commit | e0136ffc176d157d0928e7d501bca4daca3160a8 | [log] [tgz] |
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author | dpcollins-google <40498610+dpcollins-google@users.noreply.github.com> | Mon Apr 19 11:11:32 2021 -0400 |
committer | GitHub <noreply@github.com> | Mon Apr 19 08:11:32 2021 -0700 |
tree | f991e98767581a82970bcb8242473c7ddd121f8d | |
parent | ffcc5e03e0f745ce1575282ce8b809b177e4048b [diff] |
Change kafka table provider properties structure. (#14507) * Change kafka table provider properties structure. This is an intentionally breaking change in the kafka beam SQL table. Currently, using the kafka table provider is impossible with a single reference identifier (LOCATION), and the location field goes entirely unused. This change repurposes the currently unused LOCATION field to be `<single bootstrap broker>/<topic name>`. This is a breaking change because previously, users could have had LOCATION set to any string they wanted, including those without the <broker>/<topic name> structure, since this field was entirely unused. This change also changes "bootstrap.servers" which is a comma-separated string in the properties to "bootstrap_servers" which uses a proper array, and makes both the "topics" and "bootstrap_servers" parameters optional (previously, they were actually required, although the documentation said otherwise). Also update beam documentation to reflect new kafka, pubsub and pubsublite semantics added in this and previous PRs. * Change kafka table provider properties structure. This is an intentionally breaking change in the kafka beam SQL table. Currently, using the kafka table provider is impossible with a single reference identifier (LOCATION), and the location field goes entirely unused. This change repurposes the currently unused LOCATION field to be `<single bootstrap broker>/<topic name>`. This is a breaking change because previously, users could have had LOCATION set to any string they wanted, including those without the <broker>/<topic name> structure, since this field was entirely unused. This change also changes "bootstrap.servers" which is a comma-separated string in the properties to "bootstrap_servers" which uses a proper array, and makes both the "topics" and "bootstrap_servers" parameters optional (previously, they were actually required, although the documentation said otherwise). Also update beam documentation to reflect new kafka, pubsub and pubsublite semantics added in this and previous PRs. * fix: make LOCATION optional. * fix: make LOCATION optional. * Fix IT * fix whitespace * modify CHANGES.md
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.
Lang | SDK | Dataflow | Flink | Samza | Spark | Twister2 |
---|---|---|---|---|---|---|
Go | --- | --- | --- | |||
Java | ||||||
Python | --- | --- | ||||
XLang | --- |
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 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:
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.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 JIRA.
To learn how to write Beam pipelines, read the Quickstart for [Java, Python, or Go] available on our website.
To get involved in Apache Beam:
Instructions for building and testing Beam itself are in the contribution guide.