Awesome
<!--- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -->Apache DataFusion
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 asarray_to_stringcompression: reading files compressed withxz2,bzip2,flate2, andzstdcrypto_expressions: cryptographic functions such asmd5andsha256datetime_expressions: date and time functions such asto_timestampencoding_expressions:encodeanddecodefunctionsparquet: support for reading the Apache Parquet formatregex_expressions: regular expression functions, such asregexp_matchunicode_expressions: Include unicode aware functions such ascharacter_lengthunparser: enables support to reverse LogicalPlans back into SQL
Optional features:
avro: support for reading the Apache Avro formatbacktrace: include backtrace information in error messagespyarrow: conversions between PyArrow and DataFusion typesserde: enable arrow-schema'sserdefeature
Rust Version Compatibility Policy
DataFusion's Minimum Required Stable Rust Version (MSRV) policy is to support stable 4 latest Rust versions 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
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, ensuring the API is stable and healthy.