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Apache Arrow is the universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics
https://arrow.apache.org/
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8eca523490### Rationale for this change Adresses part of issue #32723. Using the parquet ReadTable functions fails on list of dicts, if they contain more than one row group. In such case, the data is in a chunked array. A NotImplemented guards stops this from being read. ### What changes are included in this PR? This PR is a minimal fix that addresses this in the case when the chunks can be combined into a single array. It does not address the overflow issues, and the preexisting arrow::Concatenate will throw on that. The error is propagated. ### Are these changes tested? One test case is added for this specific situation. It fails without the fix in reader.cc. No regressions on `parquet-arrow-reader-writer-test` found, which I also tested. On macOS. ### Are there any user-facing changes? The users will be able to read a few more parquet files that were previously rejected. ### Was AI used for this PR? Yes. Claude (Opus and Sonnet 5.5) was used for compiling information from existing issues and propose a solution. I decided to do a minimal fix, not addressing the overflow problems, to make sure I understand the PR. I also asked it to verify style guide and contributor process on https://arrow.apache.org/docs/dev/developers/guide/step_by_step/ hoping I dont miss any steps. **PR code and description written by:** - [x] Human (The PR text) - [x] AI (The code) **Reviewed before submission by:** - [x] Human - [ ] AI - [ ] Not reviewed * GitHub Issue: #32723 Authored-by: Ludvig Hult <ludvig.hult@gmail.com> Signed-off-by: Zehua Zou <zehuazou2000@gmail.com> |
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Apache Arrow
Powering In-Memory Analytics
Apache Arrow is a universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics. It contains a set of technologies that enable data systems to efficiently store, process, and move data.
Major components of the project include:
- The Arrow Columnar Format: a standard and efficient in-memory representation of various datatypes, plain or nested
- The Arrow IPC Format: an efficient serialization of the Arrow format and associated metadata, for communication between processes and heterogeneous environments
- ADBC (Arrow Database Connectivity)
↗: Arrow-powered API, drivers, and libraries for access to databases and query engines - The Arrow Flight RPC protocol: based on the Arrow IPC format, a building block for remote services exchanging Arrow data with application-defined semantics (for example a storage server or a database)
- C++ libraries
- C bindings using GLib
- .NET libraries
↗ - Gandiva: an LLVM-based Arrow expression compiler, part of the C++ codebase
- Go libraries
↗ - Java libraries
↗ - JavaScript libraries
↗ - Julia implementation
↗ - Python libraries
- R libraries
- Ruby libraries
- Rust libraries
↗ - Swift libraries
↗
The ↗ icon denotes that this component of the project is maintained in a separate
repository.
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 main.
How to Contribute
Please read our latest project contribution guide.
If you are using AI coding tools, please review our AI-generated code guidance.
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:
- Join the mailing list: send an email to dev-subscribe@arrow.apache.org. Share your ideas and use cases for the project.
- Follow our activity on GitHub issues
- Learn the format
- Contribute code to one of the reference implementations
Continuous Integration Sponsors
We use runs-on for managing the project self-hosted runners. We use AWS for some of the required infrastructure for the project.