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apache / datafusion / refs/heads/arrow2
commit744b2626081db95a254fc882820fc7812f95aa51[log] [tgz]
authorQingping Hou <dave2008713@gmail.com>Tue Mar 22 00:29:49 2022 -0700
committerQingping Hou <dave2008713@gmail.com>Tue Mar 22 00:33:47 2022 -0700
tree84e2564a64d8cb17f9dfe2f1fc629edaa78ab7ec
parentca952bd33402816dbb1550debb9b8cac3b13e8f2 [diff]
Officially maintained Arrow2 branch

    Co-authored-by: Jorge C. Leitao <jorgecarleitao@gmail.com>
    Co-authored-by: Yijie Shen <henry.yijieshen@gmail.com>
    Co-authored-by: Guillaume Balaine <igosuki@gmail.com>
    Co-authored-by: Guillaume Balaine <igosuki.github@gmail.com>
240 files changed
tree: 84e2564a64d8cb17f9dfe2f1fc629edaa78ab7ec
  1. .github/
  2. ballista/
  3. ballista-examples/
  4. benchmarks/
  5. ci/
  6. conbench/
  7. datafusion/
  8. datafusion-cli/
  9. datafusion-common/
  10. datafusion-examples/
  11. datafusion-expr/
  12. datafusion-jit/
  13. datafusion-physical-expr/
  14. datafusion-proto/
  15. dev/
  16. docs/
  17. integration-tests/
  18. python/
  19. parquet-testing
  20. testing
  21. .asf.yaml
  22. .dir-locals.el
  23. .dockerignore
  24. .env
  25. .gitattributes
  26. .github_changelog_generator
  27. .gitignore
  28. .gitmodules
  29. .hadolint.yaml
  30. .pre-commit-config.yaml
  31. .readthedocs.yml
  32. Cargo.toml
  33. CHANGELOG.md
  34. CODE_OF_CONDUCT.md
  35. CONTRIBUTING.md
  36. header
  37. LICENSE.txt
  38. NOTICE.txt
  39. pre-commit.sh
  40. README.md
  41. rustfmt.toml
README.md

DataFusion

DataFusion is an extensible query execution framework, written in Rust, that uses Apache Arrow as its in-memory format.

DataFusion supports both an SQL and a DataFrame API for building logical query plans as well as a query optimizer and execution engine capable of parallel execution against partitioned data sources (CSV and Parquet) using threads.

DataFusion also supports distributed query execution via the Ballista crate.

Use Cases

DataFusion is used to create modern, fast and efficient data pipelines, ETL processes, and database systems, which need the performance of Rust and Apache Arrow and want to provide their users the convenience of an SQL interface or a DataFrame API.

Why DataFusion?

  • High Performance: Leveraging Rust and Arrow's memory model, DataFusion achieves very high performance
  • Easy to Connect: Being part of the Apache Arrow ecosystem (Arrow, Parquet and Flight), DataFusion works well with the rest of the big data ecosystem
  • Easy to Embed: Allowing extension at almost any point in its design, DataFusion can be tailored for your specific usecase
  • High Quality: Extensively tested, both by itself and with the rest of the Arrow ecosystem, DataFusion can be used as the foundation for production systems.

Known Uses

Projects that adapt to or serve as plugins to DataFusion:

Here are some of the projects known to use DataFusion:

(if you know of another project, please submit a PR to add a link!)

Example Usage

Please see example usage to find how to use DataFusion.

Roadmap

Please see Roadmap for information of where the project is headed.

Architecture Overview

There is no formal document describing DataFusion's architecture yet, but the following presentations offer a good overview of its different components and how they interact together.

  • (March 2021): The DataFusion architecture is described in Query Engine Design and the Rust-Based DataFusion in Apache Arrow: recording (DataFusion content starts ~ 15 minutes in) and slides
  • (February 2021): How DataFusion is used within the Ballista Project is described in *Ballista: Distributed Compute with Rust and Apache Arrow: recording

User's guide

Please see User Guide for more information about DataFusion.

Contribution Guide

Please see Contribution Guide for information about contributing to DataFusion.