[
https://issues.apache.org/jira/browse/KAFKA-3511?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Eno Thereska resolved KAFKA-3511.
---------------------------------
Resolution: Won't Fix
> Add common aggregation functions like Sum and Avg as build-ins in Kafka
> Streams DSL
> -----------------------------------------------------------------------------------
>
> Key: KAFKA-3511
> URL: https://issues.apache.org/jira/browse/KAFKA-3511
> Project: Kafka
> Issue Type: Bug
> Components: streams
> Reporter: Guozhang Wang
> Assignee: Eno Thereska
> Labels: api
> Fix For: 0.10.1.0
>
>
> Currently we have the following aggregation APIs in the Streams DSL:
> {code}
> KStream.aggregateByKey(..)
> KStream.reduceByKey(..)
> KStream.countByKey(..)
> KTable.groupBy(...).aggregate(..)
> KTable.groupBy(...).reduce(..)
> KTable.groupBy(...).count(..)
> {code}
> And it is better to add common aggregation functions like Sum and Avg as
> built-in into the Streams DSL. A few questions to ask though:
> 1. Should we add those built-in functions as, for example
> {{KTable.groupBy(...).sum(...)} or {{KTable.groupBy(...).aggregate(SUM,
> ...)}}. Please see the comments below for detailed pros and cons.
> 2. If we go with the second option above, should we replace the countByKey /
> count operators with aggregate(COUNT) as well? Personally I (Guozhang) feel
> it is not necessary, as COUNT is a special aggregate function since we do not
> need to map on any value fields; this is the same approach as in Spark as
> well, where Count is built-in as first-citizen in the DSL, and others are
> built-in as {{aggregate(SUM)}}, etc.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
