|tagger||Anton Kedin <email@example.com>||Thu Aug 01 16:02:40 2019 -0700|
Apache Beam 2.14.0 Release -----BEGIN PGP SIGNATURE----- iQIzBAABCgAdFiEEieL/yufpnPbmgnz+9zSfIxD/sZMFAl1Db5wACgkQ9zSfIxD/ sZOgzQ/+InIYSVovszf6djM92YkaWYxC5TWlLhC7GE8uviXWfKCC4SH7fy4uhFgu iWknfjMdQRst+QjgisX3rIKswTLFCjLZBKUXj3er4LQd1cZMiQBwgBLmJwwWO8BQ xbizu1Vv6YxD2LV66hNLjS2FQex9yMdsYkQAvFmFl0hgBGnx5CQhoIlx8E5hM6CD wzJTg2dqakhQfeuf00Iu0Oa7SUKhcybecxgXakR/Fr5gCtJHPAwwUk3iYswByhOV uHM9O5yBcAmoyd6y6RSsLdR4Q4jPuInSnOvWxcj8pQYHwz7HPEQlzWrca/LR2l1x AS5n0cqvXfEqN7lMzDWtN57T1C7jasKpsjD5Dp5cqHqlavRXheY0tL8wXdF4DTNm QyfcnaRcuFFjD+5ZnGKtRtKgF83v0bjN5MEpeoFXeAKRpr+2ZCiRfK5uL7Cx/6Mu o/c4xDY/+2G0V+bCi7UgGV4HewNSJbQhCdH8r4e6NiKkc5HIg4/xBa2O190c6itd lYmGZEzkGTW+p1CzHXuItMTUcg2PLEifUkPLjDfauoiYh47M5w1g8dA/2pvEEIQ1 /lcTu7rQg/8v6R558IXHXMGwkJ3Cvwfw+n3qotpTqFTby77cs3bO60lCqGEImj7H SJypsWedIQJ6QDakzokr9r+UJQfRt33wEdhKjLj/9F05HKEwV/Q= =A27b -----END PGP SIGNATURE-----
|author||Anton Kedin <firstname.lastname@example.org>||Wed Jul 24 13:59:09 2019 -0700|
|committer||GitHub <email@example.com>||Wed Jul 24 13:59:09 2019 -0700|
Merge pull request #9148 from akedin/revert-move-to-215-on-214-branch Revert moving to 2.15.0-SNAPSHOT on 2.14.0 branch
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 Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow.
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.
ApexRunnerruns the pipeline on an Apache Hadoop YARN cluster (or in embedded mode).
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.
Have ideas for new Runners? See the JIRA.
If you'd like to build and install the whole project from the source distribution, you may need some additional tools installed in your system. In a Debian-based distribution:
sudo apt-get install \ openjdk-8-jdk \ python-setuptools \ python-pip \ virtualenv
Then please use the standard
./gradlew build command.
To get involved in Apache Beam:
We also have a contributor's guide.