commit | ea0d7f5c26fd97e8528d0614cdce8215ea05557c | [log] [tgz] |
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author | Kenneth Knowles <klk@google.com> | Wed Dec 14 14:33:04 2016 -0800 |
committer | Kenneth Knowles <klk@google.com> | Wed Dec 14 14:33:04 2016 -0800 |
tree | 2ecfa195e2166fd85f0a8c30c748e3751439c265 | |
parent | 10bb4767a1f989a1a75778828c07d9c72c450495 [diff] | |
parent | d9f24b86c644ea85fd197eaab4c2d16b20a70d5f [diff] |
This closes #1619
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.
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.
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.
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.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.
Beam supports executing programs on multiple distributed processing backends through PipelineRunners. Currently, the following PipelineRunners are available:
DirectRunner
runs the pipeline on your local machine.DataflowRunner
submits the pipeline to the Google Cloud Dataflow.FlinkRunner
runs the pipeline on an Apache Flink cluster. The code has been donated from dataArtisans/flink-dataflow and is now part of Beam.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.
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.
See the Flink Runner README.
See the Spark Runner README.
To get involved in Apache Beam: