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apache / arrow / a1c3d57af514d4a84e753ff51df8e563135ee55e
commita1c3d57af514d4a84e753ff51df8e563135ee55e[log] [tgz]
authorPavel Solodovnikov <psolodovnikov@querifylabs.com>Thu Aug 18 22:16:25 2022 +0300
committerGitHub <noreply@github.com>Thu Aug 18 21:16:25 2022 +0200
tree9d2ae6af8312614b428346220f886a7ecac263be
parentbc52f9f0e582474501e92e6a281f0110754a8af1 [diff]
ARROW-17306: [C++] Provide an optimized `GetFileInfoGenerator` specialization for `LocalFileSystem` (#13796)

Introduce a specialization of `GetFileInfoGenerator` in the `LocalFileSystem` class.

This implementation tries to improves performance by hiding latencies at two levels:
1. Child directories can be readahead so that listing directories entries from disk can be achieved in parallel with other work;
2. Directory entries can be `stat`'ed and yielded in chunks so that the `FileInfoGenerator` consumer can start receiving entries before a large directory is fully processed.

Both mechanisms can be tuned using dedicated parameters in `LocalFileSystemOptions`.

Signed-off-by: Pavel Solodovnikov <pavel.al.solodovnikov@gmail.com>
Co-Authored-by: Igor Seliverstov <iseliverstov@querifylabs.com>

Lead-authored-by: Pavel Solodovnikov <pavel.al.solodovnikov@gmail.com>
Co-authored-by: Antoine Pitrou <antoine@python.org>
Signed-off-by: Antoine Pitrou <antoine@python.org>
6 files changed
tree: 9d2ae6af8312614b428346220f886a7ecac263be
  1. .github/
  2. c_glib/
  3. ci/
  4. cpp/
  5. csharp/
  6. dev/
  7. docs/
  8. format/
  9. go/
  10. java/
  11. js/
  12. matlab/
  13. python/
  14. r/
  15. ruby/
  16. testing
  17. .asf.yaml
  18. .clang-format
  19. .clang-tidy
  20. .clang-tidy-ignore
  21. .dir-locals.el
  22. .dockerignore
  23. .env
  24. .gitattributes
  25. .gitignore
  26. .gitmodules
  27. .hadolint.yaml
  28. .pre-commit-config.yaml
  29. .readthedocs.yml
  30. .travis.yml
  31. appveyor.yml
  32. CHANGELOG.md
  33. cmake-format.py
  34. CODE_OF_CONDUCT.md
  35. CONTRIBUTING.md
  36. docker-compose.yml
  37. header
  38. LICENSE.txt
  39. NOTICE.txt
  40. 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 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: