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https://issues.apache.org/jira/browse/KAFKA-7658?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Matthias J. Sax resolved KAFKA-7658.
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Fix Version/s: 2.5.0
Resolution: Fixed
> Add KStream#toTable to the Streams DSL
> --------------------------------------
>
> Key: KAFKA-7658
> URL: https://issues.apache.org/jira/browse/KAFKA-7658
> Project: Kafka
> Issue Type: Improvement
> Components: streams
> Reporter: Guozhang Wang
> Assignee: highluck
> Priority: Major
> Labels: kip, newbie
> Fix For: 2.5.0
>
>
> KIP-523:
> [https://cwiki.apache.org/confluence/display/KAFKA/KIP-523%3A+Add+KStream%23toTable+to+the+Streams+DSL]
>
> We'd like to add a new API to the KStream object of the Streams DSL:
> {code:java}
> KTable KStream.toTable()
> KTable KStream.toTable(Materialized)
> {code}
> The function re-interpret the event stream {{KStream}} as a changelog stream
> {{KTable}}. Note that this should NOT be treated as a syntax-sugar as a dummy
> {{KStream.reduce()}} function which always take the new value, as it has the
> following difference:
> 1) an aggregation operator of {{KStream}} is for aggregating a event stream
> into an evolving table, which will drop null-values from the input event
> stream; whereas a {{toTable}} function will completely change the semantics
> of the input stream from event stream to changelog stream, and null-values
> will still be serialized, and if the resulted bytes are also null they will
> be interpreted as "deletes" to the materialized KTable (i.e. tombstones in
> the changelog stream).
> 2) the aggregation result {{KTable}} will always be materialized, whereas
> {{toTable}} resulted KTable may only be materialized if the overloaded
> function with Materialized is used (and if optimization is turned on it may
> still be only logically materialized if the queryable name is not set).
> Therefore, for users who want to take a event stream into a changelog stream
> (no matter why they cannot read from the source topic as a changelog stream
> {{KTable}} at the beginning), they should be using this new API instead of
> the dummy reduction function.
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