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# Apache DataFusion

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[Website](https://datafusion.apache.org/) |
[API Docs](https://docs.rs/datafusion/latest/datafusion/) |
[Chat](https://discord.com/channels/885562378132000778/885562378132000781)

<a href="https://datafusion.apache.org/">
  <img src="https://github.com/apache/datafusion/raw/HEAD/docs/source/_static/images/2x_bgwhite_original.png" width="512" alt="logo"/>
</a>

DataFusion is an extensible query engine written in [Rust] that
uses [Apache Arrow] as its in-memory format.

This crate provides libraries and binaries for developers building fast and
feature-rich database and analytic systems, customized for particular workloads.
See [use cases] for examples. The following related subprojects target end users:

- [DataFusion Python](https://github.com/apache/datafusion-python/) offers a Python interface for SQL and DataFrame
  queries.
- [DataFusion Comet](https://github.com/apache/datafusion-comet/) is an accelerator for Apache Spark based on
  DataFusion.

"Out of the box,"
DataFusion offers [SQL](https://datafusion.apache.org/user-guide/sql/index.html) and [DataFrame](https://datafusion.apache.org/user-guide/dataframe.html) APIs, excellent [performance],
built-in support for CSV, Parquet, JSON, and Avro, extensive customization, and
a great community.

DataFusion features a full query planner, a columnar, streaming, multi-threaded,
vectorized execution engine, and partitioned data sources. You can
customize DataFusion at almost all points including additional data sources,
query languages, functions, custom operators and more.
See the [Architecture] section for more details.

[rust]: http://rustlang.org
[apache arrow]: https://arrow.apache.org
[use cases]: https://datafusion.apache.org/user-guide/introduction.html#use-cases
[python bindings]: https://github.com/apache/datafusion-python
[performance]: https://benchmark.clickhouse.com/
[architecture]: https://datafusion.apache.org/contributor-guide/architecture.html

Here are links to important resources:

- [Project Site](https://datafusion.apache.org/)
- [Installation](https://datafusion.apache.org/user-guide/cli/installation.html)
- [Rust Getting Started](https://datafusion.apache.org/user-guide/example-usage.html)
- [Rust DataFrame API](https://datafusion.apache.org/user-guide/dataframe.html)
- [Rust API docs](https://docs.rs/datafusion/latest/datafusion)
- [Rust Examples](https://github.com/apache/datafusion/tree/main/datafusion-examples)
- [Python DataFrame API](https://arrow.apache.org/datafusion-python/)
- [Architecture](https://docs.rs/datafusion/latest/datafusion/index.html#architecture)

## What can you do with this crate?

DataFusion is great for building projects such as domain-specific query engines, new database platforms and data pipelines, query languages and more.
It lets you start quickly from a fully working engine, and then customize those features specific to your needs. See the [list of known users](https://datafusion.apache.org/user-guide/introduction.html#known-users).

## Contributing to DataFusion

Please see the [contributor guide] and [communication] pages for more information.

[contributor guide]: https://datafusion.apache.org/contributor-guide
[communication]: https://datafusion.apache.org/contributor-guide/communication.html

## Crate features

This crate has several [features] which can be specified in your `Cargo.toml`.

[features]: https://doc.rust-lang.org/cargo/reference/features.html

Default features:

- `nested_expressions`: functions for working with nested types such as `array_to_string`
- `compression`: reading files compressed with `xz2`, `bzip2`, `flate2`, and `zstd`
- `crypto_expressions`: cryptographic functions such as `md5` and `sha256`
- `datetime_expressions`: date and time functions such as `to_timestamp`
- `encoding_expressions`: `encode` and `decode` functions
- `parquet`: support for reading the [Apache Parquet] format
- `sql`: support for SQL parsing and planning
- `regex_expressions`: regular expression functions, such as `regexp_match`
- `unicode_expressions`: include Unicode-aware functions such as `character_length`
- `unparser`: enables support to reverse LogicalPlans back into SQL
- `recursive_protection`: uses [recursive](https://docs.rs/recursive/latest/recursive/) for stack overflow protection.

Optional features:

- `avro`: support for reading the [Apache Avro] format
- `backtrace`: include backtrace information in error messages
- `parquet_encryption`: support for using [Parquet Modular Encryption]
- `serde`: enable arrow-schema's `serde` feature

[apache avro]: https://avro.apache.org/
[apache parquet]: https://parquet.apache.org/
[parquet modular encryption]: https://parquet.apache.org/docs/file-format/data-pages/encryption/

## DataFusion API Evolution and Deprecation Guidelines

Public methods in Apache DataFusion evolve over time: while we try to maintain a
stable API, we also improve the API over time. As a result, we typically
deprecate methods before removing them, according to the [deprecation guidelines].

[deprecation guidelines]: https://datafusion.apache.org/contributor-guide/api-health.html

## Dependencies and `Cargo.lock`

Following the [guidance] on committing `Cargo.lock` files, this project commits
its `Cargo.lock` file.

CI uses the committed `Cargo.lock` file, and dependencies are updated regularly
using [Dependabot] PRs.

[guidance]: https://blog.rust-lang.org/2023/08/29/committing-lockfiles.html
[dependabot]: https://docs.github.com/en/code-security/dependabot/working-with-dependabot
