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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), andseveral extensions designed to make SQL easier to use.

DuckDB is available as astandalone CLI application and has clients forPython,R,Java,Wasm, etc., with deep integrations with packages such aspandas anddplyr.

For more information on using DuckDB, please refer to theDuckDB documentation.

If you want to install DuckDB, please seeour installation page for instructions.

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

SELECT*FROM'myfile.csv';SELECT*FROM'myfile.parquet';

Refer to ourData Import section for more information.

The documentation contains aSQL introduction and reference.

For development, DuckDB requiresCMake, Python 3 and aC++11 compliant compiler. In the root directory, runmake to compile the sources. For development, usemake debug to build a non-optimized debug version. You should runmake unit andmake allunit to verify that your version works properly after making changes. To test performance, you can runBUILD_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 ourBenchmark Guide.

Please also refer to ourBuild Guide andContribution Guide.

See theSupport Options page and the dedicatedendoflife.date page.