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https://issues.apache.org/jira/browse/FLINK-5768?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15889775#comment-15889775
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ASF GitHub Bot commented on FLINK-5768:
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Github user fhueske commented on the issue:
https://github.com/apache/flink/pull/3423
Hi @shaoxuan-wang, sorry. I missed your comment. If you haven't started
reworking the batch aggregations yet, I agree to do it later and just change
the merging to smaller batches.
So far, we always put an emphasis on robustness and tried to avoid memory
issues as much as possible. In batch, most of the JVM memory is maintained by
Flink and not available for regular user-function objects. So I'd suggest to be
a bit conservative here and batch 16 rows. We can also run a few benchmarks to
check how much the parameter affects the performance.
> Apply new aggregation functions for datastream and dataset tables
> -----------------------------------------------------------------
>
> Key: FLINK-5768
> URL: https://issues.apache.org/jira/browse/FLINK-5768
> Project: Flink
> Issue Type: Sub-task
> Components: Table API & SQL
> Reporter: Shaoxuan Wang
> Assignee: Shaoxuan Wang
>
> Apply new aggregation functions for datastream and dataset tables
> This includes:
> 1. Change the implementation of the DataStream aggregation runtime code to
> use new aggregation functions and aggregate dataStream API.
> 2. DataStream will be always running in incremental mode, as explained in
> 06/Feb/2017 in FLINK5564.
> 2. Change the implementation of the Dataset aggregation runtime code to use
> new aggregation functions.
> 3. Clean up unused class and method.
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