},instance)", "hide": 0, "includeAll": true, "multi": false, "name": "instance", "options": [], "query": { "query": "label_values(operator_log_messages_total{job=~\"$job\"},instance)", "refId": "StandardVariableQuery" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": { "type": "prometheus", "uid": "$ds" }, "definition": "label_values(vm_app_version{job=\"$job\", instance=\"$instance\"}, version)", "hide": 2, "includeAll": false, "multi": false, "name": "version", "options": [], "query": { "query": "label_values(vm_app_version{job=\"$job\", instance=\"$instance\"}, version)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 2, "type": "query" } ] }, "time": { "from": "now-15m", "to": "now" }, "timepicker": {}, "timezone": "", "title": "VictoriaMetrics - operator", "uid": "1H179hunk", "version": 1, "weekStart": "", "gnetId": 17869 }

2023-01-12 10:34:36 UTC

blackbox-exporter

BlackBox Exporter

Prometheus BlackBox Exporter Dashboard

2023-04-16 20:09:33 UTC

2023-04-27 06:31:04 UTC

# Dashboard for Prometheus Blackbox Exporter uses Prometheus Blackbox-Exporter to retreive metrics.

Other

Prometheus BlackBox Exporter Dashboard

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"label_values(probe_success{job=~\"$job\"}, instance)", "hide": 0, "includeAll": true, "label": "Instance", "multi": false, "name": "instance", "options": [], "query": { "query": "label_values(probe_success{job=~\"$job\"}, instance)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 2, "type": "query" } ] }, "time": { "from": "now-12h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "BlackBox Exporter", "uid": "WD8SaY-4z", "version": 36, "weekStart": "", "gnetId": 18538 }

2023-04-16 20:09:33 UTC

2023-04-21 07:42:01 UTC

grafana-dashboard-for-cloudflare-analytics-and-overview

Grafana Dashboard for Cloudflare Analytics and Overview

Grafana Dashboard for Cloudflare - Pulls out all the Cloudflare Information, and Analytics from the RESTful API.

2022-10-12 03:04:24 UTC

2022-10-12 03:04:44 UTC

Grafana Dashboard for Cloudflare - Pulls out all the Cloudflare Information, and Analytics from the RESTful API.

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2022-10-12 03:04:24 UTC

k8s-resource-monitoring

K8s Resource Monitoring

The dashboard offers a simple view of the resource consumption of the deployed pods and their impact on the cluster. The outline of requests and limits for each pod and their totals can be used as a tool to determine appropriate resource quotas for your cluster.

2022-11-08 16:52:45 UTC

2022-11-08 16:58:19 UTC

# Kubernetes resource monitoring Grafana dashboard [](https://github.com/instrumentisto/grafana-dashboard-kubernetes-prometheus/blob/master/LICENSE.md) [](https://github.com/prometheus/prometheus) [](https://github.com/grafana/grafana) [](https://github.com/yeusiukou/grafana-k8s-resource-monitoring) The dashboard offers a simple view of the resource consumption of the deployed pods and their impact on the cluster. The outline of requests and limits for each pod and their totals can be used as a tool to determine appropriate resource quotas for your cluster. ### Prerequisites [Kubernetes](http://kubernetes.io/) cluster with deployed [Prometheus](https://prometheus.io/). Prometheus will use metrics provided by: - [cAdvisor](https://github.com/google/cadvisor) (runs on each node by default) - node-exporter, kube-state-metrics - can be installed with Prometheus via the [helm chart](https://github.com/prometheus-community/helm-charts/tree/main/charts/prometheus). Managed Kubernetes clusters such as AKS can have them enabled [automatically](https://learn.microsoft.com/en-us/azure/azure-monitor/essentials/prometheus-metrics-scrape-default). ### Features - List of cluster nodes and their resources: CPU, memory, filesystem.  - List of the pods restarted in the selected timeframe (hover over the dots to see the exact time of the restart). - Counts of pods in each state.  - Resource outline for each pod. Includes requests, limits and current usage stats. These metrics and the ability to filter by namespace can help calculate appropriate resource quotas for your cluster. Click on the magnifying glass to filter the dashboard by namespace, node or pod.  - Pod CPU and memory usage over time. 

nodeExporter

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2022-11-08 16:52:45 UTC

2022-11-08 16:58:19 UTC

rabbitmq-stream

RabbitMQ-Stream

Stream protocol message rates and errors

2022-12-06 13:20:28 UTC

2022-12-06 13:20:38 UTC

Stream protocol message rates and errors

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}, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(242, 204, 12, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?2(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(50, 116, 217, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?3(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(163, 82, 204, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?4(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 120, 10, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?5(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(150, 217, 141, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?6(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 238, 82, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?7(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(138, 184, 255, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?8(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(202, 149, 229, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?9(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 179, 87, 1)", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 9, "w": 12, "x": 0, "y": 19 }, "id": 7, "options": { "legend": { "calcs": [ "mean", "max", "min", "last" ], "displayMode": "table", "placement": "bottom" }, "tooltip": { "mode": "single" } }, "pluginVersion": "8.0.3", "targets": [ { "panelId": 17, "refId": "A" } ], "timeFrom": null, "timeShift": null, "title": "Stream messages received / s", "type": "timeseries" }, { "datasource": "-- Dashboard --", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 20, "gradientMode": "opacity", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "smooth", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": true, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "short" }, "overrides": [ { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?0(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(86, 166, 75, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?1(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(242, 204, 12, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?2(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(50, 116, 217, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?3(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(163, 82, 204, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?4(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 120, 10, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?5(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(150, 217, 141, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?6(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 238, 82, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?7(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(138, 184, 255, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?8(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(202, 149, 229, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?9(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 179, 87, 1)", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 9, "w": 12, "x": 12, "y": 19 }, "id": 9, "options": { "legend": { "calcs": [ "mean", "max", "min", "last" ], "displayMode": "table", "placement": "bottom" }, "tooltip": { "mode": "single" } }, "pluginVersion": "8.0.3", "targets": [ { "panelId": 20, "refId": "A" } ], "timeFrom": null, "timeShift": null, "title": "Stream messages delivered / s", "type": "timeseries" }, { "datasource": "-- Dashboard --", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 20, "gradientMode": "opacity", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "smooth", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": true, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "short" }, "overrides": [ { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?0(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(86, 166, 75, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?1(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(242, 204, 12, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?2(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(50, 116, 217, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?3(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(163, 82, 204, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?4(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 120, 10, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?5(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(150, 217, 141, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?6(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 238, 82, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?7(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(138, 184, 255, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?8(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(202, 149, 229, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?9(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 179, 87, 1)", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 9, "w": 12, "x": 0, "y": 28 }, "id": 11, "options": { "legend": { "calcs": [ "mean", "max", "min", "last" ], "displayMode": "table", "placement": "bottom" }, "tooltip": { "mode": "single" } }, "pluginVersion": "8.0.3", "targets": [ { "panelId": 18, "refId": "A" } ], "timeFrom": null, "timeShift": null, "title": "Stream messages confirmed to publishers / s", "type": "timeseries" }, { "datasource": "${DS_PROMETHEUS}", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 20, "gradientMode": "opacity", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "stepBefore", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": true, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "dark-red", "value": null } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 13, "w": 24, "x": 0, "y": 37 }, "id": 23, "options": { "legend": { "calcs": [ "last" ], "displayMode": "table", "placement": "right" }, "tooltip": { "mode": "single" } }, "pluginVersion": "8.0.3", "targets": [ { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_access_refused_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "interval": "", "legendFormat": "access_refused", "refId": "A" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_authentication_failure_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "error_authentication_failure", "refId": "B" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_frame_too_large_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "frame_too_large", "refId": "C" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_internal_error_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "internal_error", "refId": "D" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_precondition_failed_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "precondition_failed", "refId": "E" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_publisher_does_not_exist_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "publisher_does_not_exist", "refId": "F" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_sasl_authentication_failure_loopback_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "sasl_authentication_failure_loopback", "refId": "G" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_sasl_challenge_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "sasl_challenge", "refId": "H" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_sasl_error_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "sasl_error", "refId": "I" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_sasl_mechanism_not_supported_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "sasl_mechanism_not_supported", "refId": "J" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_stream_already_exists_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "stream_already_exists", "refId": "K" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_stream_does_not_exist_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "stream_does_not_exist", "refId": "L" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_stream_not_available_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "stream_not_available", "refId": "M" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_subscription_id_already_exists_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "subscription_id_already_exists", "refId": "N" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_subscription_id_does_not_exist_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "subscription_id_does_not_exist", "refId": "O" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_unknown_frame_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "error_unknown_frame", "refId": "P" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_vhost_access_failure_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "vhost_access_failure", "refId": "Q" } ], "timeFrom": null, "timeShift": null, "title": "Errors since boot", "type": "timeseries" } ], "refresh": "15s", "schemaVersion": 30, "style": "dark", "tags": [ "rabbitmq-stream", "rabbitmq-prometheus" ], "templating": { "list": [ { "current": { "selected": false, "text": "default", "value": "default" }, "datasource": "${DS_PROMETHEUS}", "description": null, "error": null, "hide": 2, "includeAll": false, "label": "datasource", "multi": false, "name": "DS_PROMETHEUS", "options": [], "query": "prometheus", "refresh": 1, "regex": "", "skipUrlSync": false, "type": "datasource" }, { "allValue": null, "current": {}, "datasource": "${DS_PROMETHEUS}", "definition": "label_values(rabbitmq_identity_info, namespace)", "description": null, "error": null, "hide": 0, "includeAll": false, "label": "Namespace", "multi": false, "name": "namespace", "options": [], "query": { "query": "label_values(rabbitmq_identity_info, namespace)", "refId": "StandardVariableQuery" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "allValue": null, "current": {}, "datasource": "${DS_PROMETHEUS}", "definition": "label_values(rabbitmq_identity_info{namespace=\"$namespace\"}, rabbitmq_cluster)", "description": null, "error": null, "hide": 0, "includeAll": false, "label": "RabbitMQ Cluster", "multi": false, "name": "rabbitmq_cluster", "options": [], "query": { "query": "label_values(rabbitmq_identity_info{namespace=\"$namespace\"}, rabbitmq_cluster)", "refId": "StandardVariableQuery" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": { "refresh_intervals": [ "15s", "30s", "1m", "5m", "10m" ] }, "timezone": "", "title": "RabbitMQ-Stream", "uid": "j7MCpqZ7k", "version": 1 }

2022-12-06 13:20:28 UTC

django-requests-by-view

Django / Requests / By View

A dashboard that monitors Django which focuses on breaking down requests by view. It is created using the [Django-mixin](https://github.com/adinhodovic/django-mixin).

2022-12-15 00:37:21 UTC

2022-12-15 00:43:11 UTC

A dashboard that monitors Django which focuses on breaking down requests by view. It is created using the [Django-mixin](https://github.com/adinhodovic/django-mixin).

A dashboard that monitors Django which focuses on breaking down requests by view. It is created using the [Django-mixin](https://github.com/adinhodovic/django-mixin).

{ "__inputs": [], "__requires": [], "annotations": { "list": [] }, "description": "A dashboard that monitors Django which focuses on breaking down requests by view. 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"pluginVersion": "7", "targets": [ { "expr": "sum(\n rate(\n django_http_responses_total_by_status_view_method_total{namespace=~\"$namespace\", job=~\"$job\", view=\"$view\", method=~\"$method\", status!~\"[4-5].*\"}[1w]\n )\n) /\nsum(\n rate(\n django_http_responses_total_by_status_view_method_total{namespace=~\"$namespace\", job=~\"$job\", view=\"$view\", method=~\"$method\"}[1w]\n )\n)\n", "format": "time_series", "intervalFactor": 2, "legendFormat": "", "refId": "A" } ], "title": "Success Rate (non 4xx-5xx responses) [1w]", "transparent": false, "type": "stat" }, { "datasource": "$datasource", "fieldConfig": { "defaults": { "links": [], "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": 0.1 }, { "color": "yellow", "value": 10 }, { "color": "red", "value": 100 } ] }, "unit": "none" } }, "gridPos": { "h": 4, "w": 6, "x": 6, "y": 1 }, "id": 4, "links": [], "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", 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2022-12-15 00:37:21 UTC

postgresql-aws-rds-database-overview

PostgreSQL AWS RDS Database Overview

Dashboard for PostgreSQL AWS RDS

2023-04-14 01:47:40 UTC

2023-04-14 02:12:25 UTC

This dashboard based on prometheus-postgres-exporter and prometheus-cloudwatch-exporter for monitoring PostgreSQL AWS RDS

Other

This dashboard works with prometheus postgres-exporte and cloudwatch exporter

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"refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "", "hide": 0, "includeAll": true, "label": "Lock table", "multi": true, "name": "mode", "options": [], "query": { "query": "label_values({mode=~\"accessexclusivelock|accesssharelock|exclusivelock|rowexclusivelock|rowsharelock|sharelock|sharerowexclusivelock|shareupdateexclusivelock\"}, mode)", "refId": "Prometheus-mode-Variable-Query" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "PostgreSQL AWS RDS Database 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2023-04-14 01:47:40 UTC

2023-04-14 01:54:15 UTC

celestia

CELESTIA

Get a start with this basic Celestia dashboard

2023-05-12 09:44:04 UTC

2023-05-12 09:45:18 UTC

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}, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 16 }, "id": 12, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "tendermint_consensus_validator_power", "legendFormat": "{{job}}", "range": true, "refId": "A" } ], "title": "VOTING POWER", "type": "timeseries" } ], "schemaVersion": 36, "style": "dark", "tags": [], "templating": { "list": [] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "CELESTIA", "uid": "d6HzKOU4z", "version": 3, "weekStart": "", "gnetId": 18723, 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2023-05-12 09:44:05 UTC

2023-05-12 09:45:16 UTC

sonarqube-system-dashboard

Sonarqube - System Dashboard

The Sonarqube - System Dashboard dashboard presents all metrics in detail of Sonarqube instance.

2022-12-17 12:40:40 UTC

2022-12-17 12:53:54 UTC

## Quick start with `docker` Run command below (edit sonarqube_url and token): ``` docker run -d \ -p8198:8198 \ -eSONARQUBE_SERVER='http://192.168.3.101:9001' \ -eSONARQUBE_TOKEN='squ_af1e521e19aef5c5de1cb6df89adf3cbb3a9759e' \ return200/sonarqube-exporter:latest ``` Access `localhost:8198` 

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2022-12-17 12:40:40 UTC

2022-12-17 12:53:50 UTC

erlang-distribution

Erlang-Distribution

Erlang Distribution links, inet socket, port driver, dist process + tls_connection & tls_sender

2023-01-19 15:26:08 UTC

2023-01-19 15:36:05 UTC

Erlang Distribution links, inet socket, port driver, dist process + tls_connection & tls_sender

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"seriesOverrides": [ { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?0(\\b|\\.)/", "color": "#56A64B" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?1(\\b|\\.)/", "color": "#F2CC0C" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?2(\\b|\\.)/", "color": "#3274D9" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?3(\\b|\\.)/", "color": "#A352CC" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?4(\\b|\\.)/", "color": "#FF780A" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?5(\\b|\\.)/", "color": "#96D98D" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?6(\\b|\\.)/", "color": "#FFEE52" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?7(\\b|\\.)/", "color": "#8AB8FF" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?8(\\b|\\.)/", "color": "#CA95E5" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?9(\\b|\\.)/", "color": "#FFB357" } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "(rate(erlang_vm_dist_recv_bytes[60s]) * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", 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25 }, "id": 11, "panels": [], "title": "port driver", "type": "row" }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "The total number of bytes allocated for this port by the runtime system. 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"/^rabbit@[a-zA-Z\\.\\-]*?6(\\b|\\.)/", "color": "#FFEE52" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?7(\\b|\\.)/", "color": "#8AB8FF" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?8(\\b|\\.)/", "color": "#CA95E5" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?9(\\b|\\.)/", "color": "#FFB357" } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "erlang_vm_dist_port_memory_bytes * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"}", "format": "time_series", "intervalFactor": 1, "legendFormat": "{{rabbitmq_node}} -> {{peer}}", "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Memory used by the port driver", "tooltip": { "shared": true, "sort": 2, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "decimals": 0, "format": 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"spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "erlang_vm_dist_port_queue_size_bytes * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"}", "format": "time_series", "intervalFactor": 1, "legendFormat": "{{rabbitmq_node}} -> {{peer}}", "refId": "A" } ], "thresholds": [ { "colorMode": "critical", "fill": true, "line": true, "op": "gt", "value": 0, "yaxis": "left" } ], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Data in the port driver buffer", "tooltip": { "shared": true, "sort": 2, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "decimals": 0, "format": "decbytes", "label": null, "logBase": 1, "max": null, "min": "0", "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "collapsed": false, "datasource": "${DS_PROMETHEUS}", "gridPos": { "h": 1, "w": 24, "x": 0, "y": 31 }, "id": 14, "panels": [], "repeat": "erlang_vm_dist_proc_type", "title": "$erlang_vm_dist_proc_type process", "type": "row" }, { "cacheTimeout": null, "cards": { "cardHSpacing": 2, "cardMinWidth": 5, "cardRound": null, "cardVSpacing": 2 }, "color": { "cardColor": "#b4ff00", "colorScale": "sqrt", "colorScheme": "interpolateGnYlRd", "defaultColor": "#757575", "exponent": 0.5, "mode": "discrete", "thresholds": [ { "color": "#37872D", "tooltip": "waiting", "value": "6" }, { "color": "#96D98D", "tooltip": "running", "value": "5" }, { "color": "#1F60C4", "tooltip": "garbage_collecting", "value": "4" }, { "color": "#FADE2A", "tooltip": "runnable", "value": "3" }, { "color": "#FA6400", "tooltip": "suspended", "value": "2" }, { "color": "#C4162A", "tooltip": "exiting", "value": "1" } ] }, "data": { "decimals": null, "unitFormat": "short" }, "datasource": "${DS_PROMETHEUS}", "description": "", "gridPos": { "h": 5, "w": 12, "x": 0, "y": 32 }, "highlightCards": true, "id": 18, "legend": { "show": true }, "links": [], "nullPointMode": "as empty", "targets": [ { "aggregation": "Last", "decimals": 2, "displayAliasType": "Warning / Critical", "displayType": "Regular", "displayValueWithAlias": "Never", "expr": "erlang_vm_dist_proc_status{type=\"$erlang_vm_dist_proc_type\"} * on(instance) group_left(rabbitmq_node, rabbitmq_cluster) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"} ", "format": "time_series", "intervalFactor": 1, "legendFormat": "{{rabbitmq_node}} -> {{peer}}", "refId": "A", "units": "none", "valueHandler": "Number Threshold" } ], "timeFrom": null, "timeShift": null, "title": "Process state", "tooltip": { "show": true }, "type": "flant-statusmap-panel", "useMax": true, "xAxis": { "labelFormat": "%a %m/%d", "minBucketWidthToShowWeekends": 4, "show": true, "showCrosshair": true, "showWeekends": true }, "yAxis": { "show": true, "showCrosshair": false }, "yAxisSort": "metrics" }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "The number of messages currently in the message queue of the process\n\nAny values above 0 hint to an overloaded distribution", "fill": 0, "fillGradient": 0, "gridPos": { "h": 5, "w": 12, "x": 12, "y": 32 }, "hiddenSeries": false, "id": 16, "legend": { "alignAsTable": true, "avg": false, "current": true, "max": true, "min": true, "show": false, "total": false, "values": true }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "dataLinks": [] }, "percentage": false, "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/rabbit/", "color": "#C4162A" } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "erlang_vm_dist_proc_message_queue_len{type=\"$erlang_vm_dist_proc_type\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"}", "format": "time_series", "intervalFactor": 1, "legendFormat": "{{rabbitmq_node}} -> {{peer}}", "refId": "A" } ], "thresholds": [ { "colorMode": "critical", "fill": true, "line": true, "op": "gt", "value": 0, "yaxis": "left" } ], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Messages in the process queue", "tooltip": { "shared": true, "sort": 2, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "decimals": 0, "format": "short", "label": null, "logBase": 1, "max": null, "min": "0", "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "The size in bytes of process memory. This includes call stack, heap, and internal structures.", "fill": 0, "fillGradient": 0, "gridPos": { "h": 5, "w": 12, "x": 0, "y": 37 }, "hiddenSeries": false, "id": 15, "legend": { "alignAsTable": true, "avg": false, "current": true, "max": true, "min": true, "show": false, "total": false, "values": true }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "dataLinks": [] }, "percentage": false, "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?0(\\b|\\.)/", "color": "#56A64B" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?1(\\b|\\.)/", "color": "#F2CC0C" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?2(\\b|\\.)/", "color": "#3274D9" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?3(\\b|\\.)/", "color": "#A352CC" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?4(\\b|\\.)/", "color": "#FF780A" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?5(\\b|\\.)/", "color": "#96D98D" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?6(\\b|\\.)/", "color": "#FFEE52" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?7(\\b|\\.)/", "color": "#8AB8FF" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?8(\\b|\\.)/", "color": "#CA95E5" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?9(\\b|\\.)/", "color": "#FFB357" } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "erlang_vm_dist_proc_memory_bytes{type=\"$erlang_vm_dist_proc_type\"} * on(instance) group_left(rabbitmq_node, rabbitmq_cluster) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"}", "format": "time_series", "intervalFactor": 1, "legendFormat": "{{rabbitmq_node}} -> {{peer}}", "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Memory used by the process", "tooltip": { "shared": true, "sort": 2, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "decimals": 0, "format": "decbytes", 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2023-01-19 15:26:08 UTC

2023-01-19 15:36:05 UTC

strategies

strategies

strategies

2023-01-23 04:13:15 UTC

2023-01-23 04:14:46 UTC

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"editorMode": "code", "format": "table", "hide": false, "rawQuery": true, "rawSql": "select a.timestamp AS \"time\", da00_long, da00_short, da01_long, da01_short from (select timestamp, [long] as da00_long, [short] as da00_short from crypto_db.dbo.orders where account = 'da00') as a join (select timestamp, [long] as da01_long, [short] as da01_short from crypto_db.dbo.orders where account = 'da01') as b on a.timestamp =b.timestamp ORDER BY a.timestamp\r\n", "refId": "inv_pct", "sql": { "columns": [ { "alias": "\"time\"", "parameters": [ { "name": "timestamp", "type": "functionParameter" } ], "type": "function" }, { "alias": "\"value\"", "parameters": [ { "name": "balance", "type": "functionParameter" } ], "type": "function" } ], "groupBy": [ { "property": { "type": "string" }, "type": "groupBy" } ], "limit": 50 }, "table": "dbo.bn_balance" } ], "title": "Orders", "transparent": true, "type": "timeseries" }, { "datasource": { "type": "mssql", "uid": "${DS_MICROSOFT_SQL SERVER}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "series", "axisGridShow": true, "axisLabel": "USDT", "axisPlacement": "auto", "axisWidth": 50, "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "decimals": 0, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "none" }, "overrides": [ { "matcher": { "id": "byName", "options": "value" }, "properties": [ { "id": "color", "value": { "fixedColor": "super-light-red", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "pirce" }, "properties": [ { "id": "color", "value": { "fixedColor": "yellow", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "price" }, "properties": [ { "id": "color", "value": { "fixedColor": "yellow", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 6, "w": 24, "x": 0, "y": 24 }, "id": 41, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "pluginVersion": "9.3.1", "targets": [ { "dataset": "crypto_db", "datasource": { "type": "mssql", "uid": "${DS_MICROSOFT_SQL SERVER}" }, "editorMode": "code", "format": "table", "hide": false, "rawQuery": true, "rawSql": "SELECT timestamp AS \"time\", price AS \"price\" FROM crypto_db.dbo.prices ORDER BY timestamp", "refId": "price", "sql": { "columns": [ { "alias": "\"time\"", "parameters": [ { "name": "timestamp", "type": "functionParameter" } ], "type": "function" }, { "alias": "\"value\"", "parameters": [ { "name": "balance", "type": "functionParameter" } ], "type": "function" } ], "groupBy": [ { "property": { "type": "string" }, "type": "groupBy" } ], "limit": 50 }, "table": "dbo.bn_balance" } ], "title": "BTC Price", "transparent": true, "type": "timeseries" } ], "refresh": false, "schemaVersion": 37, "style": "dark", "tags": [], "templating": { "list": [] }, "time": { "from": "now-2d", "to": "now" }, "timepicker": {}, "timezone": "", "title": "strategies", "uid": "9M211ejMk", "version": 41, "weekStart": "", "gnetId": 17932, "description": "strategies" }

2023-01-23 04:13:15 UTC

2023-01-23 04:14:46 UTC

ping

PingExporter

A simple dashboard for https://github.com/czerwonk/ping_exporter

2023-02-28 09:32:53 UTC

2023-02-28 09:34:23 UTC

A simple Dashboard for https://github.com/czerwonk/ping_exporter

Other

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{ "alias": "IPv6 mean", "color": "#5794F2" }, { "alias": "IPv6 best", "color": "#5794F2", "lines": false }, { "alias": "IPv4 loss", "color": "#DEB6F2", "fill": 2, "linewidth": 0, "yaxis": 2 }, { "alias": "IPv6 loss", "color": "#C0D8FF", "fill": 2, "linewidth": 0, "yaxis": 2 } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "uid": "$DS_PROMETHEUS" }, "expr": "avg(ping_rtt_best_ms{job=~\"$job\",instance=~\"$instance\",target=~\"$target\",ip_version=~\"$ip_version\"}) by (ip_version)", "format": "time_series", "instant": false, "intervalFactor": 1, "legendFormat": "IPv{{ip_version}} best", "metric": "probe_", "refId": "A", "step": 2 }, { "datasource": { "uid": "$DS_PROMETHEUS" }, "expr": "avg(ping_rtt_mean_ms{job=~\"$job\",instance=~\"$instance\",target=~\"$target\",ip_version=~\"$ip_version\"}) by (ip_version)", "format": "time_series", "instant": false, "intervalFactor": 1, "legendFormat": "IPv{{ip_version}} mean", "metric": "probe_", "refId": 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"max": "1", "min": "0", "show": true } ], "yaxis": { "align": false } } ], "repeat": "instance", "targets": [ { "datasource": { "type": "prometheus", "uid": "000000001" }, "refId": "A" } ], "title": "Instance $instance", "type": "row" } ], "refresh": "10s", "schemaVersion": 37, "style": "dark", "tags": [], "templating": { "list": [ { "current": { "selected": false, "text": "PromShort", "value": "PromShort" }, "hide": 0, "includeAll": false, "multi": false, "name": "DS_PROMETHEUS", "options": [], "query": "prometheus", "queryValue": "", "refresh": 1, "regex": "", "skipUrlSync": false, "type": "datasource" }, { "allFormat": "glob", "allValue": "", "current": {}, "datasource": { "type": "prometheus", "uid": "$DS_PROMETHEUS" }, "definition": "label_values(ping_loss_percent,job)", "hide": 0, "includeAll": true, "label": "Job", "multi": true, "multiFormat": "glob", "name": "job", "options": [], "query": { "query": "label_values(ping_loss_percent,job)", "refId": "PromShort-job-Variable-Query" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "allFormat": "glob", "allValue": "", "current": {}, "datasource": { "type": "prometheus", "uid": "$DS_PROMETHEUS" }, "definition": "label_values(ping_loss_percent{job=~\"$job\"},instance)", "hide": 0, "includeAll": true, "label": "Instance", "multi": true, "multiFormat": "glob", "name": "instance", "options": [], "query": { "query": "label_values(ping_loss_percent{job=~\"$job\"},instance)", "refId": "PromShort-instance-Variable-Query" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "allFormat": "glob", "allValue": "", "current": {}, "datasource": { "type": "prometheus", "uid": "$DS_PROMETHEUS" }, "definition": "label_values(ping_loss_percent{job=~\"$job\",instance=~\"$instance\"},target)", "hide": 0, "includeAll": true, "label": "Target", "multi": true, "multiFormat": "glob", "name": "target", "options": [], "query": { "query": "label_values(ping_loss_percent{job=~\"$job\",instance=~\"$instance\"},target)", "refId": "PromShort-target-Variable-Query" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "allFormat": "glob", "allValue": "", "current": {}, "datasource": { "type": "prometheus", "uid": "$DS_PROMETHEUS" }, "definition": "label_values(ping_loss_percent{job=~\"$job\",instance=~\"$instance\"},ip_version)", "hide": 0, "includeAll": true, "label": "IP Version", "multi": true, "multiFormat": "glob", "name": "ip_version", "options": [], "query": { "query": "label_values(ping_loss_percent{job=~\"$job\",instance=~\"$instance\"},ip_version)", "refId": "PromShort-ip_version-Variable-Query" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-15m", "to": "now" }, "timepicker": { "now": true, "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "browser", "title": "PingExporter", "uid": "pv02xrZWz", "version": 7, "weekStart": "", "gnetId": 18194, "description": "A simple dashboard for https://github.com/czerwonk/ping_exporter" }

2023-02-28 09:32:53 UTC

2023-02-28 09:34:21 UTC

1-node-exporter-dashboard-22-04-17

1 Node Exporter Dashboard 22/04/17 项目分组版

使用Grafana8新表格重建，新增健康评分概念，并新增了整体资源消耗信息的一些图表。优化重要指标展示，包含整体资源展示与资源明细图表：CPU 内存 磁盘 IO 网络等监控指标。https://github.com/starsliao/ConsulManager

2023-03-08 05:46:38 UTC

2023-03-08 05:47:48 UTC

1

使用Grafana8新表格重建，新增健康评分概念，并新增了整体资源消耗信息的一些图表。优化重要指标展示，包含整体资源展示与资源明细图表：CPU 内存 磁盘 IO 网络等监控指标。https://github.com/starsliao/ConsulManager

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"custom.displayMode", "value": "color-background" }, { "id": "thresholds", "value": { "mode": "absolute", "steps": [ { "color": "rgba(50, 172, 45, 0.97)", "value": null }, { "color": "rgba(237, 129, 40, 0.89)", "value": 30485760 }, { "color": "rgba(245, 54, 54, 0.9)", "value": 104857600 } ] } }, { "id": "custom.width", "value": 81 }, { "id": "decimals" } ] }, { "matcher": { "id": "byName", "options": "上传带宽*" }, "properties": [ { "id": "unit", "value": "binbps" }, { "id": "custom.displayMode", "value": "color-background" }, { "id": "thresholds", "value": { "mode": "absolute", "steps": [ { "color": "rgba(50, 172, 45, 0.97)", "value": null }, { "color": "rgba(237, 129, 40, 0.89)", "value": 30485760 }, { "color": "rgba(245, 54, 54, 0.9)", "value": 104857600 } ] } }, { "id": "custom.width", "value": 85 }, { "id": "decimals" } ] }, { "matcher": { "id": "byName", "options": "负载" }, "properties": [ { "id": "decimals", "value": 2 }, { "id": "custom.width", "value": 50 } ] }, { "matcher": { 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"custom.displayMode", "value": "color-text" }, { "id": "color", "value": { "fixedColor": "blue", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "IP" }, "properties": [ { "id": "custom.width", "value": 115 }, { "id": "custom.filterable", "value": true } ] }, { "matcher": { "id": "byName", "options": "主机名" }, "properties": [ { "id": "custom.filterable", "value": true }, { "id": "custom.width" } ] }, { "matcher": { "id": "byName", "options": "健康值" }, "properties": [ { "id": "custom.width", "value": 69 }, { "id": "thresholds", "value": { "mode": "absolute", "steps": [ { "color": "red", "value": null }, { "color": "orange", "value": 80 }, { "color": "green", "value": 90 } ] } }, { "id": "color", "value": { "mode": "thresholds" } }, { "id": "custom.displayMode", "value": "color-background" } ] }, { "matcher": { "id": "byRegexp", "options": "/.*使用率.*/" }, "properties": [ { "id": "unit", "value": "percent" }, { "id": "custom.displayMode", "value": "gradient-gauge" }, { 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"datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "exemplar": false, "expr": "sum(time() - node_boot_time_seconds{origin_prometheus=~\"$origin_prometheus\",project=~\"$project\"})by(instance)/86400", "format": "table", "hide": false, "instant": true, "interval": "", "legendFormat": "运行时间", "refId": "D" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "exemplar": false, "expr": "node_memory_MemTotal_bytes{origin_prometheus=~\"$origin_prometheus\",project=~\"$project\"} - 0", "format": "table", "hide": false, "instant": true, "interval": "", "legendFormat": "总内存", "refId": "B" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "exemplar": false, "expr": "count(node_cpu_seconds_total{origin_prometheus=~\"$origin_prometheus\",project=~\"$project\",mode='system'}) by (instance)", "format": "table", "hide": false, "instant": true, "interval": "", "legendFormat": "总核数", "refId": "C" }, { "datasource": { "type": "prometheus", "uid": 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"datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "exemplar": false, "expr": "max((node_filesystem_size_bytes{origin_prometheus=~\"$origin_prometheus\",project=~\"$project\",fstype=~\"ext.?|xfs\"}-node_filesystem_free_bytes{origin_prometheus=~\"$origin_prometheus\",project=~\"$project\",fstype=~\"ext.?|xfs\"}) *100/(node_filesystem_avail_bytes {origin_prometheus=~\"$origin_prometheus\",project=~\"$project\",fstype=~\"ext.?|xfs\"}+(node_filesystem_size_bytes{origin_prometheus=~\"$origin_prometheus\",project=~\"$project\",fstype=~\"ext.?|xfs\"}-node_filesystem_free_bytes{origin_prometheus=~\"$origin_prometheus\",project=~\"$project\",fstype=~\"ext.?|xfs\"})))by(instance)", "format": "table", "hide": false, "instant": true, "interval": "", "legendFormat": "分区使用率", "refId": "E" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "exemplar": false, "expr": 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2023-03-08 05:46:38 UTC

app-metrics-web-monitoring-influxdb

App Metrics - Web Monitoring - InfluxDB

Dashboard to visualize metrics captured by App Metrics ASP.NET Core Middleware -https://github.com/alhardy/AppMetrics

2023-04-14 08:17:15 UTC

2023-04-14 08:18:28 UTC

Other

Dashboard to visualize metrics captured by App Metrics ASP.NET Core Middleware -https://github.com/alhardy/AppMetrics

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false, "text": "1h", "value": "1h" }, { "selected": false, "text": "6h", "value": "6h" }, { "selected": false, "text": "12h", "value": "12h" }, { "selected": false, "text": "1d", "value": "1d" }, { "selected": false, "text": "7d", "value": "7d" }, { "selected": false, "text": "14d", "value": "14d" }, { "selected": false, "text": "30d", "value": "30d" } ], "query": "5s,10s,30s,1m,10m,30m,1h,6h,12h,1d,7d,14d,30d", "refresh": 2, "type": "interval" } ] }, "time": { "from": "now-5m", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "browser", "title": "App Metrics - Web Monitoring - InfluxDB", "version": 0, "description": "Dashboard to visualize metrics captured by App Metrics ASP.NET Core Middleware -https://github.com/alhardy/AppMetrics" }

2023-04-14 08:17:15 UTC

website-monitoring

Website monitoring

Website monitoring

2023-05-23 07:45:25 UTC

2023-05-23 07:45:52 UTC

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"1" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "dark-orange", "value": null }, { "color": "dark-green", "value": 1 } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 3, "w": 3, "x": 21, "y": 0 }, "id": 22, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "mean" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "7.2.0", "targets": [ { "expr": "probe_success{job=\"$icmp_job\",instance=\"$target\"}", "instant": true, "interval": "", "legendFormat": "", "refId": "A" } ], "timeFrom": null, "timeShift": null, "title": "ICMP", "type": "stat" }, { "datasource": "${DS_PROMETHEUS}", "fieldConfig": { "defaults": { "custom": {}, "decimals": 3, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "dark-red", "value": null }, { "color": "dark-orange", "value": 97 }, { "color": "dark-green", "value": 99 } ] }, "unit": "percent" }, "overrides": [] }, "gridPos": { "h": 4, "w": 8, "x": 0, "y": 3 }, "id": 9, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "mean" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "7.2.0", "targets": [ { "expr": "avg_over_time(probe_success{job=\"$http_job\",instance=\"$target\"}[$__interval]) * 100", "instant": false, "interval": "", "legendFormat": "", "refId": "A" } ], "timeFrom": "1d", "timeShift": null, "title": "Availability", "type": "stat" }, { "datasource": "${DS_PROMETHEUS}", "fieldConfig": { "defaults": { "custom": {}, "decimals": 3, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "dark-red", "value": null }, { "color": "dark-orange", "value": 97 }, { "color": "dark-green", "value": 99 } ] }, "unit": "percent" }, "overrides": [] }, "gridPos": { "h": 4, "w": 8, "x": 8, "y": 3 }, "id": 15, "options": { "colorMode": "value", "graphMode": "area", 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"lines": false, "pointradius": 1, "points": true, "yaxis": 2 }, { "alias": "duration", "color": "#1F60C4", "fill": 3, "fillGradient": 3 }, { "alias": "invalid status code", "color": "#C4162A", "legend": false, "lines": false, "pointradius": 1, "points": true, "yaxis": 2 } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "probe_duration_seconds{instance=\"$target\",job=\"$http_job\"}", "hide": false, "interval": "", "legendFormat": "duration", "refId": "A" }, { "expr": "probe_http_status_code{instance=\"$target\"} == 200", "interval": "", "legendFormat": "status code", "refId": "B" }, { "expr": "probe_http_status_code{instance=\"$target\"} != 200", "hide": false, "interval": "", "legendFormat": "invalid status code", "refId": "C" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "HTTP probe duration / status code", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "s", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": "status code", "logBase": 1, "max": "550", "min": "0", "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "Display only ICMP failures", "fieldConfig": { "defaults": { "custom": {} }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 9, "w": 12, "x": 12, "y": 7 }, "hiddenSeries": false, "id": 6, "legend": { "avg": true, "current": false, "max": false, "min": false, "show": true, "total": false, "values": true }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "7.2.0", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "failure", "color": "#FA6400", "legend": false, "lines": false, "pointradius": 1, "points": true, "yaxis": 2 }, { "alias": "duration", "color": "#1F60C4", "fill": 3, "fillGradient": 3 } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "probe_duration_seconds{instance=\"$target\",job=\"$icmp_job\"}", "hide": false, "interval": "", "legendFormat": "duration", "refId": "A" }, { "expr": "probe_success{job=\"$icmp_job\",instance=\"$target\"} == 0", "hide": false, "interval": "", "legendFormat": "failure", "refId": "B" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "ICMP probe duration / failure", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "s", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": "failure", "logBase": 1, "max": null, "min": null, "show": false } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": true, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "fieldConfig": { "defaults": { "custom": {} }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 9, "w": 12, "x": 0, "y": 16 }, "hiddenSeries": false, "id": 2, "legend": { "alignAsTable": true, "avg": true, "current": false, "max": false, "min": false, "rightSide": true, "show": true, "total": false, "values": true }, "lines": false, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "7.2.0", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": true, "steppedLine": false, "targets": [ { "expr": "probe_http_duration_seconds{instance=\"$target\"}", "interval": "", "legendFormat": "{{ phase }}", "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "HTTP probe (by phase)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "s", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": true, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "fieldConfig": { "defaults": { "custom": {} }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 9, "w": 12, "x": 12, "y": 16 }, "hiddenSeries": false, "id": 3, "legend": { "alignAsTable": true, "avg": true, "current": false, "max": false, "min": false, "rightSide": true, "show": true, "total": false, "values": true }, "lines": false, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "7.2.0", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": true, "steppedLine": false, "targets": [ { "expr": "probe_icmp_duration_seconds{instance=\"$target\"}", "interval": "", "legendFormat": "{{ phase }}", "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "ICMP probe (by phase)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "s", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } } ], "refresh": false, "schemaVersion": 26, "style": "dark", "tags": [], "templating": { "list": [ { "allValue": null, "current": {}, "datasource": "${DS_PROMETHEUS}", "definition": "label_values(instance)", "hide": 0, "includeAll": false, "label": "target", "multi": false, "name": "target", "options": [], "query": "label_values(instance)", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false }, { "current": { "value": "${VAR_HTTP_JOB}", "text": "${VAR_HTTP_JOB}", "selected": false }, "hide": 2, "label": "http_job", "name": "http_job", "options": [ { "value": "${VAR_HTTP_JOB}", "text": "${VAR_HTTP_JOB}", "selected": false } ], "query": "${VAR_HTTP_JOB}", "skipUrlSync": false, "type": "constant" }, { "current": { "value": "${VAR_ICMP_JOB}", "text": "${VAR_ICMP_JOB}", "selected": false }, "hide": 2, "label": "icmp_job", "name": "icmp_job", "options": [ { "value": "${VAR_ICMP_JOB}", "text": "${VAR_ICMP_JOB}", "selected": false } ], "query": "${VAR_ICMP_JOB}", "skipUrlSync": false, "type": "constant" } ] }, "time": { "from": "now-30m", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Website monitoring", "uid": "EmUBHUFGk", "version": 1, "description": "Website monitoring" }

2023-05-23 07:45:25 UTC

2023-05-23 07:45:53 UTC

akamai-siem

Akamai SIEM

Akamai SIEM powered by Hydrolix dashboard for security analytics

2023-05-24 13:48:02 UTC

2023-05-24 13:49:15 UTC

Hydrolix dashboard for Akamai security events connector monitoring

{ "__inputs": [ { "name": "DS_HYDRO", "label": "Hydrolix Cluster", "description": "", "type": "datasource", "pluginId": "grafana-clickhouse-datasource", "pluginName": "ClickHouse" }, { "name": "VAR_TABLE", "type": "constant", "label": "table", "value": "", "description": "" } ], "__elements": {}, "__requires": [ { "type": "panel", "id": "geomap", "name": "Geomap", "version": "" }, { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "9.5.1" }, { "type": "datasource", "id": "grafana-clickhouse-datasource", "name": "ClickHouse", "version": "3.0.1-beta" }, { "type": "panel", "id": "logs", "name": "Logs", "version": "" }, { "type": "panel", "id": "table", "name": "Table", "version": "" }, { "type": "panel", "id": "timeseries", "name": "Time series", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": { "type": "grafana", "uid": "-- Grafana --" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "type": 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"options": { "legend": { "calcs": [ "sum", "max" ], "displayMode": "table", "placement": "bottom", "showLegend": true, "sortBy": "Total", "sortDesc": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.5.1", "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "expand": true, "format": 0, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT arrayJoin(ruleActions) as ruleAction,\ncount(),\n$__timeInterval(timestamp) as time\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(host IN (${host}), $host)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(clientIP IN (${clientIP}), $clientIP)\nAND $__conditionalAll(path IN (${path}), $path)\nAND $__conditionalAll(hasAll(ruleMessages, arrayPopBack([${message}])), $message)\nAND $__conditionalAll(hasAll(ruleTags, arrayPopBack([${tags}])), $tags)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\n\n\nGROUP BY time, ruleAction\nORDER BY time", "refId": "A", "selectedFormat": 0 } ], "title": "SIEM Logs Count", "type": "timeseries" }, { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 0 }, "id": 2, "options": { "legend": { "calcs": [ "max", "sum" ], "displayMode": "table", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.5.1", "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "expand": true, "format": 0, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT toString(status) as status_code, count(),\n$__timeInterval(timestamp) as time\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(host IN (${host}), $host)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(clientIP IN (${clientIP}), $clientIP)\nAND $__conditionalAll(path IN (${path}), $path)\nAND $__conditionalAll(hasAll(ruleMessages, arrayPopBack([${message}])), $message)\nAND $__conditionalAll(hasAll(ruleTags, arrayPopBack([${tags}])), $tags)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\nGROUP BY time, status_code\nORDER BY time", "refId": "A", "selectedFormat": 0 } ], "title": "SIEM Logs per Status Code", "type": "timeseries" }, { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "cellOptions": { "type": "auto" }, "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "count()" }, "properties": [ { "id": "custom.width", "value": 82 } ] }, { "matcher": { "id": "byName", "options": "message" }, "properties": [ { "id": "custom.width", "value": 456 } ] }, { "matcher": { "id": "byName", "options": "message" }, "properties": [ { "id": "links", "value": [ { "title": "Filter", "url": "/d/bd475a0d-b9b0-4577-8043-8a63aeb25e5e/akamai-siem?&var-message=${__data.fields[1]}&${__all_variables}&${__url_time_range}" } ] } ] }, { "matcher": { "id": "byName", "options": "exclude" }, "properties": [ { "id": "custom.width", "value": 71 } ] } ] }, "gridPos": { "h": 11, "w": 4, "x": 0, "y": 8 }, "id": 5, "options": { "cellHeight": "sm", "footer": { "countRows": false, "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [] }, "pluginVersion": "9.5.1", "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "expand": true, "format": 0, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT count(),\narrayJoin(ruleMessages) AS message\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(host IN (${host}), $host)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(clientIP IN (${clientIP}), $clientIP)\nAND $__conditionalAll(path IN (${path}), $path)\nAND $__conditionalAll(hasAll(ruleTags, arrayPopBack([${tags}])), $tags)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\nGROUP BY message\nORDER BY count() DESC\nLIMIT 100", "refId": "A", "selectedFormat": 0 } ], "title": "Top 100 Message", "type": "table" }, { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "cellOptions": { "type": "auto" }, "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "count()" }, "properties": [ { "id": "custom.width", "value": 82 } ] }, { "matcher": { "id": "byName", "options": "tags" }, "properties": [ { "id": "custom.width", "value": 456 }, { "id": "links", "value": [ { "title": "Filter", "url": "/d/bd475a0d-b9b0-4577-8043-8a63aeb25e5e/akamai-siem?var-tags=${__data.fields[1]}&${__all_variables}&${__url_time_range}" } ] } ] } ] }, "gridPos": { "h": 11, "w": 5, "x": 4, "y": 8 }, "id": 8, "options": { "cellHeight": "sm", "footer": { "countRows": false, "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [] }, "pluginVersion": "9.5.1", "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "expand": true, "format": 0, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT count(),\narrayJoin(ruleTags) AS tags\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(host IN (${host}), $host)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(clientIP IN (${clientIP}), $clientIP)\nAND $__conditionalAll(path IN (${path}), $path)\nAND $__conditionalAll(hasAll(ruleMessages, arrayPopBack([${message}])), $message)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\nGROUP BY tags\nORDER BY count() DESC\nLIMIT 100", "refId": "A", "selectedFormat": 0 } ], "title": "Top 100 Tags", "type": "table" }, { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "cellOptions": { "type": "auto" }, "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "count()" }, "properties": [ { "id": "custom.width", "value": 82 } ] }, { "matcher": { "id": "byName", "options": "path" }, "properties": [ { "id": "custom.width", "value": 731 }, { "id": "links", "value": [ { "title": "Filter", "url": "/d/bd475a0d-b9b0-4577-8043-8a63aeb25e5e/akamai-siem?var-path=${__data.fields[1]}&${__all_variables}&${__url_time_range}" } ] } ] } ] }, "gridPos": { "h": 11, "w": 6, "x": 9, "y": 8 }, "id": 11, "options": { "cellHeight": "sm", "footer": { "countRows": false, "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [] }, "pluginVersion": "9.5.1", "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "expand": true, "format": 0, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT count(),\npath\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(host IN (${host}), $host)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(clientIP IN (${clientIP}), $clientIP)\nAND $__conditionalAll(hasAll(ruleMessages, arrayPopBack([${message}])), $message)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\nGROUP BY path\nORDER BY count() DESC\nLIMIT 100", "refId": "A", "selectedFormat": 0 } ], "title": "Top 100 Path", "type": "table" }, { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "cellOptions": { "type": "auto" }, "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "count()" }, "properties": [ { "id": "custom.width", "value": 82 } ] }, { "matcher": { "id": "byName", "options": "clientIP" }, "properties": [ { "id": "links", "value": [ { "title": "", "url": "/d/bd475a0d-b9b0-4577-8043-8a63aeb25e5e/akamai-siem?&var-clientIP=${__data.fields[1]}&${__all_variables}&${__url_time_range}" } ] } ] } ] }, "gridPos": { "h": 11, "w": 4, "x": 15, "y": 8 }, "id": 6, "options": { "cellHeight": "sm", "footer": { "countRows": false, "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [] }, "pluginVersion": "9.5.1", "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "expand": true, "format": 0, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT count(),\nclientIP\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(host IN (${host}), $host)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(path IN (${path}), $path)\nAND $__conditionalAll(hasAll(ruleMessages, arrayPopBack([${message}])), $message)\nAND $__conditionalAll(hasAll(ruleTags, arrayPopBack([${tags}])), $tags)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\nGROUP BY clientIP\nORDER BY count() DESC\nLIMIT 100", "refId": "A", "selectedFormat": 0 } ], "title": "Top 100 Client IP", "type": "table" }, { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "cellOptions": { "type": "auto" }, "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "count()" }, "properties": [ { "id": "custom.width", "value": 82 } ] }, { "matcher": { "id": "byName", "options": "host" }, "properties": [ { "id": "links", "value": [ { "title": "", "url": "/d/bd475a0d-b9b0-4577-8043-8a63aeb25e5e/akamai-siem?&var-host=${__data.fields[1]}&${__all_variables}&${__url_time_range}" } ] } ] } ] }, "gridPos": { "h": 11, "w": 5, "x": 19, "y": 8 }, "id": 10, "options": { "cellHeight": "sm", "footer": { "countRows": false, "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [] }, "pluginVersion": "9.5.1", "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "expand": true, "format": 0, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT count(),\nhost\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(clientIP IN (${clientIP}), $clientIP)\nAND $__conditionalAll(path IN (${path}), $path)\nAND $__conditionalAll(hasAll(ruleMessages, arrayPopBack([${message}])), $message)\nAND $__conditionalAll(hasAll(ruleTags, arrayPopBack([${tags}])), $tags)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\nGROUP BY host\nORDER BY count() DESC\nLIMIT 100", "refId": "A", "selectedFormat": 0 } ], "title": "Top 100 Host", "type": "table" }, { "collapsed": true, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 19 }, "id": 3, "panels": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "gridPos": { "h": 16, "w": 24, "x": 0, "y": 20 }, "id": 4, "options": { "dedupStrategy": "none", "enableLogDetails": true, "prettifyLogMessage": true, "showCommonLabels": false, "showLabels": false, "showTime": false, "sortOrder": "Descending", "wrapLogMessage": true }, "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "format": 2, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT ruleActions, * EXCEPT(ruleActions)\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(host IN (${host}), $host)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(clientIP IN (${clientIP}), $clientIP)\nAND $__conditionalAll(path IN (${path}), $path)\nAND $__conditionalAll(hasAll(ruleMessages, arrayPopBack([${message}])), $message)\nAND $__conditionalAll(hasAll(ruleTags, arrayPopBack([${tags}])), $tags)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\nLIMIT 100", "refId": "A", "selectedFormat": 2 } ], "title": "Raw Logs", "type": "logs" } ], "title": "Raw Logs", "type": "row" }, { "collapsed": true, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 20 }, "id": 9, "panels": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "hideFrom": { "legend": false, "tooltip": false, "viz": false } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green" } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 19, "w": 24, "x": 0, "y": 21 }, "id": 7, "options": { "basemap": { "config": {}, "name": "Layer 0", "type": "default" }, "controls": { "mouseWheelZoom": true, "showAttribution": true, "showDebug": false, "showMeasure": false, "showScale": false, "showZoom": true }, "layers": [ { "config": { "blur": 15, "radius": 10, "weight": { "fixed": 1, "max": 1, "min": 0 } }, "filterData": { "id": "byRefId", "options": "A" }, "location": { "gazetteer": "public/gazetteer/countries.json", "lookup": "country", "mode": "lookup" }, "name": "Layer 1", "opacity": 1, "tooltip": true, "type": "heatmap" } ], "tooltip": { "mode": "details" }, "view": { "allLayers": true, "id": "zero", "lat": 0, "lon": 0, "zoom": 1 } }, "pluginVersion": "9.5.1", "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "format": 1, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT country, count()\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(host IN (${host}), $host)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(clientIP IN (${clientIP}), $clientIP)\nAND $__conditionalAll(path IN (${path}), $path)\nAND $__conditionalAll(hasAll(ruleMessages, arrayPopBack([${message}])), $message)\nAND $__conditionalAll(hasAll(ruleTags, arrayPopBack([${tags}])), $tags)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\nGROUP BY country", "refId": "A", "selectedFormat": 1 } ], "title": "Geo Map", "type": "geomap" } ], "title": "Geo Map", "type": "row" } ], "refresh": "30s", "schemaVersion": 38, "style": "dark", "tags": [], "templating": { "list": [ { "hide": 2, "name": "table", "query": "${VAR_TABLE}", "skipUrlSync": false, "type": "constant", "current": { "value": "${VAR_TABLE}", "text": "${VAR_TABLE}", "selected": false }, "options": [ { "value": "${VAR_TABLE}", "text": "${VAR_TABLE}", "selected": false } ] }, { "current": {}, "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "definition": "SELECT host\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nGROUP BY host\nLIMIT 100", "hide": 0, "includeAll": true, "label": "Host", "multi": true, "name": "host", "options": [], "query": "SELECT host\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nGROUP BY host\nLIMIT 100", "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": { "selected": true, "text": "All", "value": "$__all" }, "hide": 0, "includeAll": true, "label": "Rule Action", "multi": false, "name": "ruleActions", "options": [ { "selected": true, "text": "All", "value": "$__all" }, { "selected": false, "text": "monitor", "value": "monitor" }, { "selected": false, "text": "alert", "value": "alert" }, { "selected": false, "text": "allow", "value": "allow" }, { "selected": false, "text": "tarpit", "value": "tarpit" } ], "query": "monitor, alert, allow, tarpit", "queryValue": "", "skipUrlSync": false, "type": "custom" }, { "current": {}, "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "definition": "SELECT method\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nGROUP BY method\nLIMIT 100", "hide": 0, "includeAll": true, "label": "Method", "multi": true, "name": "method", "options": [], "query": "SELECT method\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nGROUP BY method\nLIMIT 100", "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "definition": "SELECT status\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nGROUP BY status\nLIMIT 100", "hide": 0, "includeAll": true, "label": "Status Code", "multi": true, "name": "status", "options": [], "query": "SELECT status\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nGROUP BY status\nLIMIT 100", "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "definition": "SELECT country\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nGROUP BY country\nLIMIT 100", "hide": 0, "includeAll": true, "label": "Country", "multi": true, "name": "country", "options": [], "query": "SELECT country\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nGROUP BY country\nLIMIT 100", "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": { "selected": false, "text": "", "value": "" }, "hide": 0, "label": "Rule Message", "name": "message", "options": [ { "selected": true, "text": "", "value": "" } ], "query": "", "skipUrlSync": false, "type": "textbox" }, { "current": { "selected": false, "text": "", "value": "" }, "hide": 0, "label": "Rule Tags", "name": "tags", "options": [ { "selected": true, "text": "", "value": "" } ], "query": "", "skipUrlSync": false, "type": "textbox" }, { "current": { "selected": false, "text": "", "value": "" }, "hide": 0, "label": "Client IP", "name": "clientIP", "options": [ { "selected": true, "text": "", "value": "" } ], "query": "", "skipUrlSync": false, "type": "textbox" }, { "current": { "selected": false, "text": "", "value": "" }, "hide": 0, "label": "Path", "name": "path", "options": [ { "selected": true, "text": "", "value": "" } ], "query": "", "skipUrlSync": false, "type": "textbox" }, { "current": { "selected": false, "text": "", "value": "" }, "description": "For example look * request header name *", "hide": 0, "label": "requestHeaders Name", "name": "requestHeadersName", "options": [ { "selected": true, "text": "", "value": "" } ], "query": "", "skipUrlSync": false, "type": "textbox" }, { "current": { "selected": false, "text": "", "value": "" }, "description": "For example look * response header name *", "hide": 0, "label": "responseHeaders Name", "name": "responseHeadersName", "options": [ { "selected": true, "text": "", "value": "" } ], "query": "", "skipUrlSync": false, "type": "textbox" } ] }, "time": { "from": "now-24h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Akamai SIEM", "uid": "bd475a0d-b9b0-4577-8043-8a63aeb25e5e", "version": 63, "weekStart": "", "gnetId": 18823, "description": "Akamai SIEM powered by Hydrolix dashboard for security analytics\r\n" }

2023-05-24 13:48:02 UTC

2023-05-24 13:49:07 UTC

frontend

Frontend

first test for grafana faro

2023-05-25 15:46:33 UTC

2023-05-25 15:47:01 UTC

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"type": "query" } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Frontend", "version": 1, "weekStart": "", "gnetId": 18833, "description": "first test for grafana faro" }

2023-05-25 15:46:33 UTC

2023-05-25 15:47:01 UTC

changex-dashboard-latest

ChangeX DASHBOARD For Users

For Users

2022-09-12 13:33:42 UTC

2022-09-12 13:34:00 UTC

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"defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "center", "displayMode": "color-background", "filterable": false, "inspect": true, "minWidth": 50 }, "mappings": [ { "options": { "false": { "color": "red", "index": 0, "text": "DATABASE NOT CONNECTED" }, "true": { "color": "green", "index": 1, "text": "DATABASE CONNECTED" } }, "type": "value" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "#EAB839", "value": 60 }, { "color": "red", "value": 85 } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 3, "w": 6, "x": 6, "y": 1 }, "id": 16, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [ { "desc": true, "displayName": "STATUS" } ] }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "db", "text": " STATUS", "type": "string" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "json_options": { "root_is_not_array": true }, "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://txnotifications.changex.io/status/db", "url_options": { "data": "", "method": "GET" } } ], "title": "NOTIFICATIONS SERVICE DATABASE", "transformations": [ { "id": "convertFieldType", "options": { "conversions": [ { "destinationType": "boolean", "targetField": " STATUS" } ], "fields": {} } } ], "type": "table" }, { "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "mDe4CYmVz" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "center", "displayMode": "color-background", "inspect": false }, "mappings": [ { "options": { "false": { "color": "red", "index": 1, "text": "SERVICE IS PROBABLY DOWN" }, "true": { "color": "green", "index": 0, "text": "SERVICE IS RUNNING" } }, "type": "value" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 3, "w": 6, "x": 12, "y": 1 }, "id": 33, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "message", "text": " STATUS", "type": "string" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://bridge.changex.io/status/db", "url_options": { "data": "", "method": "GET" } } ], "title": "BRIDGE SERVICE", "transformations": [ { "id": "convertFieldType", "options": { "conversions": [ { "destinationType": "boolean", "targetField": " STATUS" } ], "fields": {} } } ], "type": "table" }, { "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "mDe4CYmVz" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "center", "displayMode": "color-background", "inspect": false }, "mappings": [ { "options": { "false": { "color": "red", "index": 1, "text": "SERVICE IS PROBABLY DOWN" }, "true": { "color": "green", "index": 0, "text": "SERVICE IS RUNNING" } }, "type": "value" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 3, "w": 6, "x": 18, "y": 1 }, "id": 34, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "message", "text": " STATUS", "type": "string" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://bridge.changex.io/status/ws", "url_options": { "data": "", "method": "GET" } } ], "title": "BRIDGE WEBSOCKETS", "transformations": [ { "id": "convertFieldType", "options": { "conversions": [ { "destinationType": "boolean", "targetField": " STATUS" } ], "fields": {} } } ], "type": "table" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 4 }, "id": 23, "panels": [], "title": "MARKETS", "type": "row" }, { "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "mDe4CYmVz" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "blue", "value": null }, { "color": "semi-dark-green", "value": 80 } ] }, "unit": "currencyUSD" }, "overrides": [] }, "gridPos": { "h": 4, "w": 6, "x": 0, "y": 5 }, "id": 2, "interval": "1", "links": [], "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "titleSize": 16, "valueSize": 27 }, "textMode": "auto" }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "current_price", "text": "Current Price", "type": "number" }, { "selector": "total_volume", "text": "24h Volume", "type": "number" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://changex-price-fetcher-xcl5j.ondigitalocean.app/coins/markets?ids=changex&vs_currency=usd", "url_options": { "data": "", "method": "GET" } } ], "title": "COINGECKO", "transformations": [ { "id": "configFromData", "options": { "applyTo": { "id": "byType", "options": "string" }, "configRefId": "A", "mappings": [] } } ], "type": "stat" }, { "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "mDe4CYmVz" }, "fieldConfig": { "defaults": { "color": { "mode": "continuous-GrYlRd" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 20, "gradientMode": "scheme", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "smooth", "lineWidth": 3, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 9, "w": 18, "x": 6, "y": 5 }, "id": 10, "interval": "60", "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.1.0-76166", "targets": [ { "columns": [], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "dataframe", "global_query_id": "", "refId": "A", "root_selector": "prices", "source": "url", "type": "json", "url": "https://changex-price-fetcher-xcl5j.ondigitalocean.app/coins/hydra/market_chart?days=30&vs_currency=usd", "url_options": { "data": "", "method": "GET" } } ], "title": "CHANGE PRICE HISTORY", "transformations": [ { "id": "convertFieldType", "options": { "conversions": [ { "dateFormat": "YYYY-MM-DD", "destinationType": "time", "targetField": "0" } ], "fields": {} } }, { "id": "sortBy", "options": { "fields": {}, "sort": [ { "desc": true, "field": "timestamp" } ] } }, { "id": "renameByRegex", "options": { "regex": "([0]*)", "renamePattern": "Date" } }, { "id": "renameByRegex", "options": { "regex": "([1]*)", "renamePattern": "Price" } } ], "type": "timeseries" }, { "datasource": { "type": "fifemon-graphql-datasource", "uid": "YC_Sc6WVk" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "noValue": "....", "thresholds": { "mode": "absolute", "steps": [ { "color": "blue", "value": null }, { "color": "green", "value": 10 }, { "color": "green", "value": 80 } ] }, "unit": "currencyUSD" }, "overrides": [] }, "gridPos": { "h": 4, "w": 6, "x": 0, "y": 9 }, "id": 19, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "vertical", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": false }, "text": { "titleSize": 13, "valueSize": 27 }, "textMode": "value_and_name" }, "pluginVersion": "9.0.6", "targets": [ { "aliasBy": "", "annotationTags": "", "annotationText": "", "annotationTitle": "", "columns": [], "constant": 6.5, "dataPath": "data", "datasource": { "type": "fifemon-graphql-datasource", "uid": "YC_Sc6WVk" }, "endTimePath": "endTime", "filters": [], "format": "table", "global_query_id": "", "groupBy": "", "queryText": "query {\n data:tokenDayDatas(\n orderDirection: \"asc\"\n \torderBy: \"date\"\n \tskip: 0\n \tfirst: 0\n where: {date_gt: \"${__from:date:seconds}\" token: \"bd3c617d271b3467bd9b83dda73c9288de2fb0c9\"}) {\n priceUSD\n dailyVolumeUSD\n }\n}", "refId": "A", "root_selector": "", "source": "url", "timePath": "", "type": "json", "url": "https://jsonplaceholder.typicode.com/users", "url_options": { "data": "", "method": "GET" } } ], "title": "HYDRA DEX", "transformations": [ { "id": "organize", "options": { "excludeByName": {}, "indexByName": {}, "renameByName": { "dailyVolumeUSD": "Today's Volume", "priceUSD": "Current Price" } } }, { "id": "convertFieldType", "options": { "conversions": [ { "destinationType": "number", "targetField": "priceUSD" } ], "fields": {} } } ], "type": "stat" }, { "datasource": { "type": "fifemon-graphql-datasource", "uid": "YC_Sc6WVk" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "percentage", "steps": [ { "color": "green", "value": null }, { "color": "#EAB839", "value": 10 }, { "color": "green", "value": 20 } ] } }, "overrides": [] }, "gridPos": { "h": 3, "w": 6, "x": 0, "y": 13 }, "id": 29, "interval": "60", "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "vertical", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "titleSize": 17, "valueSize": 27 }, "textMode": "value" }, "pluginVersion": "9.0.6", "targets": [ { "aliasBy": "", "annotationTags": "", "annotationText": "", "annotationTitle": "", "columns": [], "constant": 6.5, "dataPath": "data", "datasource": { "type": "fifemon-graphql-datasource", "uid": "YC_Sc6WVk" }, "endTimePath": "endTime", "filters": [], "format": "table", "global_query_id": "", "groupBy": "", "queryText": "query {\n data:tokenDayDatas(\n orderDirection: \"asc\"\n \torderBy: \"date\"\n \tskip: 0\n \tfirst: 0\n where: {date_gt: \"${__from:date:seconds}\" token: \"bd3c617d271b3467bd9b83dda73c9288de2fb0c9\"}) {\n \tdailyTxns\n \n }\n}", "refId": "A", "root_selector": "", "source": "url", "timePath": "", "type": "json", "url": "https://jsonplaceholder.typicode.com/users", "url_options": { "data": "", "method": "GET" } } ], "title": "Today's Transactions ", "transformations": [ { "id": "convertFieldType", "options": { "conversions": [ { "destinationType": "number", "targetField": "dailyTxns" } ], "fields": {} } } ], "type": "stat" }, { "datasource": {}, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 2, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "yellow", "value": null }, { "color": "green", "value": 10 }, { "color": "semi-dark-blue", "value": 20 } ] }, "unit": "percent" }, "overrides": [] }, "gridPos": { "h": 2, "w": 9, "x": 6, "y": 14 }, "id": 27, "interval": "3600", "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "vertical", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "titleSize": 15, "valueSize": 27 }, "textMode": "auto" }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "price_change_percentage_24h", "text": "", "type": "number" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://changex-price-fetcher-xcl5j.ondigitalocean.app/coins/markets?ids=changex&vs_currency=usd", "url_options": { "data": "", "method": "GET" } } ], "title": "PRICE CHANGE FOR THE LAST 24 HOURS", "type": "stat" }, { "datasource": {}, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "percentage", "steps": [ { "color": "green", "value": null }, { "color": "yellow", "value": 70 }, { "color": "orange", "value": 85 } ] }, "unit": "percent" }, "overrides": [] }, "gridPos": { "h": 2, "w": 9, "x": 15, "y": 14 }, "id": 12, "interval": "600", "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "vertical", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "titleSize": 25, "valueSize": 30 }, "textMode": "auto" }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "inPercent", "text": "APY ≈ ", "type": "number" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://hydra-dex-backend.changex.io/api/staking/apy?amount=1", "url_options": { "data": "", "method": "GET" } } ], "type": "stat" }, { "datasource": {}, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "percentage", "steps": [ { "color": "green", "value": null }, { "color": "yellow", "value": 70 }, { "color": "orange", "value": 85 } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 3, "w": 6, "x": 0, "y": 16 }, "id": 37, "interval": "600", "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "vertical", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "titleSize": 25, "valueSize": 30 }, "textMode": "auto" }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "activeStakers", "text": " ", "type": "number" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://hydra-dex-backend.changex.io/api/staking/active-stakers", "url_options": { "data": "", "method": "GET" } } ], "title": "ACTIVE STAKERS", "type": "stat" }, { "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "mDe4CYmVz" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "percentage", "steps": [ { "color": "green", "value": null }, { "color": "yellow", "value": 70 }, { "color": "orange", "value": 85 } ] }, "unit": "currencyUSD" }, "overrides": [] }, "gridPos": { "h": 3, "w": 9, "x": 6, "y": 16 }, "id": 38, "interval": "600", "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "vertical", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "titleSize": 25, "valueSize": 30 }, "textMode": "auto" }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "totalRewardsDistributed", "text": " ", "type": "number" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://hydra-dex-backend.changex.io/api/staking/total-rewards-distributed", "url_options": { "data": "", "method": "GET" } } ], "title": "TOTAL REWARDS PAID OUT", "type": "stat" }, { "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "mDe4CYmVz" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "percentage", "steps": [ { "color": "green", "value": null }, { "color": "yellow", "value": 70 }, { "color": "orange", "value": 85 } ] }, "unit": "currencyUSD" }, "overrides": [] }, "gridPos": { "h": 3, "w": 9, "x": 15, "y": 16 }, "id": 39, "interval": "600", "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "vertical", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "titleSize": 25, "valueSize": 30 }, "textMode": "auto" }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "totalStakedBalance", "text": " ", "type": "number" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://hydra-dex-backend.changex.io/api/staking/total-staked-balance", "url_options": { "data": "", "method": "GET" } } ], "title": "TOTAL STAKED", "type": "stat" } ], "refresh": "1m", "schemaVersion": 36, "style": "dark", "tags": [], "templating": { "list": [] }, "time": { "from": "now-30d", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ] }, "timezone": "", "title": "ChangeX DASHBOARD For Users", "uid": "m-3Y5Bz4b", "version": 6, "weekStart": "", "gnetId": 16943, "description": "For Users" }

2022-09-12 13:33:42 UTC

2022-09-12 13:34:00 UTC

pixie-pod-overview

Pixie Pod Overview

Dashboard shows an overview of the specified pod, including high-level HTTP application metrics, and resource usage.

2022-09-16 15:32:13 UTC

2022-09-16 15:32:56 UTC

To learn how to monitor infrastructure health using Pixie, see the [tutorial](https://docs.px.dev/tutorials/pixie-101/infra-health/).

Other

Dashboard shows an overview of the specified pod, including high-level HTTP application metrics, and resource usage. To learn how to monitor infrastructure health using Pixie, see https://docs.px.dev/tutorials/pixie-101/infra-health/

{ "__inputs": [ { "name": "DS_PIXIE_GRAFANA DATASOURCE PLUGIN", "label": "Pixie Grafana Datasource Plugin", "description": "", "type": "datasource", "pluginId": "pixie-pixie-datasource", "pluginName": "Pixie Grafana Datasource Plugin" } ], "__requires": [ { "type": "datasource", "id": "pixie-pixie-datasource", "name": "Pixie Grafana Datasource Plugin", "version": "0.0.9" }, { "type": "panel", "id": "table", "name": "Table", "version": "" }, { "type": "panel", "id": "timeseries", "name": "Time series", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": "-- Grafana --", "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 0, "id": null, "iteration": 1657153831003, "links": [], "liveNow": false, "panels": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "request throughput", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "/s" }, "overrides": [] }, "gridPos": { "h": 13, "w": 8, "x": 0, "y": 0 }, "id": 2, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable changes\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = inbound_request_timeseries_by_container(start_time, pod)\noutput.request_throughput = output.request_throughput * px.pow(10,9)\npx.display(output[['time_', 'request_throughput', 'container']])" }, "queryType": "run-script", "refId": "A" } ], "title": "HTTP Requests", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "error rate", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "/s" }, "overrides": [] }, "gridPos": { "h": 13, "w": 8, "x": 8, "y": 0 }, "id": 4, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = inbound_request_timeseries_by_container(start_time, pod)\noutput.errors_per_ns = output.errors_per_ns * px.pow(10,9)\npx.display(output[['time_', 'errors_per_ns', 'container']])" }, "queryType": "run-script", "refId": "A" } ], "title": "HTTP Errors", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisGridShow": true, "axisLabel": "milliseconds", "axisPlacement": "auto", "axisSoftMax": 6, "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "ms" }, "overrides": [] }, "gridPos": { "h": 13, "w": 8, "x": 16, "y": 0 }, "id": 10, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = inbound_latency_timeseries(start_time, pod)\noutput.latency_p50 = output.latency_p50 / px.pow(10,6)\noutput.latency_p90 = output.latency_p90 / px.pow(10,6)\noutput.latency_p99 = output.latency_p99 / px.pow(10,6)\n\npx.display(output[['time_', 'latency_p50', 'latency_p90', 'latency_p99']])" }, "queryType": "run-script", "refId": "A" } ], "title": "HTTP Latency", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "CPU Usage", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "percent" }, "overrides": [] }, "gridPos": { "h": 12, "w": 8, "x": 0, "y": 13 }, "id": 6, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = resource_timeseries(start_time, pod)\noutput.cpu_usage_percent = output.cpu_usage * 100\npx.display(output[['time_', 'cpu_usage_percent', 'container']])" }, "queryType": "run-script", "refId": "A" } ], "title": "CPU Usage", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "displayMode": "auto", "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "state" }, "properties": [ { "id": "custom.width", "value": 131 }, { "id": "custom.displayMode", "value": "color-text" }, { "id": "mappings", "value": [ { "options": { "Failed": { "color": "red", "index": 5 }, "Pending": { "color": "yellow", "index": 6 }, "Running": { "color": "green", "index": 0 }, "Succeeded": { "color": "green", "index": 4 }, "Terminated": { "color": "red", "index": 3 }, "Unknown": { "color": "red", "index": 7 }, "Waiting": { "color": "yellow", "index": 8 }, "false": { "color": "red", "index": 2 }, "true": { "color": "green", "index": 1 } }, "type": "value" } ] } ] }, { "matcher": { "id": "byName", "options": "ready" }, "properties": [ { "id": "custom.width", "value": 97 } ] }, { "matcher": { "id": "byName", "options": "name" }, "properties": [ { "id": "custom.width", "value": 102 } ] }, { "matcher": { "id": "byName", "options": "message" }, "properties": [ { "id": "custom.width", "value": 84 } ] } ] }, "gridPos": { "h": 12, "w": 8, "x": 8, "y": 13 }, "id": 8, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [] }, "pluginVersion": "7.5.1", "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = containers(start_time, pod)\n\noutput.state = px.pluck(output.status, \"state\") \noutput.message = px.pluck(output.status, \"ready\") \n\noutput = output[['name', 'state', 'message', 'id']]\n\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Container List", "type": "table" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "displayMode": "auto", "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "pid" }, "properties": [ { "id": "custom.width", "value": 118 } ] }, { "matcher": { "id": "byName", "options": "cmd" }, "properties": [ { "id": "custom.width", "value": 321 } ] } ] }, "gridPos": { "h": 12, "w": 8, "x": 16, "y": 13 }, "id": 12, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [] }, "pluginVersion": "7.5.1", "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = processes(start_time, pod)\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Process List", "type": "table" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "Network Throughput", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "KBs" }, "overrides": [] }, "gridPos": { "h": 13, "w": 8, "x": 0, "y": 25 }, "id": 15, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = network_timeseries(start_time, pod)\noutput.rx_bytes_per_ns = output.rx_bytes_per_ns * px.pow(10, 9) / px.pow(2,10)\noutput.tx_bytes_per_ns = output.tx_bytes_per_ns * px.pow(10, 9) / px.pow(2,10)\noutput = output[['time_', 'rx_bytes_per_ns', 'tx_bytes_per_ns']]\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Network Sent And Received", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "Disk Read Throughput", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "KBs" }, "overrides": [] }, "gridPos": { "h": 13, "w": 8, "x": 8, "y": 25 }, "id": 16, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": ".05# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = resource_timeseries(start_time, pod)\noutput.total_disk_read_throughput = output.total_disk_read_throughput * px.pow(10,9) / px.pow(2,10)\noutput = output[['time_', 'total_disk_read_throughput', 'container']]\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Bytes Read", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "Disk Write Throughput", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "binBps" }, "overrides": [] }, "gridPos": { "h": 13, "w": 8, "x": 16, "y": 25 }, "id": 17, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = resource_timeseries(start_time, pod)\noutput.total_disk_write_throughput = output.total_disk_write_throughput * px.pow(10,9)\noutput = output[['time_', 'total_disk_write_throughput', 'container']]\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Bytes Written", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "RSS", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "decmbytes" }, "overrides": [] }, "gridPos": { "h": 13, "w": 8, "x": 0, "y": 38 }, "id": 18, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = resource_timeseries(start_time, pod)\noutput.rss = output.rss / px.pow(2,20)\noutput = output[['time_', 'rss', 'container']]\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Resident Set Size", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "vsize", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "decmbytes" }, "overrides": [] }, "gridPos": { "h": 13, "w": 8, "x": 8, "y": 38 }, "id": 19, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = resource_timeseries(start_time, pod)\noutput.vsize = output.vsize / px.pow(2,20)\noutput = output[['time_', 'vsize', 'container']]\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Virtual Memory Size", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "displayMode": "auto", "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "latency_p99" }, "properties": [ { "id": "custom.displayMode", "value": "color-text" }, { "id": "thresholds", "value": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "light-orange", "value": 150 }, { "color": "red", "value": 300 } ] } }, { "id": "decimals", "value": 1 }, { "id": "unit", "value": "ms" } ] }, { "matcher": { "id": "byName", "options": "error_rate" }, "properties": [ { "id": "unit", "value": "percentunit" } ] }, { "matcher": { "id": "byName", "options": "request_throughput" }, "properties": [ { "id": "unit", "value": "/s" }, { "id": "decimals", "value": 1 } ] }, { "matcher": { "id": "byName", "options": "requesting_pod" }, "properties": [ { "id": "links", "value": [ { "title": "", "url": "/d/_t3foxCnz/px-pod?orgId=1&${pixieCluster:queryparam}&var-pixiePod=${__data.fields.requesting_pod}" } ] } ] }, { "matcher": { "id": "byName", "options": "requesting_svc" }, "properties": [ { "id": "links", "value": [ { "title": "", "url": "/d/qhHtFyCnk/px-service?orgId=1&${pixieCluster:queryparam}&var-pixieService=${__data.fields.requesting_svc}" } ] } ] } ] }, "gridPos": { "h": 10, "w": 24, "x": 0, "y": 51 }, "id": 20, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [ { "desc": true, "displayName": "error_rate" } ] }, "pluginVersion": "7.5.1", "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = inbound_let_summary(start_time, pod)\noutput.latency_p99 = px.pluck_float64(output.latency, \"p99\") / px.pow(10,6)\noutput.request_throughput = output.request_throughput * px.pow(10,9)\noutput = output[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency_p99', 'error_rate', 'request_throughput']]\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Inbound HTTP Traffic To Pod", "type": "table" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "displayMode": "auto", "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "phase" }, "properties": [ { "id": "custom.displayMode", "value": "color-text" }, { "id": "mappings", "value": [ { "options": { "Running": { "color": "green", "index": 0 } }, "type": "value" } ] } ] }, { "matcher": { "id": "byName", "options": "ready" }, "properties": [ { "id": "custom.displayMode", "value": "color-text" }, { "id": "mappings", "value": [ { "options": { "false": { "color": "red", "index": 1 }, "true": { "color": "green", "index": 0 } }, "type": "value" } ] } ] }, { "matcher": { "id": "byName", "options": "node" }, "properties": [ { "id": "links", "value": [ { "title": "", "url": "/d/tcpoz_jnz/px-node?orgId=1&${pixieCluster:queryparam}&var-node=${__data.fields.node}&var-groupby=node" } ] } ] }, { "matcher": { "id": "byName", "options": "service" }, "properties": [ { "id": "links", "value": [ { "title": "", "url": "/d/qhHtFyCnk/px-service?orgId=1&${pixieCluster:queryparam}&var-pixieService=${__data.fields.service}" } ] } ] } ] }, "gridPos": { "h": 8, "w": 24, "x": 0, "y": 61 }, "id": 21, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true }, "pluginVersion": "7.5.1", "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = node(start_time, pod)\noutput.phase = px.pluck(output.status, \"phase\")\noutput.ready = px.pluck(output.status, \"ready\")\noutput = output[['node', 'phase', 'ready', 'service', 'pod_id', 'pod_start_time']]\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Pod Metadata", "type": "table" } ], "schemaVersion": 27, "style": "dark", "tags": [], "templating": { "list": [ { "current": {}, "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "definition": "Clusters", "hide": 0, "includeAll": false, "multi": false, "name": "pixieCluster", "options": [], "query": { "queryType": "get-clusters" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "definition": "Pods", "hide": 0, "includeAll": false, "multi": false, "name": "pixiePod", "options": [], "query": { "queryBody": { "clusterID": "$pixieCluster" }, "queryType": "get-pods" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" } ] }, "time": { "from": "now-15m", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Pixie Pod Overview", "description": "Dashboard shows an overview of the specified pod, including high-level HTTP application metrics, and resource usage. ", "uid": "_t3foxCnz", "version": 1, "weekStart": "", "gnetId": 17001 }

2022-09-16 15:32:13 UTC

2022-09-16 15:32:55 UTC

pi-hole-exporter

PI-Hole Exporter

A dashboard for PI-Hole Exporter from https://github.com/eko/pihole-exporter

2022-09-29 15:28:14 UTC

2022-09-29 15:29:05 UTC

nodeExporter

A dashboard for PI-Hole Exporter from https://github.com/eko/pihole-exporter

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"list": [] }, "time": { "from": "now-5m", "to": "now" }, "timepicker": { "hidden": false, "nowDelay": "", "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "PI-Hole Exporter", "uid": "MIBVglomg", "version": 122, "description": "A dashboard for PI-Hole Exporter from https://github.com/eko/pihole-exporter" }

2022-09-29 15:28:14 UTC

opentelemetry-collector

OpenTelemetry Collector v20221228

Provides information about the status of the OpenTelemetry Collector

2022-12-28 11:30:31 UTC

2022-12-28 11:36:50 UTC

Include Span, Metrics and Logs information for Reseive/Export

Other

Provides information about the status of the OpenTelemetry Collector

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"title": "Processor Spans Received, Dropped, Accepted, Refused/Second", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "min": "0", "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 9, "w": 12, "x": 12, "y": 30 }, "hiddenSeries": false, "id": 14, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "9.2.4", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(rate(otelcol_processor_queued_retry_success_send{processor=~\"$processor\"}[1m])) by (processor)", "format": "time_series", "hide": false, "interval": "", "intervalFactor": 1, "legendFormat": "{{processor}}", "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(rate(otelcol_processor_queued_retry_fail_send{processor=~\"$processor\"}[1m])) by (processor)", "interval": "", "legendFormat": "{{processor}}", "refId": "B" } ], "thresholds": [], "timeRegions": [], "title": "QueuedRetry Successful vs Failed Sent/Second", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 9, "w": 12, "x": 0, "y": 39 }, "hiddenSeries": false, "id": 2, "interval": "", "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "9.2.4", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(rate(otelcol_processor_spans_received{processor=~\"$processor\"}[1m]) / rate(otelcol_processor_batches_received{processor=~\"$processor\"}[1m])) by (processor)", "format": "time_series", "hide": false, "interval": "", "intervalFactor": 1, "legendFormat": "{{processor}}", "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "Spans per Batch (avg from rates)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 9, "w": 12, "x": 12, "y": 39 }, "hiddenSeries": false, "id": 12, "interval": "", "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "9.2.4", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(rate(otelcol_processor_batches_received{processor=~\"$processor\"}[1m])) by (processor)", "format": "time_series", "interval": "", "intervalFactor": 1, "legendFormat": "{{processor}} | received", "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(rate(otelcol_processor_trace_batches_dropped{processor=~\"$processor\"}[1m])) by (processor)", "interval": "", "legendFormat": "{{processor}} | trace dropped", "refId": "B" } ], "thresholds": [], "timeRegions": [], "title": "Batches Received vs Dropped /Second", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 9, "w": 12, "x": 0, "y": 48 }, "hiddenSeries": false, "id": 6, "interval": "", "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "9.2.4", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(otelcol_processor_queued_retry_success_send{processor=~\"$processor\"}) by (processor)", "format": "time_series", "hide": false, "interval": "", "intervalFactor": 1, "legendFormat": "{{processor}}", "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "Successful Batches Sent Cumulative", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "min": "0", "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 9, "w": 12, "x": 12, "y": 48 }, "hiddenSeries": false, "id": 3, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "9.2.4", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(otelcol_processor_spans_received{exporter=~\"$exporter\"}) by (exporter)", "format": "time_series", "hide": false, "interval": "", "intervalFactor": 1, "legendFormat": "{{exporter}}", "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "Enqueued Spans Cumulative", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } } ], "refresh": false, "schemaVersion": 37, "style": "dark", "tags": [ "opentelemetry" ], "templating": { "list": [ { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(otelcol_process_cpu_seconds,namespace)", "hide": 0, "includeAll": false, "multi": true, "name": "namespace", "options": [], "query": { "query": "label_values(otelcol_process_cpu_seconds,namespace)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(otelcol_process_cpu_seconds{namespace=~\"$namespace\"},deployment_environment)", "hide": 0, "includeAll": true, "multi": true, "name": "env", "options": [], "query": { "query": "label_values(otelcol_process_cpu_seconds{namespace=~\"$namespace\"},deployment_environment)", "refId": "StandardVariableQuery" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "allValue": ".*", "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(otelcol_receiver_accepted_metric_points, receiver)", "hide": 0, "includeAll": true, "label": "receiver", "multi": true, "name": "receiver", "options": [], "query": { "query": "label_values(otelcol_receiver_accepted_metric_points, receiver)", "refId": "StandardVariableQuery" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "allValue": ".*", "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(otelsvc_exporter)", "hide": 0, "includeAll": true, "label": "exporter", "multi": true, "name": "exporter", "options": [], "query": { "query": "label_values(otelsvc_exporter)", "refId": "Mimir-exporter-Variable-Query" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(processor)", "hide": 0, "includeAll": true, "label": "processor", "multi": true, "name": "processor", "options": [], "query": { "query": "label_values(processor)", "refId": "Mimir-processor-Variable-Query" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-15m", "to": "now" }, "timepicker": { "refresh_intervals": [ "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "OpenTelemetry Collector v20221228", "uid": "BKf2sowmj", "version": 9, "weekStart": "" }

2022-12-28 11:30:31 UTC

2022-12-28 11:34:45 UTC

readsb-prometheus

readsb

Using Prometheus as a datasource to visualise readsb data

2023-02-21 02:54:31 UTC

2023-02-21 02:56:37 UTC

Used to visualise metrics collected from the mikenye/readsb-protobuf docker image. This dashboard aims to emulate the majority of the functionality of graphs1090, but using Prometheus and Grafana.

{ "__inputs": [ { "name": "DS_PROMETHEUS", "label": "Prometheus", "description": "instance being fed by readsb's telegraf", "type": "datasource", "pluginId": "prometheus", "pluginName": "Prometheus" } ], "__elements": {}, "__requires": [ { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "9.3.6" }, { "type": "datasource", "id": "prometheus", "name": "Prometheus", "version": "1.0.0" }, { "type": "panel", "id": "stat", "name": "Stat", "version": "" }, { "type": "panel", "id": "timeseries", "name": "Time series", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": { "type": "grafana", "uid": "-- Grafana --" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 0, "id": null, "links": [], "liveNow": false, "panels": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "blue", "value": null } ] } }, "overrides": [] }, "id": 4, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.3.6", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "max(readsb_tracks_with_position{host=~\"$hostname\"})", "legendFormat": "__auto", "range": true, "refId": "A" } ], "title": "Currently Tracked AIrcraft", "type": "stat" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "blue", "value": null } ] }, "unit": "lengthm" }, "overrides": [] }, "gridPos": { "h": 3, "w": 6, "x": 6, "y": 0 }, "id": 6, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.3.6", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "readsb_max_distance_in_metres{host=~\"$hostname\"}", "legendFormat": "__auto", "range": true, "refId": "A" } ], "timeFrom": "24h", "title": "Maximum Range", "type": "stat" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 2, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "blue", "value": null } ] }, "unit": "dB" }, "overrides": [] }, "gridPos": { "h": 3, "w": 6, "x": 12, "y": 0 }, "id": 7, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.3.6", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "avg(readsb_local_signal{host=~\"$hostname\"})", "legendFormat": "__auto", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "avg(readsb_local_noise{host=~\"$hostname\"})", "hide": false, "legendFormat": "__auto", "range": true, "refId": "B" } ], "timeFrom": "24h", "title": "Avg. SNR", "transformations": [ { "id": "calculateField", "options": { "alias": "Signal to Noise Ratio", "binary": { "left": "avg(readsb_local_signal{host=~\"readsb\"})", "operator": "-", "reducer": "sum", "right": "avg(readsb_local_noise{host=~\"readsb\"})" }, "mode": "binary", "reduce": { "reducer": "sum" }, "replaceFields": true } } ], "type": "stat" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 1, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "red", "value": null }, { "color": "green", "value": 0.01 }, { "color": "red", "value": 0.05 } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 6, "x": 18, "y": 0 }, "id": 8, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.3.6", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "sum(readsb_local_strong_signals{host=~\"$hostname\"})", "legendFormat": "__auto", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "sum(readsb_local_accepted{host=~\"$hostname\"})", "hide": false, "legendFormat": "__auto", "range": true, "refId": "B" } ], "timeFrom": "24h", "title": "% Strong Signals", "transformations": [ { "id": "calculateField", "options": { "alias": "Percentage of Strong Signals", "binary": { "left": "sum(readsb_local_strong_signals{host=~\"readsb\"})", "operator": "/", "reducer": "sum", "right": "sum(readsb_local_accepted{host=~\"readsb\"})" }, "mode": "binary", "reduce": { "reducer": "sum" }, "replaceFields": true } } ], "type": "stat" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 3 }, "id": 2, "panels": [], "title": "Performance Graphs", "type": "row" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "stepBefore", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "short" }, "overrides": [ { "matcher": { "id": "byName", "options": "Messages > 3dbFS" }, "properties": [ { "id": "color", "value": { "fixedColor": "red", "mode": "fixed" } }, { "id": "custom.spanNulls", "value": true } ] }, { "matcher": { "id": "byName", "options": "Messages Received" }, "properties": [ { "id": "color", "value": { "fixedColor": "blue", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "Positions/s (RHS)" }, "properties": [ { "id": "color", "value": { "fixedColor": "super-light-blue", "mode": "fixed" } }, { "id": "unit" } ] } ] }, "gridPos": { "h": 8, "w": 24, "x": 0, "y": 4 }, "id": 10, "options": { "legend": { "calcs": [ "lastNotNull" ], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "max(readsb_messages{host=~\"$hostname\"})", "legendFormat": "Messages Received", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "readsb_local_strong_signals{host=~\"$hostname\"}", "hide": false, "legendFormat": "Messages > 3dbFS", "range": true, "refId": "B" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "max(readsb_cpr_global_ok{host=~\"$hostname\"}) / 60", "hide": false, "legendFormat": "Positions/s (RHS)", "range": true, "refId": "C" } ], "title": "Message Rate", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "decimals": 0, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 12 }, "id": 12, "options": { "legend": { "calcs": [ "max", "mean" ], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "max(readsb_tracks_with_position{host=~\"$hostname\"})", "legendFormat": "ADS-B", "range": true, "refId": "A" } ], "title": "Positions", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": 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}, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "dB" }, "overrides": [ { "matcher": { "id": "byName", "options": "Peak Signal" }, "properties": [ { "id": "color", "value": { "fixedColor": "blue", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "Noise" }, "properties": [ { "id": "color", "value": { "fixedColor": "yellow", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 20 }, "id": 18, "options": { "legend": { "calcs": [ "mean", "lastNotNull" ], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "avg(readsb_local_signal{host=~\"$hostname\"})", "legendFormat": "Signal", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "avg(readsb_local_peak_signal{host=~\"$hostname\"})", "hide": false, "legendFormat": "Peak Signal", "range": true, "refId": "B" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "avg(readsb_local_noise{host=~\"$hostname\"})", "hide": false, "legendFormat": "Noise", "range": true, "refId": "C" } ], "title": "Signal Strength", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "normal" }, "thresholdsStyle": { "mode": "off" } }, "decimals": 2, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 20 }, "id": 16, "options": { "legend": { "calcs": [ "mean", "max" ], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "max(readsb_cpu_reader{host=~\"$hostname\"}) / 60000", "legendFormat": "SDR Reader", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "max(readsb_cpu_demod{host=~\"$hostname\"}) / 60000", "hide": false, "legendFormat": "Demodulator", "range": true, "refId": "B" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "max(readsb_cpu_background{host=~\"$hostname\"}) / 60000", "hide": false, "legendFormat": "Background", "range": true, "refId": "C" } ], "title": "readsb CPU", "type": "timeseries" } ], "schemaVersion": 37, "style": "dark", "tags": [], "templating": { "list": [ { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(host)", "hide": 0, "includeAll": true, "label": "Hostname", "multi": true, "name": "hostname", "options": [], "query": { "query": "label_values(host)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "type": "query" } ] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "readsb", "uid": "xxPTRy1Vk", "version": 11, "weekStart": "", "gnetId": 18148, "description": "Using Prometheus as a datasource to visualise readsb data" }

2023-02-21 02:54:31 UTC

2023-02-21 02:56:37 UTC

ra-l-s-dashboard-grouped-by-op-filtered-by-nodes-canaries

Raúl's dashboard (grouped by op, filtered by nodes) (CANARIES)

Dashboard to track Filecoin Lotus node API endpoints.

2023-03-27 07:01:24 UTC

2023-03-27 07:03:06 UTC

Dashboard to track Filecoin Lotus node API endpoints.

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true, "multi": true, "name": "endpoint", "options": [], "query": { "query": "lotus_api_request_duration_ms_bucket", "refId": "Prometheus-endpoint-Variable-Query" }, "refresh": 2, "regex": "/.*endpoint=\"([a-zA-Z]*)\"/", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "current": {}, "datasource": { "type": "prometheus", "uid": "prometheus" }, "definition": "lotus_pubsub_send_rpc", "hide": 0, "includeAll": true, "multi": true, "name": "pod", "options": [], "query": { "query": "lotus_pubsub_send_rpc", "refId": "Prometheus-pod-Variable-Query" }, "refresh": 2, "regex": "/.*pod=\"(.*?)\"/", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ] }, "timezone": "", "title": "Raúl's dashboard (grouped by op, filtered by nodes) 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2023-03-27 07:01:24 UTC

used-wasted-resources

Used | Wasted Resources

Dashboard to show the resource requests vs allocatable in the cluster

2023-03-30 09:48:00 UTC

2023-03-30 09:51:17 UTC

Uses kube-prometheus-stack with node-exporter and kube-state-metrics

nodeExporter

Dashboard to show the resource requests vs allocatable in the cluster

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2023-03-30 09:48:00 UTC

pgbackrest

Crunchy/pgBackRest

pgBackRest monitoring dashboard for PGO Crunchy Operator

2023-05-05 14:46:43 UTC

2023-05-05 14:50:24 UTC

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Operator" }

2023-05-05 14:46:43 UTC

2023-05-05 14:50:08 UTC

ruckus-msp-cloud-dashboard-venue

Ruckus MSP Cloud Dashboard - Venue

This Dashboard queries all APs from all the End Customers (tenants) of MSP and show a single windows to monitor

2023-01-11 08:53:13 UTC

2023-01-11 08:53:48 UTC

Other

This Dashboard queries all APs from all the End Customers (tenants) of MSP and show a single windows to monitor

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false, "refId": "E", "resultFormat": "table", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "last" }, { "params": [ "Stat" ], "type": "alias" } ] ], "tags": [ { "key": "Venue_Name", "operator": "=~", "value": "/^$venue$/" }, { "condition": "AND", "key": "Status", "operator": "=~", "value": "/Failed|Disconnected|Rebooting|Lost|Offline/" } ] } ], "timeFrom": "15m", "title": "Needs attention", "transformations": [ { "id": "organize", "options": { "excludeByName": { "Customer_Name": false, "Stat": true, "Status": false, "Time": true }, "indexByName": { "Device_IPaddr": 5, "Device_Model": 4, "Device_Name": 1, "Last_Seen": 3, "Stat": 7, "Status": 2, "Time": 0, "Venue_Name": 6 }, "renameByName": { "AP_IPaddr": "IP Address", "AP_MAC": "MAC Address", "AP_Name": "Access Point Name", "Customer_Name": "End Customer name", "Dev_fw": "Firmware", "Device": "", "Device_IPaddr": "Device IP", "Device_MAC": "Device MAC", "Device_Model": "Model", "Device_Name": "Device Name", "Device_type": "Type", "Last_Seen": "Last seen on ", "Serial_No": "Serial Number", "Status": "Status", "Uptime": "Last known Uptime", "Venue_Name": "Venue Name", "valueStr": "HiVision Status" } } } ], "type": "table" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "noValue": "0 devices need attention", "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 1 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Out of (total)" }, "properties": [ { "id": "thresholds", "value": { "mode": "absolute", "steps": [ { "color": "dark-blue", "value": null } ] } } ] }, { "matcher": { "id": "byName", "options": "Needs Attention" }, "properties": [ { "id": "links", "value": [] } ] } ] }, "gridPos": { "h": 4, "w": 3, "x": 0, "y": 12 }, "id": 37, "options": { "colorMode": "background", "graphMode": "area", "justifyMode": "center", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "value_and_name" }, "pluginVersion": "9.3.2", "targets": [ { "alias": "Devices Needs Attention", "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "hide": false, "measurement": "/^$series_name$/", "orderByTime": "ASC", "policy": "default", "query": "SELECT sum(\"value\") FROM /^$series_name$/ WHERE (\"Customer_Name\" =~ /^$tenant$/ AND \"Severity\" = 'Requires Attention' AND $timeFilter) OR (\"Customer_Name\" =~ /^$tenant$/ AND \"Severity\" = 'Transceint Issue' AND $timeFilter) OR (\"Customer_Name\" =~ /^$tenant$/ AND \"Status\" = 'OFFLINE' AND $timeFilter) GROUP BY time($__interval) fill(null)", "rawQuery": false, "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "sum" } ] ], "tags": [ { "key": "Venue_Name", "operator": "=~", "value": "/^$venue$/" }, { "condition": "AND", "key": "Status", "operator": "=~", "value": "/Failed|Disconnected|Rebooting|Lost|Offline/" } ] } ], "timeFrom": "15m", "transformations": [], "transparent": true, "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "noValue": "0 APs need attention", "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 1 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Out of (total)" }, "properties": [ { "id": "thresholds", "value": { "mode": "absolute", "steps": [ { "color": "dark-blue", "value": null } ] } } ] }, { "matcher": { "id": "byName", "options": "Needs Attention" }, "properties": [ { "id": "links", "value": [ { "title": "Needs attention", "url": "d/x1d4sjWRkdsf/ruckus-cloud-tenant-dashboard?orgId=1&from=now-15m&to=now&viewPanel=27" } ] } ] } ] }, "gridPos": { "h": 4, "w": 3, "x": 3, "y": 12 }, "id": 41, "options": { "colorMode": "background", "graphMode": "area", "justifyMode": "center", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "value_and_name" }, "pluginVersion": "9.3.2", "targets": [ { "alias": "APs Needs Attention", "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "groupBy": [ { "params": [ "$interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "hide": false, "measurement": "/^$series_name$/", "orderByTime": "ASC", "policy": "default", "query": "SELECT sum(\"value\") FROM /^$series_name$/ WHERE (\"Customer_Name\" =~ /^$tenant$/ AND \"Severity\" = 'Requires Attention' AND \"Device_type\" = 'Access Point' AND $timeFilter) OR (\"Customer_Name\" =~ /^$tenant$/ AND \"Severity\" = 'Transceint Issue' AND \"Device_type\" = 'Access Point' AND $timeFilter) GROUP BY time($__interval) fill(null)", "rawQuery": false, "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "sum" } ] ], "tags": [ { "key": "Venue_Name", "operator": "=~", "value": "/^$venue$/" }, { "condition": "AND", "key": "Status", "operator": "=~", "value": "/Failed|Disconnected|Rebooting|Lost/" } ] } ], "timeFrom": "15m", "transformations": [], "transparent": true, "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "noValue": "0 ICX need attention", "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 1 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Out of (total)" }, "properties": [ { "id": "thresholds", "value": { "mode": "absolute", "steps": [ { "color": "dark-blue", "value": null } ] } } ] }, { "matcher": { "id": "byName", "options": "Needs Attention" }, "properties": [ { "id": "links", "value": [ { "title": "Needs attention", "url": "d/x1d4sjWRkdsf/ruckus-cloud-tenant-dashboard?orgId=1&from=now-15m&to=now&viewPanel=27" } ] } ] } ] }, "gridPos": { "h": 4, "w": 3, "x": 6, "y": 12 }, "id": 45, "options": { "colorMode": "background", "graphMode": "area", "justifyMode": "center", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "value_and_name" }, "pluginVersion": "9.3.2", "targets": [ { "alias": "ICX Needs Attention", "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "hide": false, "measurement": "/^$series_name$/", "orderByTime": "ASC", "policy": "default", "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "sum" } ] ], "tags": [ { "key": "Venue_Name", "operator": "=~", "value": "/^$venue$/" }, { "condition": "AND", "key": "Status", "operator": "=", "value": "Offline" } ] } ], "timeFrom": "15m", "transformations": [], "transparent": true, "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "continuous-BlPu" }, "custom": { "axisCenteredZero": false, "axisColorMode": "series", "axisLabel": "Devices", "axisPlacement": "auto", "axisSoftMin": 0, "barAlignment": 1, "drawStyle": "bars", "fillOpacity": 14, "gradientMode": "scheme", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 2, "pointSize": 10, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "sltaps.sum" }, "properties": [ { "id": "displayName", "value": "Total AP" } ] }, { "matcher": { "id": "byName", "options": "Value" }, "properties": [ { "id": "displayName", "value": "Disconnected APs" } ] } ] }, "gridPos": { "h": 8, "w": 15, "x": 9, "y": 12 }, "id": 35, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "measurement": "/^$series_name$/", "orderByTime": "ASC", "policy": "default", "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "sum" } ] ], "tags": [ { "key": "Venue_Name", "operator": "=~", "value": "/^$venue$/" } ] } ], "timeFrom": "24h", "title": "$tenant Device Status over time", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "noValue": "No Devices found", "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 1 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Out of (total)" }, "properties": [ { "id": "thresholds", "value": { "mode": "absolute", "steps": [ { "color": "dark-blue", "value": null } ] } } ] }, { "matcher": { "id": "byName", "options": "Needs Attention" }, "properties": [ { "id": "links", "value": [] } ] } ] }, "gridPos": { "h": 4, "w": 3, "x": 0, "y": 16 }, "id": 39, "options": { "colorMode": "background", "graphMode": "area", "justifyMode": "center", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "value_and_name" }, "pluginVersion": "9.3.2", "targets": [ { "alias": "Out of (total)", "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "hide": false, "measurement": "/^$series_name$/", "orderByTime": "ASC", "policy": "default", "refId": "B", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "sum" } ] ], "tags": [ { "key": "Venue_Name", "operator": "=~", "value": "/^$venue$/" } ] } ], "timeFrom": "15m", "transformations": [], "transparent": true, "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "noValue": "0", "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 1 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Out of (total)" }, "properties": [ { "id": "thresholds", "value": { "mode": "absolute", "steps": [ { "color": "dark-blue", "value": null } ] } } ] }, { "matcher": { "id": "byName", "options": "Needs Attention" }, "properties": [ { "id": "links", "value": [ { "title": "Needs attention", "url": "d/x1d4sjWRkdsf/ruckus-cloud-tenant-dashboard?orgId=1&from=now-15m&to=now&viewPanel=27" } ] } ] } ] }, "gridPos": { "h": 4, "w": 3, "x": 3, "y": 16 }, "id": 43, "options": { "colorMode": "background", "graphMode": "area", "justifyMode": "center", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "value_and_name" }, "pluginVersion": "9.3.2", "targets": [ { "alias": "Out of (total)", "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "hide": false, "measurement": "/^$series_name$/", "orderByTime": "ASC", "policy": "default", "refId": "B", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "sum" } ] ], "tags": [ { "key": "Venue_Name", "operator": "=~", "value": "/^$venue$/" }, { "condition": "AND", "key": "Device_type", "operator": "=", "value": "Access Point" } ] } ], "timeFrom": "15m", "transformations": [], "transparent": true, "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "noValue": "No Switches", "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 1 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Out of (total)" }, "properties": [ { "id": "thresholds", "value": { "mode": "absolute", "steps": [ { "color": "dark-blue", "value": null } ] } } ] }, { "matcher": { "id": "byName", "options": "Needs Attention" }, "properties": [ { "id": "links", "value": [ { "title": "Needs attention", "url": "d/x1d4sjWRkdsf/ruckus-cloud-tenant-dashboard?orgId=1&from=now-15m&to=now&viewPanel=27" } ] } ] } ] }, "gridPos": { "h": 4, "w": 3, "x": 6, "y": 16 }, "id": 47, "options": { "colorMode": "background", "graphMode": "area", "justifyMode": "center", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "value_and_name" }, "pluginVersion": "9.3.2", "targets": [ { "alias": "Out of (total)", "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "hide": false, "measurement": "/^$series_name$/", "orderByTime": "ASC", "policy": "default", "refId": "B", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "sum" } ] ], "tags": [ { "key": "Venue_Name", "operator": "=~", "value": "/^$venue$/" }, { "condition": "AND", "key": "Device_type", "operator": "=", "value": "Switch" } ] } ], "timeFrom": "15m", "transformations": [], "transparent": true, "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "description": "", "fieldConfig": { "defaults": { "custom": { "align": "auto", "displayMode": "auto", "filterable": true, "inspect": false }, "mappings": [ { "options": { "Error": { "color": "dark-red", "index": 0 }, "Not Saved": { "color": "dark-yellow", "index": 3 }, "Off": { "color": "dark-red", "index": 1 }, "Warning": { "color": "dark-yellow", "index": 2 }, "unsecure": { "color": "dark-yellow", "index": 4 } }, "type": "value" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "rgb(255, 255, 255)", "value": null } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Uptime" }, "properties": [ { "id": "custom.displayMode", "value": "auto" }, { "id": "unit", "value": "s" } ] }, { "matcher": { "id": "byName", "options": "Status" }, "properties": [ { "id": "mappings", "value": [ { "options": { "1_01_NeverContactedCloud": { "color": "blue", "index": 1, "text": "Never Contacted Cloud" }, "2_00_Operational": { "color": "green", "index": 0, "text": "Operational" }, "2_02_ApplyingConfiguration": { "color": "super-light-green", "index": 4, "text": "Applying Configuration" }, "3_04_DisconnectedFromCloud": { "color": "red", "index": 2, "text": "Disconnected From Cloud" }, "4_04_HeartbeatLost": { "color": "orange", "index": 3, "text": "Heartbeat Lost" } }, "type": "value" } ] }, { "id": "custom.displayMode", "value": "color-text" } ] }, { "matcher": { "id": "byName", "options": "Last seen on " }, "properties": [ { "id": "unit", "value": "dateTimeAsLocalNoDateIfToday" }, { "id": "custom.align", "value": "right" } ] }, { "matcher": { "id": "byName", "options": "Device Name" }, "properties": [ { "id": "links", "value": [ { "title": "Show Device details", "url": "d/MHK5JWhVk/devices?orgId=1&var-device=${__data.fields[\"Device Name\"]}&var-series_name=$series_name&from=now-15m&to=now" } ] } ] } ] }, "gridPos": { "h": 12, "w": 24, "x": 0, "y": 20 }, "id": 12, "options": { "footer": { "fields": [ "Serial_No" ], "reducer": [ "distinctCount" ], "show": true }, "showHeader": true, "sortBy": [ { "desc": true, "displayName": "Status" } ] }, "pluginVersion": "9.3.2", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "groupBy": [ { "params": [ "Device_Name" ], "type": "tag" }, { "params": [ "Device_IPaddr" ], "type": "tag" }, { "params": [ "Device_MAC" ], "type": "tag" }, { "params": [ "Customer_Name" ], "type": "tag" }, { "params": [ "Last_Seen" ], "type": "tag" }, { "params": [ "Serial_No" ], "type": "tag" }, { "params": [ "Venue_Name" ], "type": "tag" }, { "params": [ "Uptime" ], "type": "tag" }, { "params": [ "Status" ], "type": "tag" }, { "params": [ "Dev_fw" ], "type": "tag" }, { "params": [ "Device_Model" ], "type": "tag" }, { "params": [ "Device_type" ], "type": "tag" }, { "params": [ "Severity" ], "type": "tag" }, { "params": [ "previous" ], "type": "fill" } ], "measurement": "/^$series_name$/", "orderByTime": "ASC", "policy": "default", "query": "SELECT last(\"valueStr\") AS \"Status\" FROM \"PowersupplyStatus\" WHERE $timeFilter GROUP BY \"Device\"", "rawQuery": false, "refId": "E", "resultFormat": "table", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "last" }, { "params": [ "Stat" ], "type": "alias" } ] ], "tags": [ { "key": "Venue_Name", "operator": "=~", "value": "/^$venue$/" } ] } ], "timeFrom": "15m", "title": "Device Status - $venue", "transformations": [ { "id": "organize", "options": { "excludeByName": { "Customer_Name": true, "Stat": true, "Status": false, "Time": true }, "indexByName": { "Customer_Name": 4, "Dev_fw": 12, "Device_IPaddr": 2, "Device_MAC": 3, "Device_Model": 11, "Device_Name": 1, "Device_type": 10, "Last_Seen": 6, "Serial_No": 8, "Stat": 13, "Status": 9, "Time": 0, "Uptime": 7, "Venue_Name": 5 }, "renameByName": { "AP_IPaddr": "IP Address", "AP_MAC": "MAC Address", "AP_Name": "Access Point Name", "Customer_Name": "End Customer name", "Dev_fw": "Firmware", "Device": "", "Device_IPaddr": "Device IP", "Device_MAC": "Device MAC", "Device_Model": "Device Model", "Device_Name": "Device Name", "Device_type": "Type", "Last_Seen": "Last seen on ", "Serial_No": "Serial Number", "Status": "Status", "Uptime": "Last known Uptime", "Venue_Name": "Venue Name", "valueStr": "HiVision Status" } } } ], "type": "table" } ], "refresh": false, "schemaVersion": 37, "style": "dark", "tags": [ "ruckus", "msp", "cloud" ], "templating": { "list": [ { "current": { "selected": true, "text": "sltaps", "value": "sltaps" }, "hide": 2, "name": "series_name", "options": [ { "selected": true, "text": "sltaps", "value": "sltaps" } ], "query": "sltaps", "skipUrlSync": false, "type": "textbox" }, { "current": {}, "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "definition": "select Venue_Name, value from $series_name WHERE time >= now() - 15m and time <= now()", "description": "Enter your Venue", "hide": 0, "includeAll": false, "label": "Venue", "multi": false, "name": "venue", "options": [], "query": "select Venue_Name, value from $series_name WHERE time >= now() - 15m and time <= now()", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "type": "query" } ] }, "time": { "from": "now-15m", "to": "now" }, "timepicker": { "hidden": false, "refresh_intervals": [ "15m" ] }, "timezone": "browser", "title": "Ruckus MSP Cloud Dashboard - Venue", "uid": "09chXZ24k", "version": 35, "weekStart": "", "gnetId": 17853 }

2023-01-11 08:53:13 UTC

dnsdist

dnsdist

dnsdist is a highly DNS-, DoS- and abuse-aware loadbalancer. Its goal in life is to route traffic to the best server, delivering top performance to legitimate users while shunting or blocking abusive traffic.

2023-02-06 08:34:16 UTC

2023-02-06 08:40:18 UTC

Shows all available metrics for PowerDNS dnsdist

dnsdist is a highly DNS-, DoS- and abuse-aware loadbalancer. Its goal in life is to route traffic to the best server, delivering top performance to legitimate users while shunting or blocking abusive traffic.

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"options": "servfail-answers" }, "properties": [ { "id": "color", "value": { "fixedColor": "blue", "mode": "fixed" } }, { "id": "custom.transform", "value": "negative-Y" }, { "id": "custom.stacking", "value": { "group": "A", "mode": "normal" } } ] }, { "matcher": { "id": "byName", "options": "nxdomain-answers" }, "properties": [ { "id": "color", "value": { "fixedColor": "semi-dark-blue", "mode": "fixed" } }, { "id": "custom.transform", "value": "negative-Y" }, { "id": "custom.stacking", "value": { "group": "A", "mode": "normal" } } ] }, { "matcher": { "id": "byName", "options": "self-answered" }, "properties": [ { "id": "color", "value": { "fixedColor": "super-light-blue", "mode": "fixed" } }, { "id": "custom.stacking", "value": { "group": "A", "mode": "normal" } } ] }, { "matcher": { "id": "byName", "options": "noerror-answers" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-blue", "mode": "fixed" } }, { "id": "custom.transform", "value": "negative-Y" }, { "id": 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"alias(perSecond(sumSeries(dnsdist.$host.main.empty-queries)), 'empty-queries')" }, { "datasource": { "type": "graphite", "uid": "${DS_NL-INFRA-CAP1_GRAPHITE}" }, "hide": false, "refCount": 0, "refId": "H", "target": "alias(perSecond(sumSeries(dnsdist.$host.main.rdqueries)), 'recursion desired')" }, { "datasource": { "type": "graphite", "uid": "${DS_NL-INFRA-CAP1_GRAPHITE}" }, "refCount": 0, "refId": "A", "target": "alias(perSecond(sumSeries(dnsdist.$host.main.queries)), 'queries')" }, { "datasource": { "type": "graphite", "uid": "${DS_NL-INFRA-CAP1_GRAPHITE}" }, "hide": false, "refCount": 0, "refId": "B", "target": "alias(perSecond(sumSeries(dnsdist.$host.main.responses, dnsdist.$host.main.cache-hits)), 'responses')", "textEditor": false }, { "datasource": { "type": "graphite", "uid": "${DS_NL-INFRA-CAP1_GRAPHITE}" }, "hide": false, "refCount": 0, "refId": "C", "target": "alias(perSecond(sumSeries(dnsdist.$host.main.frontend-servfail)), 'servfail-answers')" }, { "datasource": { "type": "graphite", "uid": "${DS_NL-INFRA-CAP1_GRAPHITE}" }, "hide": false, "refCount": 0, "refId": "D", "target": "alias(perSecond(sumSeries(dnsdist.$host.main.frontend-nxdomain)), 'nxdomain-answers')" }, { "datasource": { "type": "graphite", "uid": "${DS_NL-INFRA-CAP1_GRAPHITE}" }, "hide": false, "refCount": 0, "refId": "F", "target": "alias(perSecond(sumSeries(dnsdist.$host.main.frontend-noerror)), 'noerror-answers')" }, { "datasource": { "type": "graphite", "uid": "${DS_NL-INFRA-CAP1_GRAPHITE}" }, "hide": false, "refCount": 0, "refId": "E", "target": "alias(perSecond(sumSeries(dnsdist.$host.main.self-answered)), 'self-answered')" } ], "title": "Queries/s vs. Responses/s", "transparent": true, "type": "timeseries" }, { "datasource": { "type": "graphite", "uid": "${DS_NL-INFRA-CAP1_GRAPHITE}" }, "description": "Number of queries per second dropped because the internal DoH pipe was full.", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": 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"absolute", "steps": [ { "color": "green", "value": null }, { "color": "semi-dark-red", "value": 5 } ] } }, "overrides": [] }, "gridPos": { "h": 4, "w": 4, "x": 4, "y": 4 }, "id": 26, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.3.1", "targets": [ { "datasource": { "type": "graphite", "uid": "${DS_NL-INFRA-CAP1_GRAPHITE}" }, "refId": "A", "target": "alias(perSecond(sumSeries(dnsdist.$host.main.doh-response-pipe-full)), 'doh-response-pipe-full')" } ], "title": "DoH response pipe full", "transparent": true, "type": "stat" }, { "datasource": { "type": "graphite", "uid": "${DS_NL-INFRA-CAP1_GRAPHITE}" }, "description": "Average response latency in microseconds of the last 10000 packets", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "µs" }, "overrides": [ { "matcher": { "id": "byName", "options": "nl-inx-fw3" }, "properties": [ { "id": "color", "value": { "fixedColor": "orange", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "nl-inx-fw2" }, "properties": [ { "id": "color", "value": { "fixedColor": "super-light-orange", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 7, "w": 8, "x": 0, "y": 8 }, "id": 15, "options": { "legend": { "calcs": [ "mean" ], "displayMode": "list", "placement": "bottom", "showLegend": true, "sortBy": "Mean", "sortDesc": true, "width": 200 }, "tooltip": { "mode": "multi", "sort": "desc" } }, "targets": [ { "datasource": { "type": "graphite", "uid": "${DS_NL-INFRA-CAP1_GRAPHITE}" }, "hide": false, "refId": "A", "target": "aliasByNode(dnsdist.$host.main.latency-avg1000, 1)" } ], "title": "Latency", "transparent": true, "type": "timeseries" }, { "datasource": { "type": "graphite", "uid": "${DS_NL-INFRA-CAP1_GRAPHITE}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "bars", "fillOpacity": 100, "gradientMode": "opacity", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "short" }, "overrides": [ { "matcher": { "id": "byName", "options": "1ms" }, "properties": [ { "id": "color", "value": { "fixedColor": "light-green", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "10ms" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-green", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "50ms" }, "properties": [ { "id": "color", "value": { "fixedColor": "light-blue", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "100ms" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-blue", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "1s" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-yellow", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "> 1s" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-red", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 7, "w": 16, "x": 8, "y": 8 }, "id": 8, "options": { "legend": { "calcs": [ "mean" ], "displayMode": "table", "placement": "right", "showLegend": true, "width": 200 }, "tooltip": { "mode": "multi", "sort": "desc" } }, "targets": [ { "datasource": { "type": "graphite", "uid": "${DS_NL-INFRA-CAP1_GRAPHITE}" }, "refCount": 0, "refId": "A", "target": "alias(perSecond(sumSeries(dnsdist.$host.main.latency0-1)), '1ms')" }, { "datasource": { "type": "graphite", "uid": "${DS_NL-INFRA-CAP1_GRAPHITE}" }, "hide": false, "refCount": 0, "refId": "B", "target": "alias(perSecond(sumSeries(dnsdist.$host.main.latency1-10)), '10ms')" }, { "datasource": { "type": "graphite", "uid": "${DS_NL-INFRA-CAP1_GRAPHITE}" }, "hide": false, "refCount": 0, "refId": "C", "target": "alias(perSecond(sumSeries(dnsdist.$host.main.latency10-50)), '50ms')" }, 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}

2023-02-06 08:34:16 UTC

2023-02-06 08:36:29 UTC

kafka-dashboard

Kafka Dashboard

Kafka resource usage and consumer lag overview

2023-03-14 05:37:07 UTC

2023-04-10 07:59:04 UTC

## 使用jmx_exporter对kafka进行监控 ## Use jmx_exporter to collect Kafka metrics 导入jmx_prometheus_javaagent-0.18.0.jar和config.yml Use jmx_prometheus_javaagent-0.18.0.jar and edit config.yml windows启动kafka-javaagent startwithagent.bat的启动脚本 Edit kafka-javaagent startwithagent.bat for Windows system to start kafka, Such as: ```powershell SET KAFKA_HOME=E:\kafka_2.12-2.8.1 SET KAFKA_OPTS=-javaagent:%KAFKA_HOME%\jmx_prometheus_javaagent-0.18.0.jar=6660:%KAFKA_HOME%\config.yml cd %KAFKA_HOME%\bin\windows kafka-server-start.bat %KAFKA_HOME%\config\server.properties ``` config.yml内容（如果不添加任何指定监控项为默认值）： ```yaml lowercaseOutputName: true rules: # Special cases and very specific rules - pattern : kafka.server<type=(.+), name=(.+), clientId=(.+), topic=(.+), partition=(.*)><>Value name: kafka_server_$1_$2 type: GAUGE labels: clientId: "$3" topic: "$4" partition: "$5" - pattern : kafka.server<type=(.+), name=(.+), clientId=(.+), brokerHost=(.+), brokerPort=(.+)><>Value name: kafka_server_$1_$2 type: GAUGE labels: clientId: "$3" broker: "$4:$5" - pattern : kafka.coordinator.(\w+)<type=(.+), name=(.+)><>Value name: kafka_coordinator_$1_$2_$3 type: GAUGE # Generic per-second counters with 0-2 key/value pairs - pattern: kafka.(\w+)<type=(.+), name=(.+)PerSec\w*, (.+)=(.+), (.+)=(.+)><>Count name: kafka_$1_$2_$3_total type: COUNTER labels: "$4": "$5" "$6": "$7" - pattern: kafka.(\w+)<type=(.+), name=(.+)PerSec\w*, (.+)=(.+)><>Count name: kafka_$1_$2_$3_total type: COUNTER labels: "$4": "$5" - pattern: kafka.(\w+)<type=(.+), name=(.+)PerSec\w*><>Count name: kafka_$1_$2_$3_total type: COUNTER - pattern: kafka.server<type=(.+), client-id=(.+)><>([a-z-]+) name: kafka_server_quota_$3 type: GAUGE labels: resource: "$1" clientId: "$2" - pattern: kafka.server<type=(.+), user=(.+), client-id=(.+)><>([a-z-]+) name: kafka_server_quota_$4 type: GAUGE labels: resource: "$1" user: "$2" clientId: "$3" # Generic gauges with 0-2 key/value pairs - pattern: kafka.(\w+)<type=(.+), name=(.+), (.+)=(.+), (.+)=(.+)><>Value name: kafka_$1_$2_$3 type: GAUGE labels: "$4": "$5" "$6": "$7" - pattern: kafka.(\w+)<type=(.+), name=(.+), (.+)=(.+)><>Value name: kafka_$1_$2_$3 type: GAUGE labels: "$4": "$5" - pattern: kafka.(\w+)<type=(.+), name=(.+)><>Value name: kafka_$1_$2_$3 type: GAUGE # Emulate Prometheus 'Summary' metrics for the exported 'Histogram's. # # Note that these are missing the '_sum' metric! - pattern: kafka.(\w+)<type=(.+), name=(.+), (.+)=(.+), (.+)=(.+)><>Count name: kafka_$1_$2_$3_count type: COUNTER labels: "$4": "$5" "$6": "$7" - pattern: kafka.(\w+)<type=(.+), name=(.+), (.+)=(.*), (.+)=(.+)><>(\d+)thPercentile name: kafka_$1_$2_$3 type: GAUGE labels: "$4": "$5" "$6": "$7" quantile: "0.$8" - pattern: kafka.(\w+)<type=(.+), name=(.+), (.+)=(.+)><>Count name: kafka_$1_$2_$3_count type: COUNTER labels: "$4": "$5" - pattern: kafka.(\w+)<type=(.+), name=(.+), (.+)=(.*)><>(\d+)thPercentile name: kafka_$1_$2_$3 type: GAUGE labels: "$4": "$5" quantile: "0.$6" - pattern: kafka.(\w+)<type=(.+), name=(.+)><>Count name: kafka_$1_$2_$3_count type: COUNTER - pattern: kafka.(\w+)<type=(.+), name=(.+)><>(\d+)thPercentile name: kafka_$1_$2_$3 type: GAUGE labels: quantile: "0.$4" ``` linux启动添加类似脚本或者改动启动文件： Edit `start.sh`: ```shell KAFKA_HOME=/xxx/kafka_2.12-2.8.1 export KAFKA_OPTS=-javaagent:$KAFKA_HOME/jmx_prometheus_javaagent-0.18.0.jar=6660:$KAFKA_HOME/config.yml $KAFKA_HOME/bin/kafka-server-start.sh -daemon $KAFKA_HOME/config/server.properties ``` ## 添加配置至prometheus ## Edit prometheus.yml ​ 在prometheus.yml文件中添加： ```yaml - job_name: "kafka-jmx" # metrics_path defaults to '/metrics' # scheme defaults to 'http'. static_configs: - targets: ["{Prometheus Server}:6660"] ``` 访问{Prometheus Server}6660/metrics查看指标 View http://{Prometheus Server}6660/metrics to checkout Kafka metrics

Other

Kafka resource usage and consumer lag overview

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"options": [], "query": { "query": "label_values(kafka_cluster_partition_replicascount{job=\"$kafka_job_name\"}, topic)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" } ] }, "time": { "from": "now-12h", "to": "now" }, "timepicker": { "refresh_intervals": [ "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "browser", "title": "Kafka Dashboard", "uid": "i8HLvrkiz", "version": 3, "weekStart": "" }

2023-03-14 05:37:07 UTC

2023-04-10 07:59:04 UTC

tuya-fe-visitor-monitor

tuya FE visitor monitor

nginx 日志监控

2022-09-30 08:47:21 UTC

2022-09-30 08:47:32 UTC

nginx 日志监控

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2022-09-30 08:47:21 UTC

fortigate-fgt200f

FORTIGATE FGT200F

Dashboard baseado em informações coletadas via SNMP no fortigate.

2023-02-14 13:47:18 UTC

2023-02-14 13:53:03 UTC

6s0xz6TTIpRQ7GD

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2023-02-14 13:47:18 UTC

2023-02-14 13:53:02 UTC

pm2-dashboard

PM2 Dashboard

pm2 metrics dashboard

2023-04-18 05:24:48 UTC

2023-04-18 05:25:33 UTC

pm2 metrics dashboard

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2023-04-18 05:24:48 UTC

windows-servers-status

Windows Servers Status Summary

Windows Server Status using Prometheus and windows_exporter

2023-05-03 10:22:10 UTC

2023-05-03 10:25:17 UTC

This dashboard for Windows Server Summary using Prometheus and windows_exporter

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2023-05-03 10:22:10 UTC

2023-05-03 10:25:17 UTC

summary

HEXAR VAL

venezuela val

2023-05-04 20:04:51 UTC

2023-05-04 20:05:44 UTC