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https://issues.apache.org/jira/browse/FLINK-5657?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15863303#comment-15863303
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sunjincheng edited comment on FLINK-5657 at 2/13/17 7:59 AM:
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Hi,guys，I made a preliminary implementation of this JIRA.
My approach is:
1. Calcite -> Flink
"LogicalProject with RexOver expression" -- (normalize rule) -> "Calcite's
LogicalWindow" -- (opt rule) -> DataStreamRowWindowAggregate
2. datastreamAPI:
a. With partitionBy situation:
approach1: inputDS.map().keyby().reduce().map()
approach2: inputDS.map().Keyby().process()
b. Without paritionBy situation:
inputDS.map().setParallelism(1), map has implement CheckPointedFunction.
3. About OrderBy:
According to the natural order of elements, procTime () use for generate
end-time of the window and guaranteed pass the sql validation.
HI,[~fhueske] IMO. “Calcite -> FLINK” part should be rowWindow related JIRAs
shared part, in order to share as soon as possible, I would like to change
JIRA. into two subtasks:
1. rowWindow with partitionBy
2. rowWindow without partitionBy.
It's that make sense for you? I would be very grateful if you could give me
some advices.
was (Author: sunjincheng121):
Hi,guys，I made a preliminary implementation of this JIRA.
My approach is:
1. Calcite -> Flink
"LogicalProject with RexOver expression" --(normalize rule)-> "Calcite's
LogicalWindow" --(opt rule) -> DataStreamRowWindowAggregate
2. datastreamAPI:
a. With partitionBy situation:
approach1: inputDS.map().keyby().reduce().map()
approach2: inputDS.map().Keyby().process()
b. Without paritionBy situation:
inputDS.map().setParallelism(1), map has implement CheckPointedFunction.
3. About OrderBy:
According to the natural order of elements, procTime () use for generate
end-time of the window and guaranteed pass the sql validation.
HI,[~fhueske] IMO. “Calcite -> FLINK” part should be rowWindow related JIRAs
shared part, in order to share as soon as possible, I would like to change
JIRA. into two subtasks:
1. rowWindow with partitionBy
2. rowWindow without partitionBy.
It's that make sense for you? I would be very grateful if you could give me
some advices.
> Add processing time OVER RANGE BETWEEN UNBOUNDED PRECEDING aggregation to SQL
> -----------------------------------------------------------------------------
>
> Key: FLINK-5657
> URL: https://issues.apache.org/jira/browse/FLINK-5657
> Project: Flink
> Issue Type: Sub-task
> Components: Table API & SQL
> Reporter: Fabian Hueske
> Assignee: sunjincheng
>
> The goal of this issue is to add support for OVER RANGE aggregations on
> processing time streams to the SQL interface.
> Queries similar to the following should be supported:
> {code}
> SELECT
> a,
> SUM(b) OVER (PARTITION BY c ORDER BY procTime() RANGE BETWEEN UNBOUNDED
> PRECEDING AND CURRENT ROW) AS sumB,
> MIN(b) OVER (PARTITION BY c ORDER BY procTime() RANGE BETWEEN UNBOUNDED
> PRECEDING AND CURRENT ROW) AS minB
> FROM myStream
> {code}
> The following restrictions should initially apply:
> - All OVER clauses in the same SELECT clause must be exactly the same.
> - The PARTITION BY clause is optional (no partitioning results in single
> threaded execution).
> - The ORDER BY clause may only have procTime() as parameter. procTime() is a
> parameterless scalar function that just indicates processing time mode.
> - bounded PRECEDING is not supported (see FLINK-5654)
> - FOLLOWING is not supported.
> The restrictions will be resolved in follow up issues. If we find that some
> of the restrictions are trivial to address, we can add the functionality in
> this issue as well.
> This issue includes:
> - Design of the DataStream operator to compute OVER ROW aggregates
> - Translation from Calcite's RelNode representation (LogicalProject with
> RexOver expression).
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