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

• Project Site
• Installation
• Rust Getting Started
• Rust DataFrame API
• Rust API docs
• Rust Examples
• Python DataFrame API
• 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 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 a Cargo.lock

datafusion is intended for use as a library and thus purposely does not have a Cargo.lock file checked in. You can read more about the distinction in the Cargo book.

CI tests always run against the latest compatible versions of all dependencies (the equivalent of doing cargo update), as suggested in the Cargo CI guide and we rely on Dependabot for other upgrades. This strategy has two problems that occasionally arise:

1. CI failures when downstream libraries upgrade in some non compatible way
2. Local development builds that fail when DataFusion inadvertently relies on a feature in a newer version of a dependency than declared in Cargo.toml (e.g. a new method is added to a trait that we use).

However, we think the current strategy is the best tradeoff between maintenance overhead and user experience and ensures DataFusion always works with the latest compatible versions of all dependencies. If you encounter either of these problems, please open an issue or PR.