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Posted to issues@spark.apache.org by "Burak Yavuz (Jira)" <ji...@apache.org> on 2020/01/31 06:13:00 UTC
[jira] [Resolved] (SPARK-30669) Introduce AdmissionControl API to
Structured Streaming
[ https://issues.apache.org/jira/browse/SPARK-30669?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Burak Yavuz resolved SPARK-30669.
---------------------------------
Fix Version/s: 3.0.0
Assignee: Burak Yavuz
Resolution: Done
Resolved by [https://github.com/apache/spark/pull/27380]
> Introduce AdmissionControl API to Structured Streaming
> ------------------------------------------------------
>
> Key: SPARK-30669
> URL: https://issues.apache.org/jira/browse/SPARK-30669
> Project: Spark
> Issue Type: Improvement
> Components: Structured Streaming
> Affects Versions: 3.0.0
> Reporter: Burak Yavuz
> Assignee: Burak Yavuz
> Priority: Major
> Fix For: 3.0.0
>
>
> In Structured Streaming, we have the concept of Triggers. With a trigger like Trigger.Once(), the semantics are to process all the data available to the datasource in a single micro-batch. However, this semantic can be broken when data source options such as `maxOffsetsPerTrigger` (in the Kafka source) rate limit the amount of data read for that micro-batch.
> We propose to add a new interface `SupportsAdmissionControl` and `ReadLimit`. A ReadLimit defines how much data should be read in the next micro-batch. `SupportsAdmissionControl` specifies that a source can rate limit its ingest into the system. The source can tell the system what the user specified as a read limit, and the system can enforce this limit within each micro-batch or impose it's own limit if the Trigger is Trigger.Once() for example.
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