Apache DataFusion

Website | API Docs | Chat

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

• DataFusion Comet is an accelerator for Apache Spark based on

"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
• sql: Support for sql parsing / 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 for stack overflow protection.

• 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

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.