52 lines
3.4 KiB
Markdown
52 lines
3.4 KiB
Markdown
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<picture>
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<source media="(prefers-color-scheme: light)" srcset="logo/DuckDB_Logo-horizontal.svg">
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<source media="(prefers-color-scheme: dark)" srcset="logo/DuckDB_Logo-horizontal-dark-mode.svg">
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<img alt="DuckDB logo" src="logo/DuckDB_Logo-horizontal.svg" height="100">
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</div>
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<br>
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<p align="center">
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<a href="https://github.com/duckdb/duckdb/actions"><img src="https://github.com/duckdb/duckdb/actions/workflows/Main.yml/badge.svg?branch=main" alt="Github Actions Badge"></a>
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<a href="https://discord.gg/tcvwpjfnZx"><img src="https://shields.io/discord/909674491309850675" alt="discord" /></a>
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<a href="https://github.com/duckdb/duckdb/releases/"><img src="https://img.shields.io/github/v/release/duckdb/duckdb?color=brightgreen&display_name=tag&logo=duckdb&logoColor=white" alt="Latest Release"></a>
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</p>
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## DuckDB
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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), and [several extensions designed to make SQL easier to use](https://duckdb.org/docs/stable/sql/dialect/friendly_sql.html).
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DuckDB is available as a [standalone CLI application](https://duckdb.org/docs/stable/clients/cli/overview) and has clients for [Python](https://duckdb.org/docs/stable/clients/python/overview), [R](https://duckdb.org/docs/stable/clients/r), [Java](https://duckdb.org/docs/stable/clients/java), [Wasm](https://duckdb.org/docs/stable/clients/wasm/overview), etc., with deep integrations with packages such as [pandas](https://duckdb.org/docs/guides/python/sql_on_pandas) and [dplyr](https://duckdb.org/docs/stable/clients/r#duckplyr-dplyr-api).
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For more information on using DuckDB, please refer to the [DuckDB documentation](https://duckdb.org/docs/stable/).
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## Installation
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If you want to install DuckDB, please see [our installation page](https://duckdb.org/docs/installation/) for instructions.
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## Data Import
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For CSV files and Parquet files, data import is as simple as referencing the file in the FROM clause:
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```sql
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SELECT * FROM 'myfile.csv';
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SELECT * FROM 'myfile.parquet';
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```
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Refer to our [Data Import](https://duckdb.org/docs/stable/data/overview) section for more information.
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## SQL Reference
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The documentation contains a [SQL introduction and reference](https://duckdb.org/docs/stable/sql/introduction).
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## Development
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For development, DuckDB requires [CMake](https://cmake.org), Python 3 and a `C++11` compliant compiler. In the root directory, run `make` 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).
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Please also refer to our [Build Guide](https://duckdb.org/docs/stable/dev/building/overview) and [Contribution Guide](CONTRIBUTING.md).
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## Support
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See the [Support Options](https://duckdblabs.com/support/) page and the dedicated [`endoflife.date`](https://endoflife.date/duckdb) page.
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