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### Rationale for this change
As #36420 says, we want add an sql-compatible `is_in` variant, which has a different logic handling Null. After a dicussion with @ ianmcook and @ bkietz, we decide to support an enum option `null_matching_behavior` for SetLookup, which actually adds two semantics of null handling for `is_in` and doesn't add an new behavior for `index_in`.

The enum option `null_matching_behavior` will replace `skip_nulls` in the future.

### What changes are included in this PR?
Add an enum parameter `null_matching_behavior` for SetLookupOptions.

### Are these changes tested?
Two kinds of tests are implemented
- Replace default parameter with `null_matching_behavior` instead of `skip_nulls` for `is_in` and `index_in` tests 
- Add tests for `NullMatchingBehavior::EMIT_NULL` and `NullMatchingBehavior::INCONCLUSIVE` for `is_in`

Besides, since the `skip_nulls` is not deprecated now, I still preserve the old tests with `skip_nulls`. When the `skip_nulls` is totally deprecated, we can replace the test parameter `skip_nulls=false` with `null_matching_behavior=MATCH` and `skip_nulls=true` with `null_matching_behavior=SKIP` for these old tests.

### Are there any user-facing changes?
No. Currently we support backward compatibility. In the future, we plan to replace `skip_nulls` with `null_matching_behavior` completely.

* Closes: #36420

Lead-authored-by: Junming Chen <>
Co-authored-by: Sutou Kouhei <>
Co-authored-by: Benjamin Kietzman <>
Signed-off-by: Benjamin Kietzman <>


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Apache Arrow

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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

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 main.

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: