|author||Heejong Lee <email@example.com>||Thu May 27 22:22:00 2021 -0700|
|committer||GitHub <firstname.lastname@example.org>||Thu May 27 22:22:00 2021 -0700|
Merge pull request #14901 from ihji/cherry-pick-expansion-fix [release-2.30.0][BEAM-12416] Populate use_sdf_read in ExpansionService.
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.
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.
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 SDKs for Java, Python and Go.
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:
DirectRunnerruns the pipeline on your local machine.
DataflowRunnersubmits the pipeline to the Google Cloud Dataflow.
FlinkRunnerruns the pipeline on an Apache Flink cluster. The code has been donated from dataArtisans/flink-dataflow and is now part of Beam.
SparkRunnerruns the pipeline on an Apache Spark cluster. The code has been donated from cloudera/spark-dataflow and is now part of Beam.
JetRunnerruns the pipeline on a Hazelcast Jet cluster. The code has been donated from hazelcast/hazelcast-jet and is now part of Beam.
Twister2Runnerruns 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 JIRA.
To get involved in Apache Beam:
Instructions for building and testing Beam itself are in the contribution guide.