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https://github.com/duckdb/duckdb

DuckDB is an in-process SQL OLAP Database Management System
https://github.com/duckdb/duckdb

analytics database embedded-database olap sql

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DuckDB is an in-process SQL OLAP Database Management System

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



## Installation

If you want to install and use DuckDB, please see [our website](https://www.duckdb.org) for installation and usage 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 [website](https://duckdb.org/docs/sql/introduction) contains a reference of functions and SQL constructs available in DuckDB.



## 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 detail of benchmarks is in our [Benchmark Guide](benchmark/README.md).



Please also refer to our [Contribution Guide](CONTRIBUTING.md).