https://github.com/duckdb/duckdb

DuckDB is an analytical in-process SQL database management system
https://github.com/duckdb/duckdb

analytics database embedded-database olap sql

Last synced: 5 days ago
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DuckDB is an analytical in-process SQL database management system

• Host: GitHub
• URL: https://github.com/duckdb/duckdb
• Owner: duckdb
• License: mit
• Created: 2018-06-26T15:04:45.000Z (over 6 years ago)
• Default Branch: main
• Last Pushed: 2024-10-29T09:53:26.000Z (about 2 months ago)
• Last Synced: 2024-10-29T09:56:17.051Z (about 2 months ago)
• Topics: analytics, database, embedded-database, olap, sql
• Language: C++
• Homepage: http://www.duckdb.org
• Size: 299 MB
• Stars: 23,812
• Watchers: 206
• Forks: 1,894
• Open Issues: 336
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md
  • Citation: CITATION.cff

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README

        


  

    

    

    

  








  

  

  



## DuckDB

DuckDB is a high-performance analytical database system. It is designed to be fast, reliable, portable, and easy to use. DuckDB provides a rich SQL dialect, with support far beyond basic SQL. DuckDB supports arbitrary and nested correlated subqueries, window functions, collations, complex types (arrays, structs, maps), and [several extensions designed to make SQL easier to use](https://duckdb.org/docs/guides/sql_features/friendly_sql).

DuckDB is available as a [standalone CLI application](https://duckdb.org/docs/api/cli/overview) and has clients for [Python](https://duckdb.org/docs/api/python/overview), [R](https://duckdb.org/docs/api/r), [Java](https://duckdb.org/docs/api/java), [Wasm](https://duckdb.org/docs/api/wasm/overview), etc., with deep integrations with packages such as [pandas](https://duckdb.org/docs/guides/python/sql_on_pandas) and [dplyr](https://duckdblabs.github.io/duckplyr/).

For more information on using DuckDB, please refer to the [DuckDB documentation](https://duckdb.org/docs/).

## Installation

If you want to install DuckDB, please see [our installation page](https://duckdb.org/docs/installation/) for instructions.

## Data Import

For CSV files and Parquet files, data import is as simple as referencing the file in the FROM clause:

```sql

SELECT * FROM 'myfile.csv';

SELECT * FROM 'myfile.parquet';

```

Refer to our [Data Import](https://duckdb.org/docs/data/overview) section for more information.

## SQL Reference

The documentation contains a [SQL introduction and reference](https://duckdb.org/docs/sql/introduction).

## Development

For development, DuckDB requires [CMake](https://cmake.org), Python3 and a `C++11` compliant compiler. Run `make` in the root directory to compile the sources. For development, use `make debug` to build a non-optimized debug version. You should run `make unit` and `make allunit` to verify that your version works properly after making changes. To test performance, you can run `BUILD_BENCHMARK=1 BUILD_TPCH=1 make` and then perform several standard benchmarks from the root directory by executing `./build/release/benchmark/benchmark_runner`. The details of benchmarks are in our [Benchmark Guide](benchmark/README.md).

Please also refer to our [Build Guide](https://duckdb.org/dev/building) and [Contribution Guide](CONTRIBUTING.md).

## Support

See the [Support Options](https://duckdblabs.com/support/) page.