Apache DataFusion

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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 offers a Python interface for SQL and DataFrame queries.
DataFusion Ray provides a distributed version of DataFusion that scales out on Ray clusters.
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.

Here are links to some important information

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 to see a list known users.

Contributing to DataFusion

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

Crate features

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

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
recursive_protection: uses recursive for stack overflow protection.

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

Rust Version Compatibility Policy

The Rust toolchain releases are tracked at Rust Versions and follow semantic versioning. A Rust toolchain release can be identified by a version string like 1.80.0, or more generally major.minor.patch.

DataFusion's supports the last 4 stable Rust minor versions released and any such versions released within the last 4 months.

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.

Note: If a Rust hotfix is released for the current MSRV, the MSRV will be updated to the specific minor version that includes all applicable hotfixes preceding other policies.

DataFusion enforces MSRV policy using a MSRV CI Check

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.

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.