Migrate to Google Cloud Dataflow Client (#37639) * [WIP] Migrate to Google Cloud Dataflow Client rebase * Trigger relevant postcommits * base image update * fix camel case * update dataflow runner + tests * slide import to avoid triggering unit tests * yapf stuff * remove extra print * further spec structs, fix incorrect piplineUrl option, remove old client code * suppress line-too-longs * formatting * linting, tweak metrics tests * Proto-specific changes to metric processing tests * try to dump logging * handle more straightforward metrics values * add skips since the unit tests now depend on the proto library * testing if there's a disconnect between proto behavior locally and in tests * correct scalar access * clean up dist accesses * linting, various fixes * fix unit test setup for direct accesses * linting * more linting * re-enable histograms * Bump dataflow client version, restore pausing/paused concept * formatting * fix enum selection * handle the proto hash PR * remove unnecessary try/except block * re-delete old messsages * fix disk_provisioned_iops/throughput_mibps tests * code bot suggestions * revert pipeline options interaction * leftover update * yapf * swap credential loading * Fix message importance parsing * fix apiclient_test case w/ cloud deps and no auth * Local runner SSL fix * last local change * apitools test fix * yapf * Apply suggestions from code review Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> * gemini suggestions * fix message importance * fix local runner case * remove legacy translation maps * revert container requirements changes * missing import * fix bad trigger file merge * Update sdks/python/apache_beam/runners/dataflow/dataflow_metrics.py Co-authored-by: Danny McCormick <dannymccormick@google.com> * fix merge problem with runnerv2 disabled tests * fix state breakage --------- Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: Danny McCormick <dannymccormick@google.com>
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.
If you're new to Apache Beam, start here:
Choose a language:
Run your first example:
Understand core concepts:
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 several 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 SDKs for Java, Python and Go.
Have ideas for new SDKs or DSLs? See the sdk-ideas label.
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.PrismRunner runs the pipeline on your local machine using Beam Portability.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.JetRunner runs the pipeline on a Hazelcast Jet cluster. The code has been donated from hazelcast/hazelcast-jet and is now part of Beam.Twister2Runner runs 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 runner-ideas label.
Instructions for building and testing Beam itself are in the contribution guide.
Here are some resources actively maintained by the Beam community to help you get started:
To get involved with Apache Beam: