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apache / datafusion / refs/tags/41.0.0
commitb10b820acb6ad92b5d69810e3d4de0ef6f2d6a87[log] [tgz]
authorAndy Grove <agrove@apache.org>Thu Aug 08 11:03:10 2024 -0600
committerAndy Grove <agrove@apache.org>Thu Aug 08 11:03:10 2024 -0600
tree065ad9f4589c052491d399ba5d4da456d734e78f
parent26bafec04263b7c33d5418d34419ff47d2a50e28 [diff]
regenerate example parquet file
2 files changed
tree: 065ad9f4589c052491d399ba5d4da456d734e78f
  1. .github/
  2. benchmarks/
  3. ci/
  4. datafusion/
  5. datafusion-cli/
  6. datafusion-examples/
  7. dev/
  8. docs/
  9. python/
  10. test-utils/
  11. parquet-testing
  12. testing
  13. .asf.yaml
  14. .dockerignore
  15. .editorconfig
  16. .gitattributes
  17. .gitignore
  18. .gitmodules
  19. Cargo.toml
  20. CHANGELOG.md
  21. clippy.toml
  22. CODE_OF_CONDUCT.md
  23. CONTRIBUTING.md
  24. doap.rdf
  25. header
  26. LICENSE.txt
  27. NOTICE.txt
  28. pre-commit.sh
  29. README.md
  30. rustfmt.toml
  31. taplo.toml
README.md

Apache DataFusion

Crates.io Apache licensed Build Status Discord chat

Website | Guides | API Docs | Chat

Apache DataFusion is a very fast, extensible query engine for building high-quality data-centric systems in Rust, using the Apache Arrow in-memory format. Python Bindings are also available. DataFusion offers SQL and Dataframe APIs, excellent performance, built-in support for CSV, Parquet, JSON, and Avro, extensive customization, and a great community.

Here are links to some important information

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

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

DataFusion's Minimum Required Stable Rust Version (MSRV) policy is to support each stable Rust version for 6 months after it is released. This generally translates to support for the most recent 3 to 4 stable Rust versions.

We enforce this policy using a MSRV CI Check