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https://issues.apache.org/jira/browse/FLINK-5584?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15831036#comment-15831036
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ASF GitHub Bot commented on FLINK-5584:
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GitHub user hongyuhong opened a pull request:
https://github.com/apache/flink/pull/3175
[FLINK-5584]support sliding-count row-window on streaming sql
Calcite has already support sliding-count row-window, the grammar look like:
select sum(amount) over (rows 10 preceding) from Order;
select sum(amount) over (partition by user rows 10 preceding) from Order;
And it will parse the sql as a LogicalWindow relnode, the logical Window
contains aggregate func info and window info, it's similar to Flink
LogicalWIndowAggregate, so we can add an convert rule to directly convert
LogicalWindow into DataStreamAggregate relnode.
1. Add HepPlanner to do the window optimize, cause valcano planner can not
choose the ProjectToWindow optimize as the best.
2. Add DataStreamWindowRule.scala to convert LogicalWindow to
DataStreamAggregate.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/hongyuhong/flink master
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/flink/pull/3175.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 #3175
----
commit 2417e24ad676474df3c9fce6701024ca88c6a439
Author: hongyuhong 00223286 <hongyuh...@huawei.com>
Date: 2017-01-20T02:02:12Z
support sliding-count row-window on streaming sql
----
> Support Sliding-count row-window on streaming sql
> -------------------------------------------------
>
> Key: FLINK-5584
> URL: https://issues.apache.org/jira/browse/FLINK-5584
> Project: Flink
> Issue Type: New Feature
> Components: Table API & SQL
> Reporter: Yuhong Hong
>
> Calcite has already support sliding-count row-window, the grammar look like:
> select sum(amount) over (rows 10 preceding) from Order;
> select sum(amount) over (partition by user rows 10 preceding) from Order;
> And it will parse the sql as a LogicalWindow relnode, the logical Window
> contains aggregate func info and window info, it's similar to Flink
> LogicalWIndowAggregate, so we can add an convert rule to directly convert
> LogicalWindow into DataStreamAggregate relnode, and if Calcite support more
> grammar, we can extend the convert rule.
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