[ 
https://issues.apache.org/jira/browse/FLINK-12294?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16826858#comment-16826858
 ] 

Gary Yao edited comment on FLINK-12294 at 4/26/19 10:26 AM:
------------------------------------------------------------

Link to user mailing list: 
[http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/kafka-partitions-data-locality-td27355.html]

Imo this is not a problem that is unique to the Kafka connector. Maybe you can 
update the title of this issue.


was (Author: gjy):
Link to user mailing list: 
http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/kafka-partitions-data-locality-td27355.html

> kafka consumer, data locality
> -----------------------------
>
>                 Key: FLINK-12294
>                 URL: https://issues.apache.org/jira/browse/FLINK-12294
>             Project: Flink
>          Issue Type: New Feature
>          Components: Connectors / Kafka, Runtime / Coordination
>            Reporter: Sergey
>            Priority: Major
>              Labels: performance
>
> Additional flag (with default false value) controlling whether topic 
> partitions already grouped by the key. Exclude unnecessary shuffle/resorting 
> operation when this parameter set to true. As an example, say we have 
> client's payment transaction in a kafka topic. We grouping by clientId 
> (transaction with the same clientId goes to one kafka topic partition) and 
> the task is to find max transaction per client in sliding windows. In terms 
> of map\reduce there is no needs to shuffle data between all topic consumers, 
> may be it`s worth to do within each consumer to gain some speedup due to 
> increasing number of executors within each partition data. With N messages 
> (in partition) instead of N*ln(N) (current realization with 
> shuffle/resorting) it will be just N operations. For windows with thousands 
> events - the tenfold gain of execution speed.



--
This message was sent by Atlassian JIRA
(v7.6.3#76005)

Reply via email to