datafusion 43.0.0

DataFusion is an in-memory query engine that uses Apache Arrow as the memory model
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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="./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 to 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 Ray](https://github.com/apache/datafusion-ray/) provides a distributed version of DataFusion that scales
  out on Ray clusters.
- [DataFusion Comet](https://github.com/apache/datafusion-comet/) is an accelerator for Apache Spark based on
  DataFusion.

"Out of the box,"
DataFusion offers [SQL] and [`Dataframe`] 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 some important information

- [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 use. [Click Here](https://datafusion.apache.org/user-guide/introduction.html#known-users) to see a list 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 type function 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
- `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

Optional features:

- `avro`: support for reading the [Apache Avro] format
- `backtrace`: include backtrace information in error messages
- `pyarrow`: conversions between PyArrow and DataFusion types
- `serde`: enable arrow-schema's `serde` feature

[apache avro]: https://avro.apache.org/
[apache parquet]: https://parquet.apache.org/

## Rust Version Compatibility Policy

DataFusion's Minimum Required Stable Rust Version (MSRV) policy is to support stable [4 latest
Rust versions](https://releases.rs) OR the stable minor Rust version as of 4 months, whichever is lower.

For example, given the releases `1.78.0`, `1.79.0`, `1.80.0`, `1.80.1` and `1.81.0` DataFusion will support 1.78.0, which is 3 minor versions prior to the most minor recent `1.81`.

If a hotfix is released for the minimum supported Rust version (MSRV), the MSRV will be the minor version with all hotfixes, even if it surpasses the four-month window.

We enforce this policy using a [MSRV CI Check](https://github.com/search?q=repo%3Aapache%2Fdatafusion+rust-version+language%3ATOML+path%3A%2F%5ECargo.toml%2F&type=code)

## DataFusion API evolution policy

Public methods in Apache DataFusion are subject to evolve as part of the API lifecycle.
Deprecated methods will be phased out in accordance with the [policy](https://datafusion.apache.org/library-user-guide/api-health.html), ensuring the API is stable and healthy.