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apache / datafusion / refs/tags/50.0.0-rc1
tag31cddec72011d40e7e073edc0ec869840e608b3b
taggerxudong963 <wxd963996380@gmail.com>Sat Sep 13 16:28:09 2025 +0800
object10343c18292fc3a9ca62b59c8bb530169fe79b82 
50.0.0-rc1
commit10343c18292fc3a9ca62b59c8bb530169fe79b82[log] [tgz]
authorAndrew Lamb <andrew@nerdnetworks.org>Sat Sep 13 01:17:34 2025 -0700
committerGitHub <noreply@github.com>Sat Sep 13 16:17:34 2025 +0800
tree82989472e421f816f0acc72419a16e811659a575
parent9e7141f36ddbb0ad9c79ed3c95e332f495c1b078 [diff]
Revert #17295 (Support from-first SQL syntax) (#17520) (#17544)

* Add failing test

* Fix regression in SELECT FROM syntax with WHERE clause

When using 'SELECT FROM table WHERE condition', the query should create
an empty projection (no columns) while still filtering rows. This was
broken by PR #17295 which added FROM-first syntax support.

The issue was that both 'FROM table' and 'SELECT FROM table' resulted
in empty projection lists, making them indistinguishable. The fix checks
for the presence of a WHERE clause to differentiate:
- 'FROM table' (no WHERE) -> add wildcard projection (all columns)
- 'SELECT FROM table WHERE ...' -> keep empty projection

Also updates the test expectation to correctly show the empty Projection
node in the query plan.

Fixes #17513

* Revert

* Fix regression: SELECT FROM syntax should return empty projection

Removes automatic wildcard projection for empty projections, fixing
the regression where `SELECT FROM table` incorrectly returned all
columns instead of empty projection.

Note: This temporarily breaks FROM-first syntax. A proper fix would
require distinguishing between `FROM table` and `SELECT FROM table`
at the parser level.

Fixes #17513

🤖 Generated with [Claude Code](https://claude.ai/code)



* add a better regression test

* remove comment

* fmt

* Update datafusion/sqllogictest/test_files/projection.slt



* Update datafusion/core/tests/sql/select.rs



* revert docs

* fmt

---------

Co-authored-by: Adrian Garcia Badaracco <1755071+adriangb@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Oleks V <comphead@users.noreply.github.com>
5 files changed
tree: 82989472e421f816f0acc72419a16e811659a575
  1. .devcontainer/
  2. .github/
  3. benchmarks/
  4. ci/
  5. datafusion/
  6. datafusion-cli/
  7. datafusion-examples/
  8. dev/
  9. docs/
  10. python/
  11. test-utils/
  12. datafusion-testing
  13. parquet-testing
  14. testing
  15. .asf.yaml
  16. .dockerignore
  17. .editorconfig
  18. .gitattributes
  19. .gitignore
  20. .gitmodules
  21. Cargo.lock
  22. Cargo.toml
  23. CHANGELOG.md
  24. clippy.toml
  25. CODE_OF_CONDUCT.md
  26. CONTRIBUTING.md
  27. doap.rdf
  28. header
  29. LICENSE.txt
  30. licenserc.toml
  31. NOTICE.txt
  32. pre-commit.sh
  33. README.md
  34. rust-toolchain.toml
  35. rustfmt.toml
  36. taplo.toml
  37. typos.toml
README.md

Apache DataFusion

Crates.io Apache licensed Build Status Commit Activity Open Issues Discord chat Linkedin Crates.io MSRV

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:

“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

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
  • 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
  • parquet_encryption: support for using Parquet Modular Encryption
  • pyarrow: conversions between PyArrow and DataFusion types
  • 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.