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Summary | This document contains [DuckDB's official documentation and guides](https://duckdb.org/) in a single-file easy-to-search form.
If you find any issues, please report them [as a GitHub issue](https://github.com/duckdb/duckdb-web/issues).
Contributions are very welcome in the form of [pull requests](https://github.com/duckdb/duckdb-web/pulls).
If you are considering submitting a contribution to the documentation, please consult our [contributor guide](https://github.com/duckdb/duckdb-web/blob/main/CONTRIBUTING.md).
Code repositories:
* DuckDB source code: [github.com/duckdb/duckdb](https://github.com/duckdb/duckdb)
* DuckDB documentation source code: [github.com/duckdb/duckdb-web](https://github.com/duckdb/duckdb-web) | 184 | [
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|||
Connect | Connect | Connect or Create a Database | To use DuckDB, you must first create a connection to a database. The exact syntax varies between the [client APIs](#docs:api:overview) but it typically involves passing an argument to configure persistence. | 43 | [
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|
Connect | Connect | Persistence | DuckDB can operate in both persistent mode, where the data is saved to disk, and in in-memory mode, where the entire data set is stored in the main memory.
> **Tip. ** Both persistent and in-memory databases use spilling to disk to facilitate larger-than-memory workloads (i.e., out-of-core-processing).
#### Persistent Database {#docs:connect:overview::persistent-database}
To create or open a persistent database, set the path of the database file, e.g., `my_database.duckdb`, when creating the connection.
This path can point to an existing database or to a file that does not yet exist and DuckDB will open or create a database at that location as needed.
The file may have an arbitrary extension, but `.db` or `.duckdb` are two common choices with `.ddb` also used sometimes.
Starting with v0.10, DuckDB's storage format is [backwards-compatible](#docs:internals:storage::backward-compatibility), i.e., DuckDB is able to read database files produced by an older versions of DuckDB.
For example, DuckDB v0.10 can read and operate on files created by the previous DuckDB version, v0.9.
For more details on DuckDB's storage format, see the [storage page](#docs:internals:storage).
#### In-Memory Database {#docs:connect:overview::in-memory-database}
DuckDB can operate in in-memory mode. In most clients, this can be activated by passing the special value `:memory:` as the database file or omitting the database file argument. In in-memory mode, no data is persisted to disk, therefore, all data is lost when the process finishes. | 362 | [
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|
Connect | Concurrency | Handling Concurrency | DuckDB has two configurable options for concurrency:
1. One process can both read and write to the database.
2. Multiple processes can read from the database, but no processes can write ([`access_mode = 'READ_ONLY'`](#docs:configuration:overview::configuration-reference)).
When using option 1, DuckDB supports multiple writer threads using a combination of [MVCC (Multi-Version Concurrency Control)](https://en.wikipedia.org/wiki/Multiversion_concurrency_control) and optimistic concurrency control (see [Concurrency within a Single Process](#::concurrency-within-a-single-process)), but all within that single writer process. The reason for this concurrency model is to allow for the caching of data in RAM for faster analytical queries, rather than going back and forth to disk during each query. It also allows the caching of functions pointers, the database catalog, and other items so that subsequent queries on the same connection are faster.
> DuckDB is optimized for bulk operations, so executing many small transactions is not a primary design goal. | 214 | [
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|
Connect | Concurrency | Concurrency within a Single Process | DuckDB supports concurrency within a single process according to the following rules. As long as there are no write conflicts, multiple concurrent writes will succeed. Appends will never conflict, even on the same table. Multiple threads can also simultaneously update separate tables or separate subsets of the same table. Optimistic concurrency control comes into play when two threads attempt to edit (update or delete) the same row at the same time. In that situation, the second thread to attempt the edit will fail with a conflict error. | 102 | [
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|
Connect | Concurrency | Writing to DuckDB from Multiple Processes | Writing to DuckDB from multiple processes is not supported automatically and is not a primary design goal (see [Handling Concurrency](#::handling-concurrency)).
If multiple processes must write to the same file, several design patterns are possible, but would need to be implemented in application logic. For example, each process could acquire a cross-process mutex lock, then open the database in read/write mode and close it when the query is complete. Instead of using a mutex lock, each process could instead retry the connection if another process is already connected to the database (being sure to close the connection upon query completion). Another alternative would be to do multi-process transactions on a MySQL, PostgreSQL, or SQLite database, and use DuckDB's [MySQL](#docs:extensions:mysql), [PostgreSQL](#docs:extensions:postgres), or [SQLite](#docs:extensions:sqlite) extensions to execute analytical queries on that data periodically.
Additional options include writing data to Parquet files and using DuckDB's ability to [read multiple Parquet files](#docs:data:parquet:overview), taking a similar approach with [CSV files](#docs:data:csv:overview), or creating a web server to receive requests and manage reads and writes to DuckDB. | 254 | [
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|
Connect | Concurrency | Optimistic Concurrency Control | DuckDB uses [optimistic concurrency control](https://en.wikipedia.org/wiki/Optimistic_concurrency_control), an approach generally considered to be the best fit for read-intensive analytical database systems as it speeds up read query processing. As a result any transactions that modify the same rows at the same time will cause a transaction conflict error:
```console
Transaction conflict: cannot update a table that has been altered!
```
> **Tip. ** A common workaround when a transaction conflict is encountered is to rerun the transaction. | 108 | [
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|
Data Import | Importing Data | The first step to using a database system is to insert data into that system.
DuckDB provides can directly connect to [many popular data sources](#docs:data:data_sources) and offers several data ingestion methods that allow you to easily and efficiently fill up the database.
On this page, we provide an overview of these methods so you can select which one is best suited for your use case. | 79 | [
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||
Data Import | Importing Data | `INSERT` Statements | `INSERT` statements are the standard way of loading data into a database system. They are suitable for quick prototyping, but should be avoided for bulk loading as they have significant per-row overhead.
```sql
INSERT INTO people VALUES (1, 'Mark');
```
For a more detailed description, see the [page on the `INSERT statement`](#docs:data:insert). | 77 | [
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|
Data Import | Importing Data | CSV Loading | Data can be efficiently loaded from CSV files using several methods. The simplest is to use the CSV file's name:
```sql
SELECT * FROM 'test.csv';
```
Alternatively, use the [`read_csv` function](#docs:data:csv:overview) to pass along options:
```sql
SELECT * FROM read_csv('test.csv', header = false);
```
Or use the [`COPY` statement](#docs:sql:statements:copy::copy--from):
```sql
COPY tbl FROM 'test.csv' (HEADER false);
```
It is also possible to read data directly from **compressed CSV files** (e.g., compressed with [gzip](https://www.gzip.org/)):
```sql
SELECT * FROM 'test.csv.gz';
```
DuckDB can create a table from the loaded data using the [`CREATE TABLE ... AS SELECT` statement](#docs:sql:statements:create_table::create-table--as-select-ctas):
```sql
CREATE TABLE test AS
SELECT * FROM 'test.csv';
```
For more details, see the [page on CSV loading](#docs:data:csv:overview). | 239 | [
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|
Data Import | Importing Data | Parquet Loading | Parquet files can be efficiently loaded and queried using their filename:
```sql
SELECT * FROM 'test.parquet';
```
Alternatively, use the [`read_parquet` function](#docs:data:parquet:overview):
```sql
SELECT * FROM read_parquet('test.parquet');
```
Or use the [`COPY` statement](#docs:sql:statements:copy::copy--from):
```sql
COPY tbl FROM 'test.parquet';
```
For more details, see the [page on Parquet loading](#docs:data:parquet:overview). | 121 | [
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