[jira] [Updated] (FLINK-5047) Add sliding group-windows for batch tables

["ASF GitHub Bot (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/FLINK-5047?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

ASF GitHub Bot updated FLINK-5047:
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    Labels: pull-request-available  (was: )

> Add sliding group-windows for batch tables
> ------------------------------------------
>
>                 Key: FLINK-5047
>                 URL: https://issues.apache.org/jira/browse/FLINK-5047
>             Project: Flink
>          Issue Type: New Feature
>          Components: Table API & SQL
>            Reporter: Jark Wu
>            Assignee: Timo Walther
>            Priority: Major
>              Labels: pull-request-available
>
> Add Slide group-windows for batch tables as described in [FLIP-11|https://cwiki.apache.org/confluence/display/FLINK/FLIP-11%3A+Table+API+Stream+Aggregations].
> There are two ways to implement sliding windows for batch:
> 1. replicate the output in order to assign keys for overlapping windows. This is probably the more straight-forward implementation and supports any aggregation function but blows up the data volume.
> 2. if the aggregation functions are combinable / pre-aggregatable, we can also find the largest tumbling window size from which the sliding windows can be assembled. This is basically the technique used to express sliding windows with plain SQL (GROUP BY + OVER clauses). For a sliding window Slide(10 minutes, 2 minutes) this would mean to first compute aggregates of non-overlapping (tumbling) 2 minute windows and assembling consecutively 5 of these into a sliding window (could be done in a MapPartition with sorted input). The implementation could be done as an optimizer rule to split the sliding aggregate into a tumbling aggregate and a SQL WINDOW operator. Maybe it makes sense to implement the WINDOW clause first and reuse this for sliding windows.
> 3. There is also a third, hybrid solution: Doing the pre-aggregation on the largest non-overlapping windows (as in 2) and replicating these results and processing those as in the 1) approach. The benefits of this is that it a) is based on the implementation that supports non-combinable aggregates (which is required in any case) and b) that it does not require the implementation of the SQL WINDOW operator. Internally, this can be implemented again as an optimizer rule that translates the SlidingWindow into a pre-aggregating TublingWindow and a final SlidingWindow (with replication).
> see FLINK-4692 for more discussion



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