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KAFKA-6453: Document how timestamps are computed for aggregations and joins (#9009) 

Reviewer: Matthias J. Sax <matthias@confluent.io>
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Jim Galasyn 2020-07-13 15:01:14 -07:00 committed by GitHub
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docs/streams/core-concepts.html
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@ -128,7 +128,33 @@
</ul>
<p>
Note, that the describe default behavior can be changed in the Processor API by assigning timestamps to output records explicitly when calling <code>#forward()</code>.
You can change the default behavior in the Processor API by assigning
timestamps to output records explicitly when calling <code>#forward()</code>.
</p>
<p>
For aggregations and joins, timestamps are computed by using the
following rules.
</p>
<p>
<ul>
<li>For joins (stream-stream, table-table) that have left and right
input records, the timestamp of the output record is assigned
<code>max(left.ts, right.ts)</code>.</li>
<li>For stream-table joins, the output record is assigned the timestamp
from the stream record.</li>
<li>For aggregations, Kafka Streams also computes the <code>max</code>
timestamp over all records, per key, either globally (for non-windowed)
or per-window.</li>
<li>For stateless operations, the input record timestamp is passed through.
For <code>flatMap</code> and siblings that emit multiple records, all
output records inherit the timestamp from the corresponding input record.</li>
</ul>
</p>
<p>
</p>
<h3><a id="streams_concepts_duality" href="#streams-concepts-duality">Duality of Streams and Tables</a></h3>