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https://issues.apache.org/jira/browse/KAFKA-3104?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15102551#comment-15102551
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ASF GitHub Bot commented on KAFKA-3104:
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GitHub user guozhangwang opened a pull request:
https://github.com/apache/kafka/pull/781
[WIP] KAFKA-3104: add windowed aggregation to KStream
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/guozhangwang/kafka K3104
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/kafka/pull/781.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #781
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commit bf4c4cb3dbb5b4066d9c3e0ada5b7ffd98eb129a
Author: Guozhang Wang <wangg...@gmail.com>
Date: 2016-01-14T20:27:58Z
add internal source topic for tracking
commit 1485dff08a76c6ff685b0fe72226ce3b629b1d3c
Author: Guozhang Wang <wangg...@gmail.com>
Date: 2016-01-14T22:32:08Z
minor fix for this.interSourceTopics
commit 60cafd0885c41f93e408f8d89880187ddec789a1
Author: Guozhang Wang <wangg...@gmail.com>
Date: 2016-01-15T01:09:00Z
add KStream windowed aggregation
commit 983a626008d987828deabe45d75e26e909032843
Author: Guozhang Wang <wangg...@gmail.com>
Date: 2016-01-15T01:34:56Z
merge from apache trunk
commit 57051720de4238feb4dc3c505053096042a87d9c
Author: Guozhang Wang <wangg...@gmail.com>
Date: 2016-01-15T21:38:53Z
v1
commit 4a49205fcab3a05ed1fd05a34c7a9a92794b992d
Author: Guozhang Wang <wangg...@gmail.com>
Date: 2016-01-15T22:07:17Z
minor fix on HoppingWindows
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> Windowed Stream Aggregation Implementation
> ------------------------------------------
>
> Key: KAFKA-3104
> URL: https://issues.apache.org/jira/browse/KAFKA-3104
> Project: Kafka
> Issue Type: Sub-task
> Reporter: Guozhang Wang
> Assignee: Guozhang Wang
>
> Proposed approach:
> 1. Add a K-V store with format <key, window> : <agg-value> with Windowed<K>
> ser-de and <T> ser-de.
> 2. Upon receiving a record forwarded from the upstream processor:
> 2.1. First get the mapping window(s) from the context timestamp.
> 2.2. For each window, first try fetch from the store, if not exist call
> initialValue().
> 2.2. Only call add(..), and never call remove(..)
> 2.3. Forward the aggregate value with key Windowed<agg-key> and value
> Change<agg-value>.
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