[jira] [Work started] (KAFKA-3429) Remove Serdes needed for repartitioning in KTable stateful operations

["Matthias J. Sax (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/KAFKA-3429?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Work on KAFKA-3429 started by Matthias J. Sax.
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> Remove Serdes needed for repartitioning in KTable stateful operations
> ---------------------------------------------------------------------
>
>                 Key: KAFKA-3429
>                 URL: https://issues.apache.org/jira/browse/KAFKA-3429
>             Project: Kafka
>          Issue Type: Sub-task
>          Components: streams
>            Reporter: Guozhang Wang
>            Assignee: Matthias J. Sax
>              Labels: api, newbie++
>             Fix For: 0.10.1.0
>
>
> Currently in KTable aggregate operations where a repartition is possibly needed since the aggregation key may not be the same as the original primary key, we require the users to provide serdes (default to configured ones) for read / write to the internally created re-partition topic. However, these are not necessary since for all KTable instances either generated from the topics directly:
> {code}table = builder.table(...){code}
> or from aggregation operations:
> {code}table = stream.aggregate(...){code}
> There are already serde provided for materializing the data, and hence the same serde can be re-used when the resulted KTable is involved in future aggregation operations. For example:
> {code}
> table1 = stream.aggregateByKey(serde);
> table2 = table1.aggregate(aggregator, selector, originalSerde, aggregateSerde);
> {code}
> We would not need to require users to specify the "originalSerde" in table1.aggregate since it could always reuse the "serde" from stream.aggregateByKey, which is used to materialize the table1 object.
> In order to get ride of it, implementation-wise we need to carry the serde information along with the KTableImpl instance in order to re-use it in a future operation that requires repartitioning.



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