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apache/beam/72ce2074cafef9b0af08ce5d28d12fbe0e8b99fe
commit
72ce2074cafef9b0af08ce5d28d12fbe0e8b99fe
[log] [tgz]
author
Shunping Huang <shunping@google.com>
Thu Feb 12 22:45:18 2026 -0500
committer
GitHub <noreply@github.com>
Thu Feb 12 22:45:18 2026 -0500
tree
296ddea1862ddff518183e3e6b8ee439065b2354
parent
9ea7380de850fafceff09cd007d3979987b5ab46 [diff]
GCS client library migration in Java SDK - part 2 (#37502)

* Migrate fileSize() and getObject()

* Move getNonWildcardPrefix() and isWildcard() from GcsUtilV1 to GcsPath

* Migrate listObjects()

* Migrate getBucket(), bucketAccessible(), verifyBucketAccessible().

* Add tests and extract setUp function.

* Migrate bucketOwner()

* Migrate createBucket() and removeBucket()

* Refactor exception handling.

* Change returned value of listBlobs. Migrate expand().

* Add deprecated annotation to some V1 apis. Refactor expand().

* Add exception handling for listBlobs()

* Add some more tests and comments.

* Fetch only the required field to minimize data transfer.

* Migrate getObjects() and refactor some method arguments.

* Refactor translateStorageException

* Control size of the batch for getBlobs.

* Formatting

* Fix GcpCoreApiSurfaceTest

* Add java deps.

* Change getBucketV2 to getBucketWithOptions. Fix GcpCoreApiSurfaceTest.

* Put the new IT tests into a different file.

* Revise according to gemini review.

* Change argument from List to Iterable in getBlobs()

* Remove redundant comments.

* Minor change.

* Revise according to review.
9 files changed
tree: 296ddea1862ddff518183e3e6b8ee439065b2354
  1. .agent/
  2. .gemini/
  3. .github/
  4. .test-infra/
  5. buildSrc/
  6. contributor-docs/
  7. dev-support/
  8. examples/
  9. gradle/
  10. infra/
  11. it/
  12. learning/
  13. model/
  14. playground/
  15. plugins/
  16. release/
  17. runners/
  18. scripts/
  19. sdks/
  20. vendor/
  21. website/
  22. .asf.yaml
  23. .editorconfig
  24. .gitattributes
  25. .gitignore
  26. .gitmodules
  27. .mailmap
  28. .pre-commit-config.yaml
  29. .yamllint.yml
  30. assembly.xml
  31. build.gradle.kts
  32. CHANGES.md
  33. CI.md
  34. CONTRIBUTING.md
  35. gradle.properties
  36. gradlew
  37. gradlew.bat
  38. LICENCE.cloudpickle
  39. LICENSE
  40. LICENSE.python
  41. local-env-setup.sh
  42. NOTICE
  43. README.md
  44. settings.gradle.kts
  45. start-build-env.sh
README.md

Apache Beam

Apache Beam is a unified model for defining both batch and streaming data-parallel processing pipelines, as well as a set of language-specific SDKs for constructing pipelines and Runners for executing them on distributed processing backends, including Apache Flink, Apache Spark, Google Cloud Dataflow, and Hazelcast Jet.

Status

Maven Version PyPI version Go version Python coverage Build python source distribution and wheels Python tests Java tests Go tests

Overview

Beam provides a general approach to expressing embarrassingly parallel data processing pipelines and supports three categories of users, each of which have relatively disparate backgrounds and needs.

  1. End Users: Writing pipelines with an existing SDK, running it on an existing runner. These users want to focus on writing their application logic and have everything else just work.
  2. SDK Writers: Developing a Beam SDK targeted at a specific user community (Java, Python, Scala, Go, R, graphical, etc). These users are language geeks and would prefer to be shielded from all the details of various runners and their implementations.
  3. Runner Writers: Have an execution environment for distributed processing and would like to support programs written against the Beam Model. Would prefer to be shielded from details of multiple SDKs.

The Beam Model

The model behind Beam evolved from several internal Google data processing projects, including MapReduce, FlumeJava, and Millwheel. This model was originally known as the “Dataflow Model”.

To learn more about the Beam Model (though still under the original name of Dataflow), see the World Beyond Batch: Streaming 101 and Streaming 102 posts on O’Reilly’s Radar site, and the VLDB 2015 paper.

The key concepts in the Beam programming model are:

  • PCollection: represents a collection of data, which could be bounded or unbounded in size.
  • PTransform: represents a computation that transforms input PCollections into output PCollections.
  • Pipeline: manages a directed acyclic graph of PTransforms and PCollections that is ready for execution.
  • PipelineRunner: specifies where and how the pipeline should execute.

SDKs

Beam supports multiple language-specific SDKs for writing pipelines against the Beam Model.

Currently, this repository contains SDKs for Java, Python and Go.

Have ideas for new SDKs or DSLs? See the sdk-ideas label.

Specific SDK Readmes

Runners

Beam supports executing programs on multiple distributed processing backends through PipelineRunners. Currently, the following PipelineRunners are available:

  • The DirectRunner runs the pipeline on your local machine.
  • The PrismRunner runs the pipeline on your local machine using Beam Portability.
  • The DataflowRunner submits the pipeline to the Google Cloud Dataflow.
  • The FlinkRunner runs the pipeline on an Apache Flink cluster. The code has been donated from dataArtisans/flink-dataflow and is now part of Beam.
  • The SparkRunner runs the pipeline on an Apache Spark cluster.
  • The JetRunner runs the pipeline on a Hazelcast Jet cluster. The code has been donated from hazelcast/hazelcast-jet and is now part of Beam.
  • The Twister2Runner runs the pipeline on a Twister2 cluster. The code has been donated from DSC-SPIDAL/twister2 and is now part of Beam.

Have ideas for new Runners? See the runner-ideas label.

Instructions for building and testing Beam itself are in the contribution guide.

📚 Learn More

Here are some resources actively maintained by the Beam community to help you get started:

Contact Us

To get involved with Apache Beam: