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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

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.

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

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
• parquet_encryption: support for using Parquet Modular Encryption
• 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

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.

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.