[
https://issues.apache.org/jira/browse/FLINK-8951?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17329538#comment-17329538
]
Flink Jira Bot commented on FLINK-8951:
---------------------------------------
This issue is assigned but has not received an update in 7 days so it has been
labeled "stale-assigned". If you are still working on the issue, please give an
update and remove the label. If you are no longer working on the issue, please
unassign so someone else may work on it. In 7 days the issue will be
automatically unassigned.
> Support OVER windows PARTITION BY (rounded) timestamp
> -----------------------------------------------------
>
> Key: FLINK-8951
> URL: https://issues.apache.org/jira/browse/FLINK-8951
> Project: Flink
> Issue Type: New Feature
> Components: Table SQL / API
> Reporter: Fabian Hueske
> Assignee: TANG Wen-hui
> Priority: Minor
> Labels: stale-assigned, stale-minor
>
> There are a few interesting use cases that can be addressed by queries that
> follow the following pattern
> {code:sql}
> SELECT sensorId COUNT(*) OVER (PARTITION BY CEIL(rowtime TO HOUR) ORDER BY
> temp ROWS BETWEEN UNBOUNDED preceding AND CURRENT ROW) FROM sensors
> {code}
> Such queries can be used to compute rolling cascading (tumbling) windows with
> aggregates that are reset in regular intervals. This can be useful for TOP-K
> per minute/hour/day queries.
> Right now, such {{OVER}} windows are not supported, because we require that
> the {{ORDER BY}} clause is defined on a timestamp (time indicator) attribute.
> In order to support this kind of queries, we would require that the
> {{PARTITION BY}} clause contains a timestamp (time indicator) attribute or a
> function that is defined on it and which is monotonicity preserving. Once the
> optimizer identifies this case, it could translate the query into a special
> time-partitioned OVER window operator.
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
