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17,451 | 1 | 742,927 | 26,805 | 0 | 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 | 0 | metricbeat for nasuyun cloud elasticsearch
by nasuyun | 78,864 | 78,865 | Beats | null | 1 | 5 | 26,805 | 1 | 17,451 | 1 | 0 | System metrics from metricbeat to elasticsearch | {
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"hide": 0,
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"name": "hostname",
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"auto_min": "10s",
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"text": "1m",
"value": "1m"
},
"hide": 0,
"name": "Interval",
"options": [
{
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"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",
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"timezone": "browser",
"title": "metricbeat-system",
"uid": "ubFKKpdVz3",
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"weekStart": ""
} | 2022-11-21 10:38:37 UTC | 2022-11-25 13:26:19 UTC | 1 | 5 |
18,017 | 1 | 417,484 | 27,634 | 0 | 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 | 0 | 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
``` | 81,220 | 81,221 | null | null | 0 | 0 | 27,634 | 1 | 18,017 | 1 | 0 | Uses prometheus queries via pryorda/vmware_exporter | {
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} | 2023-02-01 02:58:17 UTC | null | 0 | 0 |
18,291 | 1 | 817,319 | 28,077 | 0 | geth-multiple | Geth: Multiple | Various metrics of multiple geth instances | 2023-03-17 09:13:23 UTC | 2023-03-17 09:15:23 UTC | 0 | 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. | 82,524 | 82,525 | null | null | 0 | 0 | 28,077 | 1 | 18,291 | 1 | 0 | null | {
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18,483 | 1 | 428,770 | 28,357 | 0 | 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 | 0 | null | 0 | 0 | null | null | 0 | 0 | 28,357 | 1 | 18,483 | 1 | 0 | Dashboard to track Filecoin Lotus node API endpoints. | {
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18,696 | 1 | 851,960 | 28,678 | 0 | 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 | 0 | null | 84,127 | 84,128 | Other | null | 0 | 0 | 28,678 | 1 | 18,696 | 1 | 0 | null | {
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17,516 | 1 | 753,275 | 26,900 | 0 | 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 | 0 | null | 0 | 0 | collectd | null | 0 | 0 | 26,900 | 1 | 17,516 | 1 | 0 | A Grafana dashboard compatible with all the cosmos-sdk and tendermint based blockchains. | {
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17,850 | 1 | 774,390 | 27,378 | 0 | 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 | 0 | null | 0 | 0 | null | null | 0 | 0 | 27,378 | 1 | 17,850 | 1 | 0 | Visualise power metrics coming from AHOY-DTU and OpenWeatherMap | {
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17,931 | 1 | 738,351 | 27,494 | 0 | traefik-dashboard | Traefik Dashboard | Visualizing Traefik Reverse Proxy Stats | 2023-01-22 22:01:03 UTC | 2023-01-22 22:05:10 UTC | 0 | Grafana dashboard to visualize various stats of a Traefik reverse proxy. | 80,924 | 80,925 | null | null | 0 | 0 | 27,494 | 1 | 17,931 | 1 | 0 | Visualizing Traefik Reverse Proxy Stats | {
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} | 2023-01-22 22:01:03 UTC | 2023-01-22 22:03:14 UTC | 0 | 0 |
18,134 | 1 | 474,105 | 27,840 | 0 | redpanda-default-dashboard | Redpanda Default Dashboard | Redpanda Default Dashboard | 2023-02-18 00:08:04 UTC | 2023-03-06 13:49:52 UTC | 0 | 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 | 82,222 | 82,223 | null | null | 0 | 0 | 27,840 | 1 | 18,134 | 1 | 0 | null | {
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"value": "cluster"
},
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"value": "instance"
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"text": "Instance,Shard",
"value": "instance,shard"
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"time": {
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"to": "now"
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} | 2023-02-18 00:08:04 UTC | 2023-02-18 00:08:46 UTC | 0 | 0 |
18,294 | 1 | 813,964 | 28,080 | 0 | 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 | 0 | 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"]
``` | 82,535 | 82,536 | Other | null | 0 | 0 | 28,080 | 1 | 18,294 | 1 | 0 | null | {
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18,548 | 1 | 838,138 | 28,435 | 0 | 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 | 0 | null | 0 | 0 | null | null | 0 | 0 | 28,435 | 1 | 18,548 | 1 | 0 | null | {
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"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": {
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"color": {
"mode": "thresholds"
},
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"options": {
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"result": {
"color": "red",
"index": 1
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"to": 0.5
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],
"thresholds": {
"mode": "percentage",
"steps": [
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"value": null
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}
},
"overrides": [
{
"matcher": {
"id": "byName",
"options": "Percent"
},
"properties": [
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"id": "unit",
"value": "percentunit"
}
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},
"gridPos": {
"h": 3,
"w": 5,
"x": 0,
"y": 12
},
"id": 6,
"options": {
"colorMode": "value",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
"lastNotNull"
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"fields": "",
"values": false
},
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},
"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": {
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"color": {
"mode": "thresholds"
},
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"result": {
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"index": 0
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"to": 1
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{
"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"
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"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": [
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"from": 0.5,
"result": {
"color": "green",
"index": 0
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"to": 1
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"type": "range"
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{
"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": [
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"fields": "/.*/",
"values": true
},
"textMode": "auto"
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"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"
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"fields": "/.*/",
"values": true
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"textMode": "auto"
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"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": [
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"from": 0.5,
"result": {
"color": "green",
"index": 0
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"to": 1
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{
"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": [
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"fields": "",
"values": false
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"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}"
},
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"defaults": {
"color": {
"mode": "thresholds"
},
"decimals": 0,
"mappings": [
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"from": 0.5,
"result": {
"color": "green",
"index": 0
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"to": 1
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"type": "range"
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{
"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": [
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"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}"
},
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"title": "VCS-Creation-001 - %Card Created",
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"id": 36,
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"resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Hub-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Hub-INSIGHTS"
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"fields": "/.*/",
"values": true
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"targets": [
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"resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Hub-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Hub-INSIGHTS"
},
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"timeGrain": "auto"
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"type": "grafana-azure-monitor-datasource",
"uid": "${DS_AZURE_MONITOR - SAAS_INFRA}"
},
"queryType": "Azure Log Analytics",
"refId": "A"
}
],
"title": "VCS-Delivery-002 - %DeliveryRemittanceEmail",
"type": "stat"
}
],
"refresh": false,
"schemaVersion": 37,
"style": "dark",
"tags": [],
"templating": {
"list": []
},
"time": {
"from": "now-30d",
"to": "now"
},
"timepicker": {},
"timezone": "America/New_York",
"title": "Avid Pay SLO Monitoring",
"uid": "iOxGpJx4k",
"version": 311,
"weekStart": "",
"gnetId": 18548,
"description": "Testing import AvidPay SLO Monitoring dashboard"
} | 2023-04-17 17:58:03 UTC | 2023-04-17 17:59:28 UTC | 0 | 0 |
17,234 | 1 | 377,006 | 26,470 | 0 | medidores | MEDIDORES | test 1 | 2022-10-20 13:25:50 UTC | 2022-10-20 13:27:30 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 26,470 | 1 | 17,234 | 1 | 0 | null | {
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"style": "dark",
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},
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"multi": false,
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"sort": 0,
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"hide": 0,
"includeAll": false,
"label": "MAQUINA",
"multi": false,
"name": "pmstations",
"options": [],
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"definition": "SELECT station FROM pmstations1 WHERE nombre='$pmstations'",
"hide": 2,
"includeAll": false,
"label": "",
"multi": false,
"name": "medidores",
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},
"time": {
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"weekStart": "",
"gnetId": 17234,
"description": "test 1"
} | 2022-10-20 13:25:50 UTC | 2022-10-20 13:27:30 UTC | 0 | 0 |
17,377 | 1 | 740,566 | 26,705 | 0 | 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 | 0 | 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. | 0 | 0 | Other | null | 0 | 0 | 26,705 | 1 | 17,377 | 1 | 0 | A dashboard for monitoring a Windows Desktop/Gaming PC. Using Prometheus, wmi_exporter, and OhmGraphite. | {
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} | 2022-11-09 03:47:05 UTC | 2022-11-09 03:56:22 UTC | 0 | 0 |
17,547 | 1 | 638,729 | 26,941 | 0 | 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 | 0 | ## 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
```
| 79,407 | 79,408 | Other | null | 0 | 0 | 26,941 | 1 | 17,547 | 1 | 0 | null | {
"__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",
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"version": "1.0.0"
},
{
"type": "panel",
"id": "piechart",
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{
"type": "panel",
"id": "table",
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"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"
}
]
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"editable": true,
"fiscalYearStartMonth": 0,
"graphTooltip": 0,
"id": null,
"links": [],
"liveNow": false,
"panels": [
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},
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"enableLogDetails": true,
"prettifyLogMessage": false,
"showCommonLabels": false,
"showLabels": false,
"showTime": false,
"sortOrder": "Descending",
"wrapLogMessage": false
},
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"uid": "${DS_LOKI}"
},
"editorMode": "code",
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"refId": "A"
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"hide": false,
"queryType": "range",
"refId": "B"
}
],
"title": "Firewall Base Search (do not delete)",
"transformations": [],
"type": "logs"
},
{
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"type": "loki",
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},
"gridPos": {
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"id": 20,
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"wrapLogMessage": false
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"hide": false,
"queryType": "range",
"refId": "IPv4-NoDrop-Query"
},
{
"datasource": {
"type": "loki",
"uid": "${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.*\" ",
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"editorMode": "code",
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"hide": false,
"queryType": "range",
"refId": "IPv6-NoDrop-Query"
},
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"datasource": {
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"hide": false,
"queryType": "range",
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}
],
"title": "IDS Base Search (do not delete)",
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"type": "logs"
},
{
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"w": 24,
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},
"id": 27,
"title": "Overview",
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},
{
"datasource": {
"type": "loki",
"uid": "${DS_LOKI}"
},
"fieldConfig": {
"defaults": {
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},
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"steps": [
{
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},
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},
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},
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"queryType": "range",
"refId": "A"
}
],
"title": "Firewall Logs",
"type": "stat"
},
{
"datasource": {
"type": "loki",
"uid": "${DS_LOKI}"
},
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"mode": "thresholds"
},
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"targets": [
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"queryType": "range",
"refId": "A"
}
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"title": "IDS Events",
"type": "stat"
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{
"datasource": {
"type": "datasource",
"uid": "-- Dashboard --"
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"title": "Firewall Connections",
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"overrides": [
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"matcher": {
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"options": "block"
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"mode": "fixed"
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"gridPos": {
"h": 8,
"w": 4,
"x": 4,
"y": 6
},
"id": 30,
"options": {
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],
"legend": {
"displayMode": "table",
"placement": "right",
"showLegend": false,
"values": [
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]
},
"pieType": "pie",
"reduceOptions": {
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"fields": "",
"values": true
},
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"targets": [
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"uid": "-- Dashboard --"
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"panelId": 23,
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}
],
"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": {
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"Action": {
"aggregations": []
},
"Direction": {
"aggregations": [],
"operation": "groupby"
},
"Line": {
"aggregations": [
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],
"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": {
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},
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}
],
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}
}
],
"type": "logs"
},
{
"datasource": {
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"uid": "-- Dashboard --"
},
"description": "",
"gridPos": {
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},
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"title": "Allowed by IDS/IPS Rules",
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},
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},
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"expr": "{job=\"syslog-ids\",host=\"$host\"} \n|~ \"$searchregex\"\n!~ \"\\\\[\\\\d:\\\\d+:\\\\d\\\\]\"",
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],
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}
],
"title": "Others",
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}
],
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"schemaVersion": 37,
"style": "dark",
"tags": [
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"Firewall",
"IDS/IPS",
"OPNsense"
],
"templating": {
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{
"current": {},
"datasource": {
"type": "loki",
"uid": "${DS_LOKI}"
},
"definition": "label_values({job=\"syslog-ids\"}, host)",
"hide": 0,
"includeAll": false,
"label": "Host",
"multi": false,
"name": "host",
"options": [],
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{
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"value": ""
},
"hide": 0,
"label": "Search-Regex",
"name": "searchregex",
"options": [
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}
],
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},
"time": {
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"to": "now"
},
"timepicker": {},
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"description": "Dashboards to monitor Firewall Rules & IDS/IPS potential Attacks."
} | 2022-12-06 14:26:22 UTC | 2022-12-06 14:49:07 UTC | 0 | 0 |
17,288 | 1 | 724,048 | 26,578 | 0 | 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 | 0 | 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). | 0 | 0 | Other | null | 0 | 0 | 26,578 | 1 | 17,288 | 1 | 0 | null | {
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"linkTooltip": "Drill down",
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18,202 | 1 | 806,130 | 27,946 | 0 | nextcloud | Nextcloud | Dashboard for Nextcloud exporter | 2023-03-01 13:24:39 UTC | 2023-03-01 13:25:41 UTC | 0 | Needs node-exporter, cadvisor, and nextcloud-exporter. | 0 | 0 | null | null | 0 | 0 | 27,946 | 1 | 18,202 | 1 | 0 | null | {
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"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": "${DS_LOKI-ADAM}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisLabel": "%",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 71,
"gradientMode": "opacity",
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},
"lineInterpolation": "smooth",
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"fill": "solid"
},
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},
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"group": "A",
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},
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"mode": "off"
}
},
"decimals": 0,
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"max": 50,
"min": 0,
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{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
}
},
"overrides": [
{
"matcher": {
"id": "byName",
"options": "/hello"
},
"properties": [
{
"id": "color",
"value": {
"fixedColor": "red",
"mode": "fixed"
}
}
]
},
{
"matcher": {
"id": "byName",
"options": "/observability"
},
"properties": [
{
"id": "color",
"value": {
"fixedColor": "semi-dark-purple",
"mode": "fixed"
}
}
]
}
]
},
"gridPos": {
"h": 7,
"w": 8,
"x": 8,
"y": 15
},
"id": 15,
"options": {
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"displayMode": "list",
"placement": "bottom"
},
"tooltip": {
"mode": "single",
"sort": "none"
}
},
"targets": [
{
"datasource": {
"type": "loki",
"uid": "${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 | 0 | 0 |
18,389 | 1 | 603,671 | 28,215 | 0 | 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 | 0 | # Overview.
This dashboard is designed to visualize the sensor data collected using Telegraf `inputs.docker`. | 82,885 | 82,886 | Telegraf | null | 0 | 0 | 28,215 | 1 | 18,389 | 1 | 0 | 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",
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},
{
"type": "datasource",
"id": "influxdb",
"name": "InfluxDB",
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},
{
"type": "panel",
"id": "stat",
"name": "Stat",
"version": ""
},
{
"type": "panel",
"id": "timeseries",
"name": "Time series",
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}
],
"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": "${DS_DROPET-INFLUXDB}"
},
"fieldConfig": {
"defaults": {
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},
"mappings": [],
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"steps": [
{
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]
},
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},
"gridPos": {
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},
"id": 8,
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],
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"values": false
},
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},
"pluginVersion": "9.4.7",
"targets": [
{
"datasource": {
"type": "influxdb",
"uid": "${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"
},
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]
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"gridPos": {
"h": 5,
"w": 3,
"x": 3,
"y": 0
},
"id": 11,
"options": {
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"orientation": "auto",
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"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": "${DS_DROPET-INFLUXDB}"
},
"fieldConfig": {
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},
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"steps": [
{
"color": "green",
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}
]
},
"unit": "decbytes"
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"overrides": []
},
"gridPos": {
"h": 5,
"w": 3,
"x": 6,
"y": 0
},
"id": 7,
"options": {
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"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"textMode": "auto"
},
"pluginVersion": "9.4.7",
"targets": [
{
"datasource": {
"type": "influxdb",
"uid": "${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": "${DS_DROPET-INFLUXDB}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "orange",
"value": null
}
]
}
},
"overrides": []
},
"gridPos": {
"h": 5,
"w": 3,
"x": 12,
"y": 0
},
"id": 5,
"options": {
"colorMode": "value",
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"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"textMode": "auto"
},
"pluginVersion": "9.4.7",
"targets": [
{
"datasource": {
"type": "influxdb",
"uid": "${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"
},
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} | 2023-03-30 04:17:51 UTC | 2023-03-30 04:22:05 UTC | 0 | 0 |
18,507 | 1 | 502,618 | 28,385 | 0 | 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 | 0 | null | 83,310 | 83,311 | null | null | 0 | 0 | 28,385 | 1 | 18,507 | 1 | 0 | null | {
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} | 2023-04-12 20:59:43 UTC | 2023-04-12 21:13:12 UTC | 0 | 0 |
17,481 | 1 | 722,446 | 26,843 | 0 | 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 | 0 | null | 0 | 0 | null | null | 0 | 0 | 26,843 | 1 | 17,481 | 1 | 0 | Monitor your Bitcoin core node and aspects of the network. | {
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},
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"to": "now"
},
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} | 2022-11-25 02:13:04 UTC | null | 0 | 0 |
17,832 | 1 | 705,056 | 27,346 | 0 | 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 | 0 | # 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 $HTDOCS
CMD exec python3 -m http.server 8080
HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=1 CMD curl --fail --silent --show-error http://localhost:8080 || exit 1
LABEL HOW_TO_BUILD="BUILDKIT=y docker build --file Dockerfile --tag static_server ."
LABEL HOW_TO_RUN="docker run --init --detach --name home_fitness --publish 8080:8080 --restart=always --volume /home/josevnz/fitness:/var/www/htdocs:ro static_server"
```
You can see a [demonstration video here](https://youtu.be/RQYhDJw9wtI)
| 80,462 | 80,463 | Other | null | 0 | 0 | 27,346 | 1 | 17,832 | 1 | 0 | Visualization of NYRR.org race result export | {
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"uid": "${DS_NYRR.ORG_RESULTS}"
},
"fieldConfig": {
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},
"mappings": [],
"thresholds": {
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"steps": [
{
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"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "percent"
},
"overrides": []
},
"gridPos": {
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"w": 6,
"x": 0,
"y": 0
},
"id": 5,
"options": {
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"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
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],
"fields": "",
"values": false
},
"textMode": "value_and_name"
},
"pluginVersion": "9.3.2",
"targets": [
{
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"uid": "${DS_NYRR.ORG_RESULTS}"
},
"decimalSeparator": ".",
"delimiter": ",",
"header": true,
"ignoreUnknown": false,
"refId": "A",
"schema": [
{
"name": "Age-Graded Percent",
"type": "number"
}
],
"skipRows": 0
}
],
"title": "Best",
"type": "stat"
},
{
"datasource": {
"type": "marcusolsson-csv-datasource",
"uid": "${DS_NYRR.ORG_RESULTS}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"custom": {
"align": "auto",
"displayMode": "color-text",
"filterable": true,
"inspect": true
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "#EAB839",
"value": 50
}
]
},
"unit": "percent"
},
"overrides": [
{
"matcher": {
"id": "byName",
"options": "Count"
},
"properties": [
{
"id": "unit"
}
]
},
{
"matcher": {
"id": "byName",
"options": "Age-Graded Percent (max)"
},
"properties": [
{
"id": "custom.displayMode",
"value": "lcd-gauge"
}
]
},
{
"matcher": {
"id": "byName",
"options": "Age-Graded Percent (min)"
},
"properties": [
{
"id": "custom.displayMode",
"value": "lcd-gauge"
}
]
}
]
},
"gridPos": {
"h": 10,
"w": 18,
"x": 6,
"y": 0
},
"id": 7,
"options": {
"footer": {
"fields": "",
"reducer": [
"sum"
],
"show": false
},
"showHeader": true
},
"pluginVersion": "9.3.2",
"targets": [
{
"datasource": {
"type": "marcusolsson-csv-datasource",
"uid": "${DS_NYRR.ORG_RESULTS}"
},
"decimalSeparator": ".",
"delimiter": ",",
"header": true,
"ignoreUnknown": false,
"refId": "A",
"schema": [
{
"name": "Distance",
"type": "string"
}
],
"skipRows": 0
}
],
"title": "By distance",
"transformations": [
{
"id": "groupBy",
"options": {
"fields": {
"Age-Graded Percent": {
"aggregations": [
"max",
"min",
"count"
],
"operation": "aggregate"
},
"Distance": {
"aggregations": [],
"operation": "groupby"
}
}
}
},
{
"id": "sortBy",
"options": {
"fields": {},
"sort": [
{
"desc": true,
"field": "Age-Graded Percent (max)"
}
]
}
},
{
"id": "organize",
"options": {
"excludeByName": {},
"indexByName": {},
"renameByName": {
"Age-Graded Percent (count)": "Count",
"Age-Graded Percent (min)": ""
}
}
}
],
"type": "table"
},
{
"datasource": {
"type": "marcusolsson-csv-datasource",
"uid": "${DS_NYRR.ORG_RESULTS}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "percent"
},
"overrides": []
},
"gridPos": {
"h": 5,
"w": 6,
"x": 0,
"y": 5
},
"id": 6,
"options": {
"colorMode": "value",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
"min"
],
"fields": "",
"values": false
},
"textMode": "value_and_name"
},
"pluginVersion": "9.3.2",
"targets": [
{
"datasource": {
"type": "marcusolsson-csv-datasource",
"uid": "${DS_NYRR.ORG_RESULTS}"
},
"decimalSeparator": ".",
"delimiter": ",",
"header": true,
"ignoreUnknown": false,
"refId": "A",
"schema": [
{
"name": "Age-Graded Percent",
"type": "number"
}
],
"skipRows": 0
}
],
"title": "Worst",
"type": "stat"
},
{
"datasource": {
"type": "marcusolsson-csv-datasource",
"uid": "${DS_NYRR.ORG_RESULTS}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "fixed"
},
"custom": {
"align": "auto",
"displayMode": "auto",
"filterable": true,
"inspect": true
},
"links": [],
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
}
]
}
},
"overrides": [
{
"matcher": {
"id": "byName",
"options": "Event Name"
},
"properties": [
{
"id": "links",
"value": [
{
"targetBlank": true,
"title": "Race Calendar",
"url": "https://www.nyrr.org/run/race-calendar"
}
]
},
{
"id": "links",
"value": []
},
{
"id": "custom.width",
"value": 342
}
]
},
{
"matcher": {
"id": "byName",
"options": "Age-Graded Time"
},
"properties": [
{
"id": "links",
"value": [
{
"targetBlank": true,
"title": "A guide to age grading",
"url": "https://www.runnersworld.com/advanced/a20794116/a-guide-to-age-grading/"
}
]
}
]
},
{
"matcher": {
"id": "byName",
"options": "Age-Graded Percent"
},
"properties": [
{
"id": "unit",
"value": "percent"
}
]
},
{
"matcher": {
"id": "byName",
"options": "Age-Group Place"
},
"properties": [
{
"id": "links",
"value": [
{
"targetBlank": true,
"title": "Why age group place results are important",
"url": "https://runnerclick.com/why-age-group-place-results-at-races-are-important/"
}
]
}
]
},
{
"matcher": {
"id": "byName",
"options": "Event Date"
},
"properties": [
{
"id": "unit",
"value": "dateTimeAsIso"
}
]
}
]
},
"gridPos": {
"h": 8,
"w": 24,
"x": 0,
"y": 10
},
"id": 2,
"options": {
"footer": {
"enablePagination": true,
"fields": "",
"reducer": [
"sum"
],
"show": false
},
"frameIndex": 7,
"showHeader": true,
"sortBy": [
{
"desc": true,
"displayName": "Distance"
}
]
},
"pluginVersion": "9.3.2",
"targets": [
{
"datasource": {
"type": "marcusolsson-csv-datasource",
"uid": "${DS_NYRR.ORG_RESULTS}"
},
"decimalSeparator": ".",
"delimiter": ",",
"header": true,
"ignoreUnknown": true,
"refId": "A",
"schema": [
{
"name": "Event Name",
"type": "string"
},
{
"name": "Event Date",
"type": "time"
},
{
"name": "Distance",
"type": "string"
},
{
"name": "Finish Time",
"type": "string"
},
{
"name": "Pace",
"type": "string"
},
{
"name": "Gun Time",
"type": "string"
},
{
"name": "Overall Place",
"type": "number"
},
{
"name": "Gender Place",
"type": "number"
},
{
"name": "Age-Group Place",
"type": "number"
},
{
"name": "Age-Graded Time",
"type": "string"
},
{
"name": "Age-Graded Place",
"type": "number"
},
{
"name": "Age-Graded Percent",
"type": "number"
}
],
"skipRows": 0
}
],
"title": "NYRR.org overall results",
"transformations": [
{
"id": "sortBy",
"options": {
"fields": {},
"sort": [
{
"desc": false,
"field": "Age-Graded Percent"
}
]
}
},
{
"id": "organize",
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"indexByName": {
"Age-Group Place": 8,
"Distance": 1,
"Event Date": 0,
"Event Name": 2,
"Finish Time": 3,
"Gender Place": 7,
"Gun Time": 5,
"Overall Place": 6,
"Pace": 4
},
"renameByName": {
"Finish Time": "",
"Pace": "Pace (minutes/mile)"
}
}
},
{
"id": "partitionByValues",
"options": {
"fields": [
"Distance"
]
}
}
],
"type": "table"
},
{
"datasource": {
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"uid": "${DS_NYRR.ORG_RESULTS}"
},
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"mode": "continuous-GrYlRd",
"seriesBy": "last"
},
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"axisColorMode": "text",
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 20,
"gradientMode": "scheme",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"lineInterpolation": "smooth",
"lineStyle": {
"fill": "solid"
},
"lineWidth": 3,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "auto",
"spanNulls": 3600000,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
}
]
}
},
"overrides": []
},
"gridPos": {
"h": 10,
"w": 24,
"x": 0,
"y": 18
},
"id": 3,
"options": {
"legend": {
"calcs": [
"count"
],
"displayMode": "list",
"placement": "bottom",
"showLegend": true
},
"tooltip": {
"mode": "single",
"sort": "none"
}
},
"pluginVersion": "9.3.2",
"targets": [
{
"datasource": {
"type": "marcusolsson-csv-datasource",
"uid": "${DS_NYRR.ORG_RESULTS}"
},
"decimalSeparator": ".",
"delimiter": ",",
"header": true,
"ignoreUnknown": true,
"refId": "A",
"schema": [
{
"name": "Event Date",
"type": "time"
},
{
"name": "Age-Graded Percent",
"type": "number"
}
],
"skipRows": 0,
"timezone": "America/New_York"
}
],
"title": "NYRR.org races",
"transformations": [
{
"id": "convertFieldType",
"options": {
"conversions": [
{
"dateFormat": "MM-DD-YYYY",
"destinationType": "time",
"targetField": "Event Date"
}
],
"fields": {}
}
}
],
"type": "timeseries"
}
],
"schemaVersion": 37,
"style": "dark",
"tags": [
"running",
"fitness",
"nyrr"
],
"templating": {
"list": []
},
"time": {
"from": "now-15y",
"to": "now"
},
"timepicker": {
"refresh_intervals": [
"2h",
"1d"
]
},
"timezone": "America/New_York",
"title": "NYRR",
"uid": "vPBMUrpVz",
"version": 23,
"weekStart": "",
"gnetId": 17832
} | 2023-01-08 21:22:12 UTC | 2023-01-08 21:30:31 UTC | 0 | 0 |
18,316 | 1 | 683,756 | 28,127 | 0 | 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 | 0 | # 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"
``` | 0 | 0 | null | null | 0 | 0 | 28,127 | 1 | 18,316 | 1 | 0 | This dashboard works with postgres_exporter for prometheus | {
"__inputs": [
{
"name": "DS_PROMETHEUS",
"label": "Prometheus",
"description": "postgresql_exporter",
"type": "datasource",
"pluginId": "prometheus",
"pluginName": "Prometheus"
}
],
"__elements": {},
"__requires": [
{
"type": "grafana",
"id": "grafana",
"name": "Grafana",
"version": "9.4.3"
},
{
"type": "panel",
"id": "graph",
"name": "Graph (old)",
"version": ""
},
{
"type": "datasource",
"id": "prometheus",
"name": "Prometheus",
"version": "1.0.0"
},
{
"type": "panel",
"id": "stat",
"name": "Stat",
"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": "This dashboard works with postgres_exporter for prometheus",
"editable": true,
"fiscalYearStartMonth": 0,
"gnetId": 18316,
"graphTooltip": 0,
"id": null,
"links": [],
"liveNow": false,
"panels": [
{
"collapsed": false,
"datasource": {
"type": "prometheus",
"uid": "bBFL4Ra4z"
},
"gridPos": {
"h": 1,
"w": 24,
"x": 0,
"y": 0
},
"id": 34,
"panels": [],
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "bBFL4Ra4z"
},
"refId": "A"
}
],
"title": "General Counters, CPU, Memory and File Descriptor Stats",
"type": "row"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"mappings": [
{
"options": {
"1": {
"index": 0,
"text": "运行中"
}
},
"type": "value"
}
],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "semi-dark-red",
"value": null
},
{
"color": "dark-green",
"value": 1
}
]
},
"unit": "none"
},
"overrides": []
},
"gridPos": {
"h": 3,
"w": 3,
"x": 0,
"y": 1
},
"id": 71,
"links": [],
"maxDataPoints": 100,
"options": {
"colorMode": "background",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "horizontal",
"reduceOptions": {
"calcs": [
"mean"
],
"fields": "",
"values": false
},
"text": {},
"textMode": "value"
},
"pluginVersion": "9.4.3",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"exemplar": true,
"expr": "pg_up{instance=\"$instance\"}",
"format": "time_series",
"instant": true,
"interval": "",
"intervalFactor": 1,
"legendFormat": "",
"refId": "A"
}
],
"title": "Status",
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"mappings": [
{
"options": {
"match": "null",
"result": {
"text": "N/A"
}
},
"type": "special"
}
],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "dark-green",
"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "none"
},
"overrides": []
},
"gridPos": {
"h": 3,
"w": 3,
"x": 3,
"y": 1
},
"id": 36,
"links": [],
"maxDataPoints": 100,
"options": {
"colorMode": "background",
"graphMode": "none",
"justifyMode": "auto",
"orientation": "horizontal",
"reduceOptions": {
"calcs": [
"mean"
],
"fields": "",
"values": false
},
"text": {},
"textMode": "name"
},
"pluginVersion": "9.4.3",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"exemplar": true,
"expr": "pg_static{instance=\"$instance\"}",
"format": "time_series",
"instant": true,
"interval": "",
"intervalFactor": 1,
"legendFormat": "{{short_version}}",
"refId": "A"
}
],
"title": "Version",
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"mappings": [
{
"options": {
"match": "null",
"result": {
"text": "N/A"
}
},
"type": "special"
}
],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "blue",
"value": null
}
]
},
"unit": "bytes"
},
"overrides": []
},
"gridPos": {
"h": 3,
"w": 2,
"x": 6,
"y": 1
},
"id": 46,
"links": [],
"maxDataPoints": 100,
"options": {
"colorMode": "background",
"graphMode": "none",
"justifyMode": "auto",
"orientation": "horizontal",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"text": {},
"textMode": "auto"
},
"pluginVersion": "9.4.3",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"expr": "pg_settings_work_mem_bytes{instance=\"$instance\"}",
"format": "time_series",
"intervalFactor": 1,
"legendFormat": "",
"refId": "A"
}
],
"title": "Work Mem",
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"mappings": [
{
"options": {
"match": "null",
"result": {
"text": "N/A"
}
},
"type": "special"
}
],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "dark-blue",
"value": null
}
]
},
"unit": "bytes"
},
"overrides": []
},
"gridPos": {
"h": 3,
"w": 2,
"x": 8,
"y": 1
},
"id": 40,
"links": [],
"maxDataPoints": 100,
"options": {
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"graphMode": "area",
"justifyMode": "center",
"orientation": "horizontal",
"reduceOptions": {
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"lastNotNull"
],
"fields": "",
"values": false
},
"text": {},
"textMode": "auto"
},
"pluginVersion": "9.4.3",
"targets": [
{
"datasource": {
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"uid": "${DS_PROMETHEUS}"
},
"expr": "pg_settings_shared_buffers_bytes{instance=\"$instance\"}",
"format": "time_series",
"intervalFactor": 1,
"refId": "A"
}
],
"title": "Shared Buffers",
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"description": "Active Connections",
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"mappings": [
{
"options": {
"match": "null",
"result": {
"text": "N/A"
}
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}
],
"thresholds": {
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"steps": [
{
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},
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}
]
},
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},
"overrides": []
},
"gridPos": {
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"w": 3,
"x": 10,
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},
"id": 38,
"links": [],
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} | 2023-03-29 08:22:52 UTC | null | 0 | 0 |
18,459 | 1 | 831,881 | 28,324 | 0 | celestia-appd | Celestia Appd | Then most important metrics. | 2023-04-07 10:01:55 UTC | 2023-04-10 02:21:11 UTC | 0 | Set your Prometheus job (the one connect to celestia-appd) name to 'validator', then it should work. | 83,221 | 83,222 | Other | null | 0 | 0 | 28,324 | 1 | 18,459 | 1 | 0 | Golang internal statistics. | {
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"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 | 0 | 0 |
18,504 | 1 | 835,147 | 28,382 | 0 | 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 | 0 | This is a dashboard that will work for any project instrumented with [Autometrics](https://autometrics.dev). | 83,306 | 83,307 | Other | null | 0 | 0 | 28,382 | 1 | 18,504 | 1 | 0 | null | {
"__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": "${DS_PROMETHEUS}"
},
"description": "Calls per second is calculated as the average over a 5-minute window",
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisCenteredZero": false,
"axisColorMode": "text",
"axisLabel": "Calls per Second",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 10,
"gradientMode": "none",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"lineInterpolation": "linear",
"lineWidth": 1,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "never",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
},
"mappings": [],
"min": 0,
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "none"
},
"overrides": []
},
"gridPos": {
"h": 8,
"w": 24,
"x": 0,
"y": 0
},
"id": 4,
"options": {
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"calcs": [
"lastNotNull",
"max"
],
"displayMode": "table",
"placement": "right",
"showLegend": true,
"sortBy": "Max",
"sortDesc": true
},
"tooltip": {
"mode": "multi",
"sort": "desc"
}
},
"pluginVersion": "9.4.1",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${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": {
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}
},
"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 | 0 | 0 |
17,472 | 1 | 749,837 | 26,834 | 0 | 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 | 0 | 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 | 78,999 | 79,000 | Other | null | 1 | 5 | 26,834 | 1 | 17,472 | 1 | 0 | null | {
"__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": {},
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"type": "grafana",
"id": "grafana",
"name": "Grafana",
"version": "9.0.3"
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"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": [
{
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"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": {
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},
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"value": null
}
]
},
"unit": "percentunit"
},
"overrides": []
},
"gridPos": {
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"w": 4,
"x": 12,
"y": 1
},
"id": 39,
"options": {
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"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
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],
"fields": "",
"values": false
},
"text": {
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},
"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": {
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},
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"thresholds": {
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"steps": [
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}
]
},
"unit": "bytes"
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"overrides": []
},
"gridPos": {
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"w": 4,
"x": 16,
"y": 1
},
"id": 14,
"options": {
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"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
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],
"fields": "",
"values": false
},
"text": {
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},
"textMode": "auto"
},
"pluginVersion": "9.0.3",
"targets": [
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"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}))}))",
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"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})) ",
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} | 2022-11-24 15:19:31 UTC | 2022-11-25 08:45:51 UTC | 1 | 5 |
18,151 | 1 | 801,052 | 27,876 | 0 | 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 | 0 | null | 0 | 0 | null | null | 0 | 0 | 27,876 | 1 | 18,151 | 1 | 0 | null | {
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} | 2023-03-08 07:58:02 UTC | 2023-03-08 08:02:27 UTC | 0 | 0 |
18,366 | 1 | 428,770 | 28,188 | 0 | 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 | 0 | null | 0 | 0 | null | null | 0 | 0 | 28,188 | 1 | 18,366 | 1 | 0 | Dashboard to track Filecoin Lotus node API endpoints. | {
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"interval": "",
"legendFormat": "{{pod}}",
"range": true,
"refId": "Height"
}
],
"title": "Synced height",
"transformations": [],
"transparent": true,
"type": "heatmap-new"
}
],
"refresh": false,
"schemaVersion": 36,
"style": "dark",
"tags": [],
"templating": {
"list": [
{
"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"
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"refresh": 2,
"regex": "/.*pod=\"(.*?)\"/",
"skipUrlSync": false,
"sort": 1,
"tagValuesQuery": "",
"tagsQuery": "",
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]
},
"time": {
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"to": "now"
},
"timepicker": {
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"1m",
"5m",
"15m",
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]
},
"timezone": "",
"title": "Raúl's dashboard -- NODE SYNC",
"uid": "Qn8_doBVk",
"version": 7,
"weekStart": ""
} | 2023-03-27 07:16:36 UTC | null | 0 | 0 |
18,442 | 1 | 828,810 | 28,292 | 0 | pint-dashboard | Pint Dashboard | Lint your Prometheus | 2023-04-05 06:57:33 UTC | 2023-04-05 06:58:18 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 28,292 | 1 | 18,442 | 1 | 0 | Lint your Prometheus | {
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],
"__elements": {},
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"id": "bargauge",
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"version": ""
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{
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"id": "grafana",
"name": "Grafana",
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{
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"type": "panel",
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],
"annotations": {
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"builtIn": 1,
"datasource": {
"type": "grafana",
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"enable": true,
"hide": true,
"iconColor": "rgba(0, 211, 255, 1)",
"name": "Annotations & Alerts",
"target": {
"limit": 100,
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"tags": [],
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},
"description": "Lint your Prometheus",
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"title": "Alerting Problems",
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"type": "stat"
},
{
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"type": "prometheus",
"uid": "${DS_PROM-CLOUDSCALE-THANOS-PROD}"
},
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"options": "name"
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"value": 217
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}
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"operation": "groupby"
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"indexByName": {
"kind": 2,
"name": 0,
"problem": 1
},
"renameByName": {}
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"type": "prometheus",
"uid": "${DS_PROM-CLOUDSCALE-THANOS-PROD}"
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}
]
}
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},
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"y": 8
},
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"displayMode": "gradient",
"minVizHeight": 10,
"minVizWidth": 0,
"orientation": "horizontal",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"showUnfilled": true
},
"pluginVersion": "9.4.3",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROM-CLOUDSCALE-THANOS-PROD}"
},
"editorMode": "builder",
"expr": "pint_check_duration_seconds_sum",
"legendFormat": "{{check}}",
"range": true,
"refId": "A"
}
],
"title": "Check Duration",
"type": "bargauge"
}
],
"refresh": "",
"revision": 1,
"schemaVersion": 38,
"style": "dark",
"tags": [],
"templating": {
"list": []
},
"time": {
"from": "now-6h",
"to": "now"
},
"timepicker": {},
"timezone": "",
"title": "Pint Dashboard",
"uid": "oOK-SEfVk",
"version": 6,
"weekStart": "",
"gnetId": 18442
} | 2023-04-05 06:57:33 UTC | 2023-04-05 06:58:17 UTC | 0 | 0 |
18,847 | 1 | 866,675 | 28,906 | 0 | 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 | 0 | # 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 | 84,731 | 84,732 | Other | null | 0 | 0 | 28,906 | 1 | 18,847 | 1 | 0 | MongoDB -> Mongodb_Exporter -> Prometheus -> Grafana. | {
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"name": "DS_PROMETHEUS",
"label": "Prometheus",
"description": "Utilisation de mongoDB_extractor 2.37.0",
"type": "datasource",
"pluginId": "prometheus",
"pluginName": "Prometheus"
}
],
"__elements": {},
"__requires": [
{
"type": "panel",
"id": "gauge",
"name": "Gauge",
"version": ""
},
{
"type": "grafana",
"id": "grafana",
"name": "Grafana",
"version": "9.5.2"
},
{
"type": "panel",
"id": "graph",
"name": "Graph (old)",
"version": ""
},
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"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": {
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"builtIn": 1,
"datasource": {
"type": "datasource",
"uid": "grafana"
},
"enable": true,
"hide": true,
"iconColor": "rgba(0, 211, 255, 1)",
"name": "Annotations & Alerts",
"type": "dashboard"
}
]
},
"description": "MongoDB -> Mongodb_Exporter -> Prometheus -> Grafana.",
"editable": false,
"fiscalYearStartMonth": 0,
"gnetId": 18847,
"graphTooltip": 1,
"id": null,
"links": [],
"liveNow": false,
"panels": [
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"collapsed": false,
"datasource": {
"type": "prometheus",
"uid": "a51b9adf-aa64-4765-927a-75a7d3d13ac1"
},
"gridPos": {
"h": 1,
"w": 24,
"x": 0,
"y": 0
},
"id": 22,
"panels": [],
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"repeatDirection": "h",
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"datasource": {
"type": "prometheus",
"uid": "a51b9adf-aa64-4765-927a-75a7d3d13ac1"
},
"refId": "A"
}
],
"title": "Ressources Globales $instance",
"type": "row"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"description": "Affiche le statut des instances.",
"fieldConfig": {
"defaults": {
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{
"from": "0",
"id": 1,
"operator": "",
"text": "DOWN",
"to": "0.9",
"type": 2,
"value": ""
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{
"from": "",
"id": 2,
"operator": "",
"text": "UP",
"to": "",
"type": 1,
"value": "1"
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"thresholds": {
"mode": "absolute",
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"color": "dark-red",
"value": null
},
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"color": "green",
"value": 1
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"unit": "short"
},
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},
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"x": 0,
"y": 1
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"id": 27,
"options": {
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"lastNotNull"
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"fields": "",
"values": false
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"showThresholdMarkers": false
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"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"editorMode": "code",
"expr": "mongodb_up{instance=~\"$instance\"}",
"format": "time_series",
"instant": false,
"interval": "",
"legendFormat": "",
"refId": "A"
}
],
"title": "Instance",
"type": "gauge"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"description": "Disponibilité du serveur.",
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"mode": "thresholds"
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"mappings": [
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"options": {
"match": "null",
"result": {
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"type": "special"
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"thresholds": {
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"steps": [
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"color": "rgba(245, 54, 54, 0.9)",
"value": null
},
{
"color": "rgba(237, 129, 40, 0.89)",
"value": 0
},
{
"color": "rgba(50, 172, 45, 0.97)",
"value": 360
}
]
},
"unit": "s"
},
"overrides": []
},
"gridPos": {
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"w": 4,
"x": 4,
"y": 1
},
"id": 10,
"links": [],
"maxDataPoints": 100,
"options": {
"colorMode": "value",
"graphMode": "none",
"justifyMode": "auto",
"orientation": "horizontal",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"textMode": "auto"
},
"pluginVersion": "9.5.2",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"editorMode": "code",
"expr": "mongodb_ss_uptime{instance=~\"$instance\"}",
"format": "time_series",
"intervalFactor": 2,
"legendFormat": "",
"range": true,
"refId": "A",
"step": 1800
}
],
"title": "Uptime",
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"description": "Nombre de CPU",
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "blue",
"value": null
}
]
},
"unit": "none"
},
"overrides": []
},
"gridPos": {
"h": 4,
"w": 4,
"x": 8,
"y": 1
},
"id": 37,
"options": {
"colorMode": "value",
"graphMode": "none",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
"mean"
],
"fields": "",
"values": false
},
"textMode": "auto"
},
"pluginVersion": "9.5.2",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"editorMode": "code",
"expr": "mongodb_sys_cpu_num_cpus{instance=~\"$instance\"}",
"format": "time_series",
"instant": false,
"key": "Q-c4651da4-4c67-4857-a730-7aeffab5e38a-0",
"legendFormat": "__auto",
"range": true,
"refId": "A"
}
],
"title": "CPU",
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"description": "Charge du CPU pour le système.",
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
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"value": null
},
{
"color": "orange",
"value": 70
},
{
"color": "red",
"value": 90
}
]
},
"unit": "percent"
},
"overrides": []
},
"gridPos": {
"h": 4,
"w": 4,
"x": 12,
"y": 1
},
"id": 63,
"options": {
"colorMode": "value",
"graphMode": "none",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
"mean"
],
"fields": "",
"values": false
},
"textMode": "auto"
},
"pluginVersion": "9.5.2",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"editorMode": "code",
"exemplar": false,
"expr": "rate(mongodb_sys_cpu_system_ms{instance=~\"$instance\"}[$__rate_interval])/mongodb_sys_cpu_num_cpus{instance=~\"$instance\"}/10",
"format": "time_series",
"instant": false,
"key": "Q-c4651da4-4c67-4857-a730-7aeffab5e38a-0",
"legendFormat": "__auto",
"range": true,
"refId": "A"
}
],
"title": "CPU System",
"type": "stat"
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} | 2023-05-29 11:03:51 UTC | 2023-05-29 14:20:51 UTC | 0 | 0 |
17,308 | 1 | 299,646 | 26,611 | 0 | 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 | 0 | 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/). | 78,163 | 78,164 | null | null | 0 | 0 | 26,611 | 1 | 17,308 | 1 | 0 | A dashboard that monitors RabbitMQ. It is created using the (rabbitmq-mixin)[https://github.com/adinhodovic/rabbitmq-mixin]. | {
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17,645 | 1 | 763,155 | 27,105 | 0 | 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 | 0 | null | 0 | 0 | null | null | 0 | 0 | 27,105 | 1 | 17,645 | 1 | 0 | This dashboard uses a Prometheus exporter for the Tado thermostat to simply show the temperature, humidity and heating settings. | {
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18,100 | 1 | 795,249 | 27,791 | 0 | 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 | 0 | null | 0 | 0 | null | null | 0 | 0 | 27,791 | 1 | 18,100 | 1 | 0 | Monitoring for trading team | {
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18,135 | 1 | 474,105 | 27,841 | 0 | redpanda-ops-dashboard | Redpanda Ops Dashboard | Redpanda Operations Dashboard | 2023-02-18 00:12:36 UTC | 2023-03-06 13:54:54 UTC | 0 | A dashboard focused on Redpanda operations, with the following charts:
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} | 2023-02-18 00:12:36 UTC | 2023-02-18 00:13:01 UTC | 0 | 0 |
17,055 | 1 | 716,188 | 26,177 | 0 | 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 | 0 | null | 0 | 0 | null | null | 0 | 0 | 26,177 | 1 | 17,055 | 1 | 0 | Shows stats of a SAP NetWeaver cluster. Brought to you by SUSE. | {
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} | 2022-09-26 20:19:35 UTC | null | 0 | 0 |
17,382 | 1 | 736,857 | 26,710 | 0 | sql-servers | SQL Servers | MSSQL Server performance metrics by Instances | 2022-11-09 16:14:21 UTC | 2022-11-09 16:14:27 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 26,710 | 1 | 17,382 | 1 | 0 | MSSQL Server performance metrics by Instances | {
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],
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},
{
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],
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"hide": 0,
"includeAll": false,
"label": null,
"multi": false,
"multiFormat": "glob",
"name": "Instance",
"options": [],
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"sort": 1,
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"time": {
"from": "now-1h",
"to": "now"
},
"timepicker": {
"now": true,
"refresh_intervals": [
"10s",
"30s",
"1m",
"5m",
"15m",
"30m",
"1h",
"2h",
"1d"
],
"time_options": [
"5m",
"15m",
"1h",
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"12h",
"24h",
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"title": "SQL Servers",
"uid": "p1-aL7dmz",
"version": 100
} | 2022-11-09 16:14:21 UTC | null | 0 | 0 |
17,638 | 1 | 751,517 | 27,097 | 0 | 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 | 0 | # 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
| 79,712 | 79,713 | Other | null | 0 | 0 | 27,097 | 1 | 17,638 | 1 | 0 | Podman仪表盘,由冰雪殇璃陌梦基于Prometheus-podman-exporter制作 | {
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"uid": "${DS_PROMETHEUS-OULUCLOUD-US}"
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"id": 27,
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"displayMode": "list",
"placement": "bottom",
"showLegend": true
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"tooltip": {
"mode": "single",
"sort": "none"
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"targets": [
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"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS-OULUCLOUD-US}"
},
"editorMode": "code",
"expr": "increase(podman_container_net_input_total{instance=\"$instance\"}[$__rate_interval]) * 8",
"legendFormat": "{{id}}_in",
"range": true,
"refId": "input"
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{
"datasource": {
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"uid": "${DS_PROMETHEUS-OULUCLOUD-US}"
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"expr": "increase(podman_container_net_output_total{instance=\"$instance\"}[$__rate_interval]) * 8",
"hide": false,
"legendFormat": "{{id}}_out",
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],
"title": "网络",
"type": "timeseries"
},
{
"collapsed": false,
"gridPos": {
"h": 1,
"w": 24,
"x": 0,
"y": 25
},
"id": 15,
"panels": [],
"title": "镜像信息(宿主机:$instance)",
"type": "row"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS-OULUCLOUD-US}"
},
"description": "",
"fieldConfig": {
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"color": {
"fixedColor": "blue",
"mode": "thresholds"
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"mappings": [],
"thresholds": {
"mode": "percentage",
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"color": "green"
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},
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},
"gridPos": {
"h": 8,
"w": 4,
"x": 0,
"y": 26
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"id": 33,
"options": {
"colorMode": "value",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
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"fields": "",
"values": false
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"datasource": {
"type": "prometheus",
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"editorMode": "code",
"exemplar": false,
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"format": "table",
"instant": true,
"legendFormat": "占用的存储空间",
"range": false,
"refId": "A"
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"title": "镜像总大小",
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{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS-OULUCLOUD-US}"
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{
"color": "#EAB839",
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"color": "red",
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"unit": "percentunit"
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"w": 4,
"x": 4,
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"id": 35,
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"fields": "",
"values": false
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"showThresholdLabels": false,
"showThresholdMarkers": true
},
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"datasource": {
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"uid": "${DS_PROMETHEUS-OULUCLOUD-US}"
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"expr": "sum(podman_image_size{instance=~\"$instance\"}) / sum(node_filesystem_size_bytes{instance=~\"$instance\"})",
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"title": "磁盘占比",
"type": "gauge"
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{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS-OULUCLOUD-US}"
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"matcher": {
"id": "byName",
"options": "创建时间"
},
"properties": [
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"id": "unit",
"value": "dateTimeFromNow"
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{
"id": "custom.width",
"value": 120
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},
{
"matcher": {
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"options": "镜像大小"
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"id": "unit",
"value": "bits"
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},
{
"matcher": {
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"options": "ID"
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"id": "custom.width",
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},
{
"matcher": {
"id": "byName",
"options": "tag"
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"id": "custom.width",
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]
},
"gridPos": {
"h": 8,
"w": 16,
"x": 8,
"y": 26
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"id": 31,
"options": {
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"reducer": [
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"show": false
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"showHeader": true,
"sortBy": []
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"refId": "B"
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"uid": "${DS_PROMETHEUS-OULUCLOUD-US}"
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"hide": false,
"instant": true,
"legendFormat": "__auto",
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"refId": "C"
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],
"title": "镜像信息",
"transformations": [
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"id": "filterFieldsByName",
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"id",
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{
"id": "merge",
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"id": "organize",
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"allValue": " ",
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"uid": "${DS_PROMETHEUS-OULUCLOUD-US}"
},
"definition": "label_values(podman_container_info, instance)",
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"time": {
"from": "2022-12-13T21:10:31.482Z",
"to": "2022-12-14T05:17:14.426Z"
},
"timepicker": {
"refresh_intervals": [
"30s",
"1m",
"5m",
"15m",
"30m",
"1h",
"2h",
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"title": "Podman Dashboard 中文版(zh_CN)",
"uid": "podman",
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} | 2022-12-16 13:52:06 UTC | 2022-12-16 15:13:09 UTC | 0 | 0 |
17,869 | 1 | 247,913 | 27,399 | 0 | 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 | 0 | # 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). | 80,619 | 80,620 | null | null | 0 | 0 | 27,399 | 1 | 17,869 | 1 | 0 | Overview for operator VictoriaMetrics v0.25.0 or higher | {
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} | 2023-01-12 10:34:36 UTC | null | 0 | 0 |
18,538 | 1 | 420,992 | 28,425 | 0 | blackbox-exporter | BlackBox Exporter | Prometheus BlackBox Exporter Dashboard | 2023-04-16 20:09:33 UTC | 2023-04-27 06:31:04 UTC | 0 | # Dashboard for Prometheus Blackbox Exporter
uses Prometheus Blackbox-Exporter to retreive metrics. | 83,753 | 83,754 | Other | null | 0 | 0 | 28,425 | 1 | 18,538 | 1 | 0 | Prometheus BlackBox Exporter Dashboard | {
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} | 2022-10-12 03:04:24 UTC | null | 0 | 0 |
17,375 | 1 | 739,594 | 26,695 | 0 | 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 | 0 | # Kubernetes resource monitoring Grafana dashboard
[![license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/instrumentisto/grafana-dashboard-kubernetes-prometheus/blob/master/LICENSE.md)
[![prometheus](https://img.shields.io/badge/prometheus-%5E15.18.0-green.svg)](https://github.com/prometheus/prometheus)
[![grafana](https://img.shields.io/badge/grafana-%5E9.2.3-green.svg)](https://github.com/grafana/grafana)
[![GitHub Repo stars](https://img.shields.io/github/stars/yeusiukou/grafana-k8s-resource-monitoring?style=social)](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.
![CleanShot 2022-11-07 at 12 32 38](https://user-images.githubusercontent.com/8567013/200406399-96fbd65f-39ea-48af-a8ec-2b06bbf476d9.png)
- 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.
![CleanShot 2022-11-07 at 11 57 00](https://user-images.githubusercontent.com/8567013/200406541-f4c98d38-ede8-44db-954a-0d016fcdf9db.png)
- 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.
![CleanShot 2022-11-07 at 12 09 09](https://user-images.githubusercontent.com/8567013/200406603-7153d3cd-6f92-4415-92b4-ffeb1cd55426.png)
- Pod CPU and memory usage over time.
![CleanShot 2022-11-07 at 12 13 09](https://user-images.githubusercontent.com/8567013/200406615-2a2fd231-de0d-4a10-bbfc-94a5a4338d68.png)
| 0 | 0 | nodeExporter | null | 1 | 5 | 26,695 | 1 | 17,375 | 1 | 0 | null | {
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} | 2022-11-08 16:52:45 UTC | 2022-11-08 16:58:19 UTC | 1 | 5 |
17,545 | 1 | 756,411 | 26,938 | 0 | rabbitmq-stream | RabbitMQ-Stream | Stream protocol message rates and errors | 2022-12-06 13:20:28 UTC | 2022-12-06 13:20:38 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 26,938 | 1 | 17,545 | 1 | 0 | Stream protocol message rates and errors | {
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17,613 | 1 | 299,646 | 27,061 | 0 | 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 | 0 | 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). | 79,586 | 79,587 | null | null | 2 | 5 | 27,061 | 1 | 17,613 | 1 | 0 | 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). | {
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"editorMode": "code",
"expr": "irate(pg_stat_database_temp_bytes{job=\"$job\", datname=~\"$datname\"}[5m])",
"format": "time_series",
"intervalFactor": 1,
"legendFormat": "{{datname}}",
"range": true,
"refId": "A"
}
],
"title": "Temp File (Bytes)",
"type": "timeseries"
}
],
"refresh": false,
"schemaVersion": 37,
"style": "dark",
"tags": [
"postgres"
],
"templating": {
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"value": "Prometheus"
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"includeAll": false,
"label": "datasource",
"multi": false,
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"refresh": 1,
"regex": "",
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},
{
"auto": true,
"auto_count": 200,
"auto_min": "1s",
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},
"hide": 0,
"label": "Interval",
"name": "interval",
"options": [
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"selected": true,
"text": "auto",
"value": "$__auto_interval_interval"
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{
"selected": false,
"text": "1s",
"value": "1s"
},
{
"selected": false,
"text": "5s",
"value": "5s"
},
{
"selected": false,
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"value": "1m"
},
{
"selected": false,
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},
{
"selected": false,
"text": "1h",
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},
{
"selected": false,
"text": "6h",
"value": "6h"
},
{
"selected": false,
"text": "1d",
"value": "1d"
}
],
"query": "1s,5s,1m,5m,1h,6h,1d",
"queryValue": "",
"refresh": 2,
"skipUrlSync": false,
"type": "interval"
},
{
"current": {},
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"definition": "query_result(pg_exporter_last_scrape_duration_seconds)",
"hide": 0,
"includeAll": false,
"label": "Namespace",
"multi": false,
"name": "namespace",
"options": [],
"query": {
"query": "query_result(pg_exporter_last_scrape_duration_seconds)",
"refId": "StandardVariableQuery"
},
"refresh": 2,
"regex": "/.*namespace=\"([^\"]+).*/",
"skipUrlSync": false,
"sort": 1,
"tagValuesQuery": "",
"tagsQuery": "",
"type": "query",
"useTags": false
},
{
"current": {},
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"definition": "label_values(pg_up, job)",
"hide": 0,
"includeAll": false,
"label": "job",
"multi": false,
"name": "job",
"options": [],
"query": {
"query": "label_values(pg_up, job)",
"refId": "StandardVariableQuery"
},
"refresh": 1,
"regex": "",
"skipUrlSync": false,
"sort": 1,
"tagValuesQuery": "",
"tagsQuery": "",
"type": "query",
"useTags": false
},
{
"current": {},
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"definition": "",
"hide": 0,
"includeAll": true,
"label": "Database",
"multi": true,
"name": "datname",
"options": [],
"query": {
"query": "label_values(datname)",
"refId": "Prometheus-datname-Variable-Query"
},
"refresh": 1,
"regex": "",
"skipUrlSync": false,
"sort": 1,
"tagValuesQuery": "",
"tagsQuery": "",
"type": "query",
"useTags": false
},
{
"current": {},
"datasource": {
"type": "prometheus",
"uid": "${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": [
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"1h",
"6h",
"12h",
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"7d",
"30d"
]
},
"timezone": "",
"title": "PostgreSQL AWS RDS Database Overview ",
"uid": "000000039",
"version": 22,
"weekStart": ""
} | 2023-04-14 01:47:40 UTC | 2023-04-14 01:54:15 UTC | 0 | 0 |
18,723 | 1 | 855,483 | 28,733 | 0 | celestia | CELESTIA | Get a start with this basic Celestia dashboard | 2023-05-12 09:44:04 UTC | 2023-05-12 09:45:18 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 28,733 | 1 | 18,723 | 1 | 0 | null | {
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],
"__elements": [],
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"type": "panel",
"id": "gauge",
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{
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"type": "timeseries"
},
{
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"uid": "${DS_PROMETHEUS}"
},
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"expr": "tendermint_consensus_validator_last_signed_height",
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"range": true,
"refId": "A"
}
],
"title": "VALIDATOR LAST SIGNED HEIGHT",
"type": "gauge"
},
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"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
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"w": 12,
"x": 0,
"y": 16
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"uid": "${DS_PROMETHEUS}"
},
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"expr": "tendermint_consensus_validator_missed_blocks",
"legendFormat": "{{job}}",
"range": true,
"refId": "A"
}
],
"title": "MISSED BLOCKS",
"type": "stat"
},
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"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
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},
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"viz": false
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"lineWidth": 1,
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"mode": "none"
},
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}
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"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
}
},
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},
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"h": 8,
"w": 12,
"x": 12,
"y": 16
},
"id": 12,
"options": {
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"calcs": [],
"displayMode": "list",
"placement": "bottom"
},
"tooltip": {
"mode": "single",
"sort": "none"
}
},
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"editorMode": "code",
"expr": "tendermint_consensus_validator_power",
"legendFormat": "{{job}}",
"range": true,
"refId": "A"
}
],
"title": "VOTING POWER",
"type": "timeseries"
}
],
"schemaVersion": 36,
"style": "dark",
"tags": [],
"templating": {
"list": []
},
"time": {
"from": "now-6h",
"to": "now"
},
"timepicker": {},
"timezone": "",
"title": "CELESTIA",
"uid": "d6HzKOU4z",
"version": 3,
"weekStart": "",
"gnetId": 18723,
"description": "Get a start with this basic Celestia dashboard"
} | 2023-05-12 09:44:05 UTC | 2023-05-12 09:45:16 UTC | 0 | 0 |
17,641 | 1 | 762,844 | 27,100 | 0 | sonarqube-system-dashboard | Sonarqube - System Dashboard | The Sonarqube - System Dashboard dashboard presents all metrics in detail of Sonarqube instance. | 2022-12-17 12:40:40 UTC | 2022-12-17 12:53:54 UTC | 0 | ## Quick start with `docker`
Run command below (edit sonarqube_url and token):
```
docker run -d \
-p8198:8198 \
-eSONARQUBE_SERVER='http://192.168.3.101:9001' \
-eSONARQUBE_TOKEN='squ_af1e521e19aef5c5de1cb6df89adf3cbb3a9759e' \
return200/sonarqube-exporter:latest
```
Access `localhost:8198`
![overview!](https://github.com/return200-ok/sonarqube_prometheus_exporter/blob/main/assets/sonarqube_exporter_metrisc.png?raw=true)
| 79,734 | 79,735 | null | null | 1 | 5 | 27,100 | 1 | 17,641 | 1 | 0 | null | {
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"x": 0,
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"legendFormat": "__auto",
"range": true,
"refId": "A"
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],
"title": "Health check info",
"type": "stat"
},
{
"collapsed": false,
"gridPos": {
"h": 1,
"w": 24,
"x": 0,
"y": 2
},
"id": 78,
"panels": [],
"title": "# Statistics",
"type": "row"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"fieldConfig": {
"defaults": {
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"steps": [
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"color": "green",
"value": null
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{
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"unit": "short"
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"gridPos": {
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"w": 24,
"x": 0,
"y": 3
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"id": 88,
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"editorMode": "code",
"expr": "project_count_by_language",
"legendFormat": "{{language}}",
"range": true,
"refId": "A"
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],
"title": "Total of project by language",
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"fieldConfig": {
"defaults": {
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"value": null
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"unit": "short"
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"overrides": []
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"gridPos": {
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"w": 24,
"x": 0,
"y": 7
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"id": 90,
"options": {
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"fields": "",
"values": false
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"textMode": "auto"
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"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
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"editorMode": "code",
"expr": "ncloc_count_by_language",
"legendFormat": "{{language}}",
"range": true,
"refId": "A"
}
],
"title": "Total line of code by language",
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
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"fieldConfig": {
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"thresholds": {
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"unit": "short"
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"gridPos": {
"h": 7,
"w": 6,
"x": 0,
"y": 11
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"id": 84,
"options": {
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"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
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"values": false
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"legendFormat": "{{instance}}",
"range": true,
"refId": "A"
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],
"title": "Total line of code",
"type": "stat"
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{
"datasource": {
"type": "prometheus",
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17,907 | 1 | 746,033 | 27,451 | 0 | 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 | 0 | null | 0 | 0 | null | null | 0 | 0 | 27,451 | 1 | 17,907 | 1 | 0 | Erlang Distribution links, inet socket, port driver, dist process + tls_connection & tls_sender | {
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} | 2023-01-19 15:26:08 UTC | 2023-01-19 15:36:05 UTC | 0 | 0 |
17,932 | 1 | 405,174 | 27,495 | 0 | strategies | strategies | strategies | 2023-01-23 04:13:15 UTC | 2023-01-23 04:14:46 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 27,495 | 1 | 17,932 | 1 | 0 | null | {
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} | 2023-01-23 04:13:15 UTC | 2023-01-23 04:14:46 UTC | 0 | 0 |
18,194 | 1 | 304,234 | 27,934 | 0 | 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 | 0 | A simple Dashboard for https://github.com/czerwonk/ping_exporter | 0 | 0 | Other | null | 0 | 0 | 27,934 | 1 | 18,194 | 1 | 0 | null | {
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18,234 | 1 | 312,323 | 28,002 | 0 | 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 | 0 | 1 | 0 | 0 | null | null | 0 | 0 | 28,002 | 1 | 18,234 | 1 | 0 | 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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18,823 | 1 | 497,391 | 28,863 | 0 | akamai-siem | Akamai SIEM | Akamai SIEM powered by Hydrolix dashboard for security analytics
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18,833 | 1 | 850,340 | 28,879 | 0 | frontend | Frontend | first test for grafana faro | 2023-05-25 15:46:33 UTC | 2023-05-25 15:47:01 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 28,879 | 1 | 18,833 | 1 | 0 | null | {
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} | 2022-09-12 13:33:42 UTC | 2022-09-12 13:34:00 UTC | 0 | 0 |
17,001 | 1 | 498,884 | 26,065 | 0 | 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 | 0 | To learn how to monitor infrastructure health using Pixie, see the [tutorial](https://docs.px.dev/tutorials/pixie-101/infra-health/). | 76,976 | 76,977 | Other | null | 0 | 0 | 26,065 | 1 | 17,001 | 1 | 0 | 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/ | {
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"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']])"
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"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"
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],
"title": "HTTP Errors",
"type": "timeseries"
},
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
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"custom": {
"axisGridShow": true,
"axisLabel": "milliseconds",
"axisPlacement": "auto",
"axisSoftMax": 6,
"barAlignment": 0,
"drawStyle": "line",
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"mappings": [],
"thresholds": {
"mode": "absolute",
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"h": 13,
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{
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"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": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"description": "",
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisLabel": "CPU Usage",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 0,
"gradientMode": "none",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"lineInterpolation": "linear",
"lineWidth": 1,
"pointSize": 5,
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{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
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"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 # 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"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)"
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"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)"
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"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)"
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"title": "Network Sent And Received",
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{
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"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)"
},
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"refId": "A"
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],
"title": "Bytes Read",
"type": "timeseries"
},
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"description": "",
"fieldConfig": {
"defaults": {
"color": {
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"custom": {
"axisLabel": "Disk Write Throughput",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 0,
"gradientMode": "none",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
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"lineInterpolation": "linear",
"lineWidth": 1,
"pointSize": 5,
"scaleDistribution": {
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"showPoints": "auto",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
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"thresholdsStyle": {
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"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green"
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{
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},
"unit": "binBps"
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"overrides": []
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"gridPos": {
"h": 13,
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"id": 17,
"options": {
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"tooltip": {
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"targets": [
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
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"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)"
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"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)"
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"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
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"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": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"description": "",
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"custom": {
"align": "auto",
"displayMode": "auto",
"inspect": false
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"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green"
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{
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"overrides": [
{
"matcher": {
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"options": "latency_p99"
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"properties": [
{
"id": "custom.displayMode",
"value": "color-text"
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{
"id": "thresholds",
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"color": "green"
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{
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{
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"properties": [
{
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{
"matcher": {
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{
"id": "unit",
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{
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{
"matcher": {
"id": "byName",
"options": "requesting_pod"
},
"properties": [
{
"id": "links",
"value": [
{
"title": "",
"url": "/d/_t3foxCnz/px-pod?orgId=1&${pixieCluster:queryparam}&var-pixiePod=${__data.fields.requesting_pod}"
}
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}
]
},
{
"matcher": {
"id": "byName",
"options": "requesting_svc"
},
"properties": [
{
"id": "links",
"value": [
{
"title": "",
"url": "/d/qhHtFyCnk/px-service?orgId=1&${pixieCluster:queryparam}&var-pixieService=${__data.fields.requesting_svc}"
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}
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}
]
},
"gridPos": {
"h": 10,
"w": 24,
"x": 0,
"y": 51
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"id": 20,
"options": {
"footer": {
"fields": "",
"reducer": [
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},
"pluginVersion": "7.5.1",
"targets": [
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
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"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)"
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"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 # 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17,084 | 1 | 546,928 | 26,215 | 0 | 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 | 0 | null | 0 | 0 | nodeExporter | null | 0 | 0 | 26,215 | 1 | 17,084 | 1 | 0 | A dashboard for PI-Hole Exporter from https://github.com/eko/pihole-exporter | {
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} | 2022-12-28 11:30:31 UTC | 2022-12-28 11:34:45 UTC | 0 | 0 |
18,148 | 1 | 800,178 | 27,859 | 0 | 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 | 0 | 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. | 0 | 0 | null | null | 0 | 0 | 27,859 | 1 | 18,148 | 1 | 0 | null | {
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18,677 | 1 | 721,308 | 28,653 | 0 | pgbackrest | Crunchy/pgBackRest | pgBackRest monitoring dashboard for PGO Crunchy Operator | 2023-05-05 14:46:43 UTC | 2023-05-05 14:50:24 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 28,653 | 1 | 18,677 | 1 | 0 | null | {
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"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": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
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"axisColorMode": "text",
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "bars",
"fillOpacity": 100,
"gradientMode": "opacity",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"lineInterpolation": "linear",
"lineWidth": 1,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "never",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "short"
},
"overrides": [
{
"matcher": {
"id": "byName",
"options": "1ms"
},
"properties": [
{
"id": "color",
"value": {
"fixedColor": "light-green",
"mode": "fixed"
}
}
]
},
{
"matcher": {
"id": "byName",
"options": "10ms"
},
"properties": [
{
"id": "color",
"value": {
"fixedColor": "dark-green",
"mode": "fixed"
}
}
]
},
{
"matcher": {
"id": "byName",
"options": "50ms"
},
"properties": [
{
"id": "color",
"value": {
"fixedColor": "light-blue",
"mode": "fixed"
}
}
]
},
{
"matcher": {
"id": "byName",
"options": "100ms"
},
"properties": [
{
"id": "color",
"value": {
"fixedColor": "dark-blue",
"mode": "fixed"
}
}
]
},
{
"matcher": {
"id": "byName",
"options": "1s"
},
"properties": [
{
"id": "color",
"value": {
"fixedColor": "dark-yellow",
"mode": "fixed"
}
}
]
},
{
"matcher": {
"id": "byName",
"options": "> 1s"
},
"properties": [
{
"id": "color",
"value": {
"fixedColor": "dark-red",
"mode": "fixed"
}
}
]
}
]
},
"gridPos": {
"h": 7,
"w": 16,
"x": 8,
"y": 8
},
"id": 8,
"options": {
"legend": {
"calcs": [
"mean"
],
"displayMode": "table",
"placement": "right",
"showLegend": true,
"width": 200
},
"tooltip": {
"mode": "multi",
"sort": "desc"
}
},
"targets": [
{
"datasource": {
"type": "graphite",
"uid": "${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.$host.main.latency10-50)), '50ms')"
},
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"hide": false,
"refCount": 0,
"refId": "D",
"target": "alias(perSecond(sumSeries(dnsdist.$host.main.latency50-100)), '100ms')"
},
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"hide": false,
"refCount": 0,
"refId": "E",
"target": "alias(perSecond(sumSeries(dnsdist.$host.main.latency100-1000)), '1s')"
},
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"hide": false,
"refCount": 0,
"refId": "F",
"target": "alias(perSecond(sumSeries(dnsdist.$host.main.latency-slow)), '> 1s')"
}
],
"title": "Time To Answer",
"transparent": true,
"type": "timeseries"
},
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisCenteredZero": false,
"axisColorMode": "text",
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "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": 8,
"x": 0,
"y": 15
},
"id": 16,
"options": {
"legend": {
"calcs": [
"mean"
],
"displayMode": "table",
"placement": "right",
"showLegend": true,
"width": 200
},
"tooltip": {
"mode": "multi",
"sort": "none"
}
},
"targets": [
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"refCount": 0,
"refId": "A",
"target": "alias(perSecond(sumSeries(dnsdist.$host.main.rule-drop)), 'rule-drop')"
},
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"hide": false,
"refCount": 0,
"refId": "B",
"target": "alias(perSecond(sumSeries(dnsdist.$host.main.rule-nxdomain)), 'rule-nxdomain')"
},
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"hide": false,
"refCount": 0,
"refId": "C",
"target": "alias(perSecond(sumSeries(dnsdist.$host.main.rule-refused)), 'rule-refused')"
},
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"hide": false,
"refCount": 0,
"refId": "D",
"target": "alias(perSecond(sumSeries(dnsdist.$host.main.rule-servfail)), 'rule-servfail')"
}
],
"title": "Rule Answers",
"transparent": true,
"type": "timeseries"
},
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisCenteredZero": false,
"axisColorMode": "text",
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 10,
"gradientMode": "none",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"lineInterpolation": "linear",
"lineWidth": 1,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "never",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
},
"decimals": 2,
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
}
},
"overrides": [
{
"matcher": {
"id": "byName",
"options": "servfail"
},
"properties": [
{
"id": "color",
"value": {
"fixedColor": "blue",
"mode": "fixed"
}
}
]
},
{
"matcher": {
"id": "byName",
"options": "timeouts"
},
"properties": [
{
"id": "color",
"value": {
"fixedColor": "light-orange",
"mode": "fixed"
}
}
]
},
{
"matcher": {
"id": "byName",
"options": "send errors"
},
"properties": [
{
"id": "color",
"value": {
"fixedColor": "red",
"mode": "fixed"
}
}
]
},
{
"matcher": {
"id": "byName",
"options": "not available"
},
"properties": [
{
"id": "color",
"value": {
"fixedColor": "#777676",
"mode": "fixed"
}
}
]
},
{
"matcher": {
"id": "byName",
"options": "non-compliant"
},
"properties": [
{
"id": "color",
"value": {
"fixedColor": "#484848",
"mode": "fixed"
}
}
]
}
]
},
"gridPos": {
"h": 6,
"w": 16,
"x": 8,
"y": 15
},
"id": 28,
"options": {
"legend": {
"calcs": [
"mean"
],
"displayMode": "table",
"placement": "right",
"showLegend": true,
"sortBy": "Mean",
"sortDesc": true,
"width": 200
},
"tooltip": {
"mode": "multi",
"sort": "none"
}
},
"targets": [
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"refCount": 0,
"refId": "A",
"target": "alias(perSecond(sumSeries(dnsdist.$host.main.downstream-send-errors)), 'send errors')"
},
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"hide": false,
"refCount": 0,
"refId": "B",
"target": "alias(perSecond(sumSeries(dnsdist.$host.main.downstream-timeouts)), 'timeouts')"
},
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"hide": false,
"refCount": 0,
"refId": "C",
"target": "alias(perSecond(sumSeries(dnsdist.$host.main.no-policy)), 'not available')"
},
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"hide": false,
"refCount": 0,
"refId": "D",
"target": "alias(perSecond(sumSeries(dnsdist.$host.main.servfail-responses)), 'servfail')"
},
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"hide": false,
"refCount": 0,
"refId": "E",
"target": "alias(perSecond(sumSeries(dnsdist.$host.main.noncompliant-responses)), 'non-compliant')"
}
],
"title": "Backend Problems",
"transparent": true,
"type": "timeseries"
},
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"description": "Number of queries per second dropped because of a dynamic block",
"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": 900000,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
},
"links": [],
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "short"
},
"overrides": []
},
"gridPos": {
"h": 6,
"w": 8,
"x": 0,
"y": 21
},
"id": 22,
"options": {
"legend": {
"calcs": [
"mean"
],
"displayMode": "table",
"placement": "right",
"showLegend": true,
"width": 190
},
"tooltip": {
"mode": "multi",
"sort": "none"
}
},
"pluginVersion": "9.3.1",
"targets": [
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"hide": false,
"refCount": 0,
"refId": "A",
"target": "alias(perSecond(sumSeries(dnsdist.$host.main.dyn-blocked)), 'dynamic-block')"
},
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"hide": false,
"refCount": 0,
"refId": "B",
"target": "alias(perSecond(sumSeries(dnsdist.$host.main.dyn-block-nmg-size)), 'block-entries')"
}
],
"title": "Dynamic Block",
"transparent": true,
"type": "timeseries"
},
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
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"axisColorMode": "text",
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 10,
"gradientMode": "none",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"lineInterpolation": "linear",
"lineWidth": 1,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "never",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
},
"links": [],
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "short"
},
"overrides": [
{
"matcher": {
"id": "byName",
"options": "hits"
},
"properties": [
{
"id": "color",
"value": {
"fixedColor": "light-blue",
"mode": "fixed"
}
}
]
},
{
"matcher": {
"id": "byName",
"options": "misses"
},
"properties": [
{
"id": "color",
"value": {
"fixedColor": "light-orange",
"mode": "fixed"
}
}
]
}
]
},
"gridPos": {
"h": 6,
"w": 16,
"x": 8,
"y": 21
},
"id": 20,
"options": {
"legend": {
"calcs": [
"mean"
],
"displayMode": "table",
"placement": "right",
"showLegend": true,
"width": 190
},
"tooltip": {
"mode": "multi",
"sort": "none"
}
},
"pluginVersion": "9.3.1",
"targets": [
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"hide": false,
"refCount": 0,
"refId": "A",
"target": "alias(perSecond(sumSeries(dnsdist.$host.main.cache-hits)), 'hits')"
},
{
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"hide": false,
"refCount": 0,
"refId": "B",
"target": "alias(perSecond(sumSeries(dnsdist.$host.main.cache-misses)), 'misses')"
}
],
"title": "Packet Cache Hits/s vs. Misses/s",
"transparent": true,
"type": "timeseries"
}
],
"refresh": "30s",
"schemaVersion": 37,
"style": "dark",
"tags": [
"dns",
"dnsdist"
],
"templating": {
"list": [
{
"current": {},
"datasource": {
"type": "graphite",
"uid": "${DS_NL-INFRA-CAP1_GRAPHITE}"
},
"definition": "",
"hide": 0,
"includeAll": true,
"multi": false,
"name": "host",
"options": [],
"query": {
"queryType": "Default",
"refId": "A",
"target": "dnsdist.*"
},
"refresh": 1,
"regex": "",
"skipUrlSync": false,
"sort": 0,
"type": "query"
}
]
},
"time": {
"from": "now-24h",
"to": "now"
},
"timepicker": {},
"timezone": "",
"title": "dnsdist",
"uid": "pyx7eETVk",
"version": 46,
"weekStart": "",
"gnetId": 18044
} | 2023-02-06 08:34:16 UTC | 2023-02-06 08:36:29 UTC | 0 | 0 |
18,276 | 1 | 683,756 | 28,050 | 0 | 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 | 0 | ## 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 $KAFKA_HOME/config/server.properties
```
## 添加配置至prometheus
## Edit prometheus.yml
在prometheus.yml文件中添加:
```yaml
- job_name: "kafka-jmx"
# metrics_path defaults to '/metrics'
# scheme defaults to 'http'.
static_configs:
- targets: ["{Prometheus Server}:6660"]
```
访问{Prometheus Server}6660/metrics查看指标
View http://{Prometheus Server}6660/metrics to checkout Kafka metrics
| 0 | 0 | Other | null | 0 | 0 | 28,050 | 1 | 18,276 | 1 | 0 | Kafka resource usage and consumer lag overview | {
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"params": [],
"type": "count"
}
]
],
"tags": [
{
"key": "host",
"operator": "=~",
"value": "/^$hostname$/"
},
{
"condition": "AND",
"key": "path",
"operator": "=~",
"value": "/^$path$/"
},
{
"condition": "AND",
"key": "verb",
"operator": "=~",
"value": "/^$method$/"
},
{
"condition": "AND",
"key": "resp_code",
"operator": "=",
"value": "302"
}
]
},
{
"alias": "304",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"0"
],
"type": "fill"
}
],
"measurement": "nginx_log",
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"policy": "short",
"refId": "D",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"ident"
],
"type": "field"
},
{
"params": [],
"type": "count"
}
]
],
"tags": [
{
"key": "host",
"operator": "=~",
"value": "/^$hostname$/"
},
{
"condition": "AND",
"key": "path",
"operator": "=~",
"value": "/^$path$/"
},
{
"condition": "AND",
"key": "verb",
"operator": "=~",
"value": "/^$method$/"
},
{
"condition": "AND",
"key": "resp_code",
"operator": "=",
"value": "304"
}
]
},
{
"alias": "403",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"0"
],
"type": "fill"
}
],
"measurement": "nginx_log",
"orderByTime": "ASC",
"policy": "short",
"refId": "E",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"ident"
],
"type": "field"
},
{
"params": [],
"type": "count"
}
]
],
"tags": [
{
"key": "host",
"operator": "=~",
"value": "/^$hostname$/"
},
{
"condition": "AND",
"key": "path",
"operator": "=~",
"value": "/^$path$/"
},
{
"condition": "AND",
"key": "verb",
"operator": "=~",
"value": "/^$method$/"
},
{
"condition": "AND",
"key": "resp_code",
"operator": "=",
"value": "403"
}
]
},
{
"alias": "404",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"0"
],
"type": "fill"
}
],
"measurement": "nginx_log",
"orderByTime": "ASC",
"policy": "short",
"refId": "F",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"ident"
],
"type": "field"
},
{
"params": [],
"type": "count"
}
]
],
"tags": [
{
"key": "host",
"operator": "=~",
"value": "/^$hostname$/"
},
{
"condition": "AND",
"key": "path",
"operator": "=~",
"value": "/^$path$/"
},
{
"condition": "AND",
"key": "verb",
"operator": "=~",
"value": "/^$method$/"
},
{
"condition": "AND",
"key": "resp_code",
"operator": "=",
"value": "404"
}
]
},
{
"alias": "500",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"0"
],
"type": "fill"
}
],
"measurement": "nginx_log",
"orderByTime": "ASC",
"policy": "short",
"query": "SELECT count(\"ident\") FROM \"short\".\"nginx_log\" WHERE (\"path\" =~ /^$path$/ AND \"host\" =~ /^$hostname$/ AND \"resp_code\" =~ /5[\\d]{2}/ AND \"verb\" =~ /^$method$/) AND $timeFilter GROUP BY time($__interval) fill(0)",
"rawQuery": true,
"refId": "G",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"ident"
],
"type": "field"
},
{
"params": [],
"type": "count"
}
]
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"tags": [
{
"key": "path",
"operator": "=~",
"value": "/^$path$/"
},
{
"condition": "AND",
"key": "host",
"operator": "=~",
"value": "/^$hostname$/"
},
{
"condition": "AND",
"key": "resp_code",
"operator": "=",
"value": "500"
},
{
"condition": "AND",
"key": "verb",
"operator": "=~",
"value": "/^$method$/"
}
]
},
{
"alias": "502",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"0"
],
"type": "fill"
}
],
"measurement": "nginx_log",
"orderByTime": "ASC",
"policy": "short",
"query": "SELECT count(\"ident\") FROM \"short\".\"nginx_log\" WHERE (\"path\" =~ /^$path$/ AND \"host\" =~ /^$hostname$/ AND \"resp_code\" = '502' AND \"verb\" =~ /^$method$/) AND $timeFilter GROUP BY time($__interval) fill(0)",
"rawQuery": true,
"refId": "H",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"ident"
],
"type": "field"
},
{
"params": [],
"type": "count"
}
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],
"tags": [
{
"key": "path",
"operator": "=~",
"value": "/^$path$/"
},
{
"condition": "AND",
"key": "host",
"operator": "=~",
"value": "/^$hostname$/"
},
{
"condition": "AND",
"key": "resp_code",
"operator": "=",
"value": "502"
},
{
"condition": "AND",
"key": "verb",
"operator": "=~",
"value": "/^$method$/"
}
]
},
{
"alias": "5xx",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"0"
],
"type": "fill"
}
],
"measurement": "nginx_log",
"orderByTime": "ASC",
"policy": "short",
"query": "SELECT count(\"ident\") FROM \"short\".\"nginx_log\" WHERE (\"path\" =~ /^$path$/ AND \"host\" =~ /^$hostname$/ AND \"resp_code\" =~ /5[\\d]{2}/ AND \"verb\" =~ /^$method$/) AND $timeFilter GROUP BY time($__interval) fill(0)",
"rawQuery": true,
"refId": "J",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"ident"
],
"type": "field"
},
{
"params": [],
"type": "count"
}
]
],
"tags": [
{
"key": "path",
"operator": "=~",
"value": "/^$path$/"
},
{
"condition": "AND",
"key": "host",
"operator": "=~",
"value": "/^$hostname$/"
},
{
"condition": "AND",
"key": "resp_code",
"operator": "=",
"value": "502"
},
{
"condition": "AND",
"key": "verb",
"operator": "=~",
"value": "/^$method$/"
}
]
},
{
"alias": "4xx",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"0"
],
"type": "fill"
}
],
"measurement": "nginx_log",
"orderByTime": "ASC",
"policy": "short",
"query": "SELECT count(\"ident\") FROM \"short\".\"nginx_log\" WHERE (\"path\" =~ /^$path$/ AND \"host\" =~ /^$hostname$/ AND \"resp_code\" =~ /4[\\d]{2}/ AND \"verb\" =~ /^$method$/) AND $timeFilter GROUP BY time($__interval) fill(0)",
"rawQuery": true,
"refId": "K",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"ident"
],
"type": "field"
},
{
"params": [],
"type": "count"
}
]
],
"tags": [
{
"key": "path",
"operator": "=~",
"value": "/^$path$/"
},
{
"condition": "AND",
"key": "host",
"operator": "=~",
"value": "/^$hostname$/"
},
{
"condition": "AND",
"key": "resp_code",
"operator": "=",
"value": "502"
},
{
"condition": "AND",
"key": "verb",
"operator": "=~",
"value": "/^$method$/"
}
]
},
{
"alias": "5xx",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"0"
],
"type": "fill"
}
],
"hide": true,
"measurement": "nginx_log",
"orderByTime": "ASC",
"policy": "short",
"query": "SELECT count(\"ident\") FROM \"short\".\"nginx_log\" WHERE ((\"path\" = '/mnt/logs/iot.developer.log' OR \"path\" = '/mnt/logs/developer.log') AND \"resp_code\" =~ /5[\\d]{2}/) AND $timeFilter GROUP BY time($__interval) fill(0)",
"rawQuery": true,
"refId": "I",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"ident"
],
"type": "field"
},
{
"params": [],
"type": "count"
}
]
],
"tags": [
{
"key": "path",
"operator": "=",
"value": "/mnt/logs/iot.developer.log"
},
{
"condition": "AND",
"key": "resp_code",
"operator": "=",
"value": "502"
}
]
},
{
"alias": "4xx",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"0"
],
"type": "fill"
}
],
"hide": true,
"measurement": "nginx_log",
"orderByTime": "ASC",
"policy": "short",
"query": "SELECT count(\"ident\") FROM \"short\".\"nginx_log\" WHERE ((\"path\" = '/mnt/logs/iot.developer.log' OR \"path\" = '/mnt/logs/developer.log') AND \"resp_code\" =~ /4[\\d]{2}/) AND $timeFilter GROUP BY time($__interval) fill(0)",
"rawQuery": true,
"refId": "L",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"ident"
],
"type": "field"
},
{
"params": [],
"type": "count"
}
]
],
"tags": [
{
"key": "path",
"operator": "=",
"value": "/mnt/logs/iot.developer.log"
},
{
"condition": "AND",
"key": "resp_code",
"operator": "=",
"value": "502"
}
]
}
],
"thresholds": [
{
"colorMode": "critical",
"fill": true,
"line": true,
"op": "gt",
"value": 3
}
],
"timeFrom": null,
"timeShift": null,
"title": "状态码统计",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"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
}
},
{
"alert": {
"conditions": [
{
"evaluator": {
"params": [
4
],
"type": "gt"
},
"operator": {
"type": "and"
},
"query": {
"params": [
"C",
"5m",
"now"
]
},
"reducer": {
"params": [],
"type": "max"
},
"type": "query"
}
],
"executionErrorState": "alerting",
"frequency": "5m",
"handler": 1,
"message": "开发平台时间",
"name": "开发平台处理时间",
"noDataState": "no_data",
"notifications": [
{
"id": 1
}
]
},
"aliasColors": {},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_INFLUXDB_NGINX}",
"fill": 1,
"gridPos": {
"h": 9,
"w": 12,
"x": 12,
"y": 14
},
"id": 4,
"legend": {
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"current": false,
"max": false,
"min": false,
"show": true,
"total": false,
"values": false
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"alias": "服务器处理时间",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"0"
],
"type": "fill"
}
],
"hide": false,
"measurement": "nginx_log",
"orderByTime": "ASC",
"policy": "short",
"refId": "A",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"stream_time"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": [
{
"key": "host",
"operator": "=~",
"value": "/^$hostname$/"
},
{
"condition": "AND",
"key": "resp_code",
"operator": "=~",
"value": "/^$code$/"
},
{
"condition": "AND",
"key": "verb",
"operator": "=~",
"value": "/^$method$/"
},
{
"condition": "AND",
"key": "path",
"operator": "=~",
"value": "/^$path$/"
}
]
},
{
"alias": "请求时间",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"0"
],
"type": "fill"
}
],
"measurement": "nginx_log",
"orderByTime": "ASC",
"policy": "short",
"refId": "B",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"server_time"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": [
{
"key": "host",
"operator": "=~",
"value": "/^$hostname$/"
},
{
"condition": "AND",
"key": "path",
"operator": "=~",
"value": "/^$path$/"
},
{
"condition": "AND",
"key": "resp_code",
"operator": "=~",
"value": "/^$code$/"
},
{
"condition": "AND",
"key": "verb",
"operator": "=~",
"value": "/^$method$/"
}
]
},
{
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"hide": true,
"measurement": "nginx_log",
"orderByTime": "ASC",
"policy": "short",
"refId": "C",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"server_time"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": [
{
"key": "path",
"operator": "=",
"value": "/mnt/logs/developer.log"
},
{
"condition": "OR",
"key": "path",
"operator": "=",
"value": "/mnt/logs/iot.developer.log"
}
]
}
],
"thresholds": [
{
"colorMode": "critical",
"fill": true,
"line": true,
"op": "gt",
"value": 4
}
],
"timeFrom": null,
"timeShift": null,
"title": "服务器处理时间",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"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
}
},
{
"collapsed": true,
"gridPos": {
"h": 1,
"w": 24,
"x": 0,
"y": 23
},
"id": 26,
"panels": [],
"title": "业务日志",
"type": "row"
},
{
"aliasColors": {},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_ELASTIC_DEVELOPER}",
"fill": 1,
"gridPos": {
"h": 9,
"w": 12,
"x": 0,
"y": 24
},
"id": 21,
"legend": {
"avg": false,
"current": false,
"max": false,
"min": false,
"show": true,
"total": false,
"values": false
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"alias": "release",
"bucketAggs": [
{
"field": "@timestamp",
"id": "2",
"settings": {
"interval": "auto",
"min_doc_count": 0,
"trimEdges": 0
},
"type": "date_histogram"
}
],
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"metrics": [
{
"field": "select field",
"id": "1",
"type": "count"
}
],
"orderByTime": "ASC",
"policy": "default",
"query": "tags:release",
"refId": "A",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"value"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": [],
"timeField": "@timestamp"
},
{
"alias": "preRelease",
"bucketAggs": [
{
"field": "@timestamp",
"id": "2",
"settings": {
"interval": "auto",
"min_doc_count": 0,
"trimEdges": 0
},
"type": "date_histogram"
}
],
"metrics": [
{
"field": "select field",
"id": "1",
"type": "count"
}
],
"query": "tags:preRelease",
"refId": "B",
"timeField": "@timestamp"
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "不同环境日志量",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
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{
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"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": false,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_ELASTIC_DEVELOPER}",
"fill": 1,
"gridPos": {
"h": 9,
"w": 12,
"x": 12,
"y": 24
},
"id": 22,
"legend": {
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"current": false,
"max": false,
"min": false,
"show": true,
"total": false,
"values": false
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"alias": "error",
"bucketAggs": [
{
"field": "@timestamp",
"id": "2",
"settings": {
"interval": "auto",
"min_doc_count": 0,
"trimEdges": 0
},
"type": "date_histogram"
}
],
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"metrics": [
{
"field": "select field",
"id": "1",
"type": "count"
}
],
"orderByTime": "ASC",
"policy": "default",
"query": "level:ERROR",
"refId": "A",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"value"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": [],
"timeField": "@timestamp"
},
{
"alias": "total",
"bucketAggs": [
{
"field": "@timestamp",
"id": "2",
"settings": {
"interval": "auto",
"min_doc_count": 0,
"trimEdges": 0
},
"type": "date_histogram"
}
],
"metrics": [
{
"field": "select field",
"id": "1",
"type": "count"
}
],
"query": "*",
"refId": "B",
"timeField": "@timestamp"
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "不同level日志",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"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
}
},
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17,188 | 1 | 724,295 | 26,409 | 0 | cloudflare-zone-analytics | CloudFlare Zone Analytics | Dashboard for Prometheus cloudflare-exporter
https://github.com/lablabs/cloudflare-exporter | 2022-10-14 13:56:37 UTC | 2022-10-14 13:58:10 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 26,409 | 1 | 17,188 | 1 | 0 | Dashboard for Prometheus cloudflare-exporter
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17,389 | 1 | 737,359 | 26,718 | 0 | bind-dns | Bind DNS | Bind DNS Service Statistics.
| 2022-11-11 08:46:25 UTC | 2022-11-11 08:46:40 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 26,718 | 1 | 17,389 | 1 | 0 | Bind DNS Service Statistics.
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17,544 | 1 | 706,461 | 26,937 | 0 | mikrotik-snmp | Mikrotik snmp | Mikrotik monitoring (8.2.1) Edited By Soroush Kaviani | 2022-12-06 13:12:04 UTC | 2022-12-06 13:14:07 UTC | 0 | null | 0 | 0 | nodeExporter | null | 0 | 0 | 26,937 | 1 | 17,544 | 1 | 0 | Mikrotik monitoring (8.2.1) Edited By Soroush Kaviani | {
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17,670 | 1 | 764,425 | 27,133 | 0 | discord-bot | Discord Bot | This is a dashboard for the discord-ext-prometheus Python library. | 2022-12-20 16:56:28 UTC | 2022-12-20 18:52:51 UTC | 0 | discord-ext-prometheus is available on [PyPi](https://pypi.org/project/discord-ext-prometheus/). | 0 | 0 | Other | null | 0 | 0 | 27,133 | 1 | 17,670 | 1 | 0 | null | {
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} | 2023-05-19 14:42:26 UTC | null | 0 | 0 |
16,812 | 1 | 582,917 | 25,777 | 0 | grafanacloud-billing | GrafanaCloud Billing | GrafanaCloud User dashboard for viewing metrics related to billing. | 2022-08-30 01:11:00 UTC | 2022-08-30 01:11:12 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 25,777 | 1 | 16,812 | 1 | 0 | GrafanaCloud User dashboard for viewing metrics related to billing. | {
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} | 2022-08-30 01:11:00 UTC | null | 0 | 0 |
17,479 | 1 | 750,145 | 26,841 | 0 | loki-stack-monitoring-promtail-loki | Loki stack monitoring (Promtail, Loki) | This dashboard can be used to detect issues on the Loki stack, when deployed in Kubernetes. Shows: some error metrics published by Promtail/Loki. Error and warning logs emitted by Promtail/Loki. Memory and CPU usage of Promtail/Loki compared against the Kubernetes memory/cpu limits and requests. | 2022-11-24 22:59:39 UTC | 2022-11-24 23:02:00 UTC | 0 | null | 0 | 0 | collectd | null | 0 | 0 | 26,841 | 1 | 17,479 | 1 | 0 | This dashboard can be used to detect issues on the Loki stack, when deployed in Kubernetes. Shows: some error metrics published by Promtail/Loki. Error and warning logs emitted by Promtail/Loki. Memory and CPU usage of Promtail/Loki compared against the Kubernetes memory/cpu limits and requests. | {
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"time_options": [
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} | 2023-04-07 03:50:44 UTC | null | 0 | 0 |
18,599 | 1 | 345,879 | 28,514 | 0 | open-liberty | Open Liberty - mpMetrics-5.0 | Created by the Open Liberty observability team | 2023-04-24 20:39:35 UTC | 2023-04-27 20:58:21 UTC | 0 | This dashboard visualizes CPU, Heap, REST, Servlet, Connection Pool, Sessions, Threadpool, GC, and other JVM metrics.
This dashboard works with Open Liberty servers running with `mpMetrics-5.x` feature. For prior mpMetrics versions, use the [Open Liberty - mpMetrics-2.x-4.x](https://grafana.com/grafana/dashboards/11706) dashboard
View our Open Liberty MicroProfile 3.3 blog post for detailed [setup instructions](https://openliberty.io/blog/2020/04/09/microprofile-3-3-open-liberty-20004.html#gra).
### Open Liberty
- Enable the **/metrics** endpoint by enabling the desired **`mpMetrics`** feature (e.g. `mpMetrics-5.0`) in the `server.xml`.
- Learn about [Monitoring with MicroProfile metrics](https://openliberty.io/docs/latest/introduction-monitoring-metrics.html)
- Check out our Open Liberty Guide on [Providing metrics from a Microservice](https://openliberty.io/guides/microprofile-metrics.html)
### Prometheus
Configure the `scrape_configs` section of *prometheus.yml* in one of the following ways.
For metrics on an **insecure** endpoint:
```
- job_name: 'liberty'
scrape_interval: 5s
static_configs:
- targets: ['localhost:9080']
```
For metrics on an **secure** endpoint using **Basic Authentication**:
```
- job_name: 'liberty-secure'
scrape_interval: 5s
static_configs:
- targets: ['localhost:9443']
basic_auth:
username: "<your-username>"
password: "<your-password>"
tls_config:
insecure_skip_verify: true
scheme: "https"
```
###
| 83,705 | 83,706 | Other | null | 0 | 0 | 28,514 | 1 | 18,599 | 1 | 0 | Created by the Open Liberty observability team | {
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} | 2022-12-28 20:29:59 UTC | null | 0 | 0 |
18,112 | 1 | 796,794 | 27,816 | 0 | summary | Summary | myDashboard1 | 2023-02-15 18:30:31 UTC | 2023-02-15 18:34:10 UTC | 0 | null | 0 | 0 | Telegraf | null | 0 | 0 | 27,816 | 1 | 18,112 | 1 | 0 | null | {
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} | 2023-05-26 19:15:36 UTC | 2023-05-26 19:31:50 UTC | 0 | 0 |
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} | 2022-12-22 12:08:56 UTC | 2022-12-22 12:09:58 UTC | 0 | 0 |
17,806 | 1 | 17,382 | 27,299 | 0 | asonix-relay | AodeRelay ActivityPub Relay | Insights into the AodeRelay ActivityPub Relay software. | 2023-01-05 10:25:20 UTC | 2023-01-06 08:11:05 UTC | 0 | The AodeRelay ActivityPub Relay built by @asonix@masto.asonix.dog can export Prometheus metrics since version v0.3.78.
See their repo for more information on how to enable its scraping endpoint. | 80,368 | 80,369 | Other | null | 0 | 0 | 27,299 | 1 | 17,806 | 1 | 0 | Insights in the running Asonix Relay | {
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17,629 | 1 | 761,884 | 27,081 | 0 | prometheus-flask-exporter-dashboard | Prometheus Flask exporter dashboard | Dashboard | 2022-12-15 19:15:02 UTC | 2022-12-15 19:16:55 UTC | 0 | null | 0 | 0 | Other | null | 0 | 0 | 27,081 | 1 | 17,629 | 1 | 0 | Dashboard | {
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17,686 | 1 | 764,262 | 27,150 | 0 | kubernetes-cluster-overview | Kubernetes - Cluster Overview | Kubernetes Cluster Overview | 2022-12-22 12:14:12 UTC | 2022-12-22 12:14:57 UTC | 0 | Kubernetes Cluster Overview | 79,962 | 79,963 | Other | null | 0 | 0 | 27,150 | 1 | 17,686 | 1 | 0 | Overview of Kubernetes cluster-level metrics | {
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} | 2022-12-22 12:14:12 UTC | 2022-12-22 12:14:56 UTC | 0 | 0 |
18,505 | 1 | 835,147 | 28,383 | 0 | autometrics-overview | Autometrics Overview | Shows the functions with the highest request and error rates | 2023-04-12 17:55:40 UTC | 2023-04-12 17:58:09 UTC | 0 | This is a dashboard that will work for any project instrumented with [Autometrics](https://autometrics.dev). | 83,302 | 83,303 | Other | null | 0 | 0 | 28,383 | 1 | 18,505 | 1 | 0 | null | {
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18,753 | 1 | 857,441 | 28,771 | 0 | solar-pv-system | Solar PV System | Solar PV System,
You can monitor the energy flow of the solar pv system.
| 2023-05-15 16:24:19 UTC | 2023-05-15 16:28:13 UTC | 0 | null | 84,344 | 84,345 | null | null | 0 | 0 | 28,771 | 1 | 18,753 | 1 | 0 | Iammeter Solar PV System,
You can monitor the energy flow of the solar pv system.
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} | 2023-05-15 16:24:19 UTC | 2023-05-15 16:28:13 UTC | 0 | 0 |