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apache / arrow / 377811f47dfbcc57a4fd3b4b90aea96e2d6587fb
commit377811f47dfbcc57a4fd3b4b90aea96e2d6587fb[log] [tgz]
authorGang Wu <ustcwg@gmail.com>Fri Jun 30 14:55:40 2023 +0800
committerGitHub <noreply@github.com>Fri Jun 30 15:55:40 2023 +0900
tree09bed8946ac0880c55803925d6e2a75c5007c302
parent4e42b839e4d75c9b48718a4e18e1b4e5c619b5e0 [diff]
GH-36405: [C++][ORC] Upgrade ORC to 1.9.0 (#36406)

### Rationale for this change

Apache ORC has released 1.9.0 recently: https://orc.apache.org/news/2023/06/28/ORC-1.9.0/

The code base does not compile if we upgrade directly due to a new API below:
```cpp
    virtual std::unique_ptr<ColumnVectorBatch> createRowBatch(
        uint64_t size, MemoryPool& pool,
        bool encoded = false) const = 0;

    virtual std::unique_ptr<ColumnVectorBatch> createRowBatch(
        uint64_t size, MemoryPool& pool, bool encoded = false,
        bool useTightNumericVector = false) const = 0;

```

### What changes are included in this PR?

Explicitly specify which overload of `createRowBatch` to use in the orc test.

### Are these changes tested?

Yes, make sure all tests build and pass.

### Are there any user-facing changes?

NO.
* Closes: #36405

Authored-by: Gang Wu <ustcwg@gmail.com>
Signed-off-by: Sutou Kouhei <kou@clear-code.com>
3 files changed
tree: 09bed8946ac0880c55803925d6e2a75c5007c302
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  7. docs/
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  31. .travis.yml
  32. appveyor.yml
  33. CHANGELOG.md
  34. cmake-format.py
  35. CODE_OF_CONDUCT.md
  36. CONTRIBUTING.md
  37. docker-compose.yml
  38. header
  39. LICENSE.txt
  40. NOTICE.txt
  41. README.md
README.md

Apache Arrow

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 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: