|tagger||Mark Liu <firstname.lastname@example.org>||Mon Oct 07 15:07:24 2019 -0700|
Apache Beam 2.16.0 release -----BEGIN PGP SIGNATURE----- iQIzBAABCgAdFiEEwRCxyCB0iDpCQdl3WZ1jBf86uzIFAl2btyYACgkQWZ1jBf86 uzLnKA//Whrbs60uVR8j3JsIRbgjZ2dz72OFo4qog/VajLXD2FCgXF03qjZoqXvO Jhvn16aMyMu4Qj4+TJndHp7/jkt9fMZ85FewUO6VFaw3LttCvSfhnTAfsiqtwM4G z+5ESC/BUMxDYb54rS6IysSJ4huJPYbBzXXPCM7q1zBbt/bG3e54KRqJdqoLLT5+ xCy8niFFRk0WgLi2/ebQ472NwzTfLIVatze5n3keg82vbYEIGNZgOkXEtGakuD9y UjjFxOelCDul0Fyr7XcqOYR+CiaR4L2HelDbiD242WaRPyNxmj+AAgnLD/EYO55J cyTb48UYGcvoMq5/LVNodX95M5+u10/UCnz1QTtim9rv0jihz5mNrv/UwVhjIpWa zJvggzJ0/GZSrptoiSPVyRV8hMe2ULlObrbQF6JjmZ3fTYJL3UAfDJAGK94p0Si9 6GS/k+Si5IeWgYz8tE7lEpQ8Sh8Nir/QJT9vtyy+LrD3rxCH3URS2ctoBjRbgdyz fldSu6ug/wELKEJkICmlWooNDljlh9s4fbHzBgmGpCbxpb/ZSkXbNemmhauFIoOf WS1x3nlBhXO3V62AktTXcLADa5JrRth3SsQzTDfja6nRKdErmwBVTJ782QI/yZze YDrNNjuthLadO6OLjgBy1aMVAPMJz82Eaynq2SOswYbNN3n7NKA= =VdAO -----END PGP SIGNATURE-----
|author||tvalentyn <email@example.com>||Mon Sep 30 14:27:37 2019 -0700|
|committer||GitHub <firstname.lastname@example.org>||Mon Sep 30 14:27:37 2019 -0700|
Merge pull request #9696 [BEAM-8324] Restrict the upper bound for dill due to incompatibility between versions 0.3.0 and 0.3.1.1.
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.