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apache / beam / v0.4.0-incubating-RC1
tag4077cbd494bf90380a7f4119399b29fe6e21f2a5
taggerKenneth Knowles <kenn@apache.org>Tue Oct 24 20:23:05 2017 -0700
objectaf3989b086ed2e1bf0a08358ce6b7620c334cf49 
v0.4.0-incubating-RC1
commitaf3989b086ed2e1bf0a08358ce6b7620c334cf49[log] [tgz]
authorDavor Bonaci <davor@google.com>Wed Dec 14 18:58:57 2016 -0800
committerDavor Bonaci <davor@google.com>Wed Dec 14 18:58:57 2016 -0800
treebac148b1e31ae349429f55818df777377db79fdb
parentea0d7f5c26fd97e8528d0614cdce8215ea05557c [diff]
[maven-release-plugin] prepare release v0.4.0-incubating-RC1
33 files changed
tree: bac148b1e31ae349429f55818df777377db79fdb
  1. .github/
  2. .jenkins/
  3. .travis/
  4. examples/
  5. runners/
  6. sdks/
  7. .gitattributes
  8. .gitignore
  9. .travis.yml
  10. DISCLAIMER
  11. LICENSE
  12. NOTICE
  13. pom.xml
  14. README.md
README.md

Apache Beam (incubating)

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 like Apache Spark, Apache Flink, and Google Cloud Dataflow.

Apache Beam is an effort undergoing incubation at the Apache Software
Foundation (ASF), sponsored by the Apache Incubator PMC.

Incubation is required of all newly accepted projects until a further review
indicates that the infrastructure, communications, and decision making process
have stabilized in a manner consistent with other successful ASF projects.

While incubation status is not necessarily a reflection of the completeness
or stability of the code, it does indicate that the project has yet to be
fully endorsed by the ASF.

Status

The Apache Beam project is in the process of bootstrapping. This includes the creation of project resources, the refactoring of the initial code submissions, and the formulation of project documentation, planning, and design documents. Please expect a significant amount of churn and breaking changes in the near future.

Build Status Build Status Coverage Status

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 a number of 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 the Beam Java SDK, which is in the process of evolving from the Dataflow Java SDK. The Dataflow Python SDK will also become part of Beam in the near future.

Have ideas for new SDKs or DSLs? See the JIRA.

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 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 code has been donated from cloudera/spark-dataflow and is now part of Beam.

Have ideas for new Runners? See the JIRA.

Getting Started

Please refer to the Quickstart available on our website.

If you'd like to build and install the whole project from the source distribution, please use the standard mvn clean install command.

Flink Runner

See the Flink Runner README.

Spark Runner

See the Spark Runner README.

Contact Us

To get involved in Apache Beam:

More Information