Google Git
Sign in
apache / arrow / c2f665b5c9c3670145fc131e3f827b4392cd4406
commitc2f665b5c9c3670145fc131e3f827b4392cd4406[log] [tgz]
authorHeres, Daniel <danielheres@gmail.com>Thu Apr 08 18:02:09 2021 -0400
committerAndrew Lamb <andrew@nerdnetworks.org>Thu Apr 08 18:02:09 2021 -0400
treeda7a0f0e5e809c13a1ab578ba60b66c595e06df2
parentac38410ead57c0d3e5fac27bf3d8161774839c97 [diff]
ARROW-12279: [Rust][DataFusion] Add test for null handling in hash join (ARROW-12266)

This PR adds a (ignored) test for https://issues.apache.org/jira/browse/ARROW-12266

```
SELECT id1, id2 FROM (SELECT null AS id1) t1
LEFT JOIN (SELECT 0 AS id2) t2 ON id1 = id2
```

current result:

```NULL, NULL```

(should be empty result set)

We should filter on nulls beforehand to make this result correct. Probably the best way to go here I think is to add a filter in the logical plan on non-null for inner / left and right joins.
This can make things more efficient as the non-null filter can be pushed down which can lead to efficiency gains (making data-set smaller, not having to deal with nullable data in batches, or even entire files could be skipped when they only contain nulls).

Closes #9937 from Dandandan/join_null

Authored-by: Heres, Daniel <danielheres@gmail.com>
Signed-off-by: Andrew Lamb <andrew@nerdnetworks.org>
1 file changed
tree: da7a0f0e5e809c13a1ab578ba60b66c595e06df2
  1. .github/
  2. c_glib/
  3. ci/
  4. cpp/
  5. csharp/
  6. dev/
  7. docs/
  8. format/
  9. go/
  10. java/
  11. js/
  12. julia/
  13. matlab/
  14. python/
  15. r/
  16. ruby/
  17. rust/
  18. testing
  19. .asf.yaml
  20. .clang-format
  21. .clang-tidy
  22. .clang-tidy-ignore
  23. .dir-locals.el
  24. .dockerignore
  25. .env
  26. .gitattributes
  27. .gitignore
  28. .gitmodules
  29. .hadolint.yaml
  30. .pre-commit-config.yaml
  31. .readthedocs.yml
  32. .travis.yml
  33. appveyor.yml
  34. CHANGELOG.md
  35. cmake-format.py
  36. CODE_OF_CONDUCT.md
  37. CONTRIBUTING.md
  38. docker-compose.yml
  39. header
  40. LICENSE.txt
  41. NOTICE.txt
  42. README.md
  43. run-cmake-format.py
README.md

Apache Arrow

Build Status Coverage Status Fuzzing Status License Twitter Follow

Powering In-Memory Analytics

Apache Arrow is a development platform for in-memory analytics. It contains a set of technologies that enable big data systems to process and move data fast.

Major components of the project include:

Arrow is an Apache Software Foundation project. Learn more at arrow.apache.org.

What's in the Arrow libraries?

The reference Arrow libraries contain many distinct software components:

  • Columnar vector and table-like containers (similar to data frames) supporting flat or nested types
  • Fast, language agnostic metadata messaging layer (using Google's Flatbuffers library)
  • Reference-counted off-heap buffer memory management, for zero-copy memory sharing and handling memory-mapped files
  • IO interfaces to local and remote filesystems
  • Self-describing binary wire formats (streaming and batch/file-like) for remote procedure calls (RPC) and interprocess communication (IPC)
  • Integration tests for verifying binary compatibility between the implementations (e.g. sending data from Java to C++)
  • Conversions to and from other in-memory data structures
  • Readers and writers for various widely-used file formats (such as Parquet, CSV)

Implementation status

The official Arrow libraries in this repository are in different stages of implementing the Arrow format and related features. See our current feature matrix on git master.

How to Contribute

Please read our latest project contribution guide.

Getting involved

Even if you do not plan to contribute to Apache Arrow itself or Arrow integrations in other projects, we'd be happy to have you involved: