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https://issues.apache.org/jira/browse/FLINK-5047?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15947948#comment-15947948
]
ASF GitHub Bot commented on FLINK-5047:
---------------------------------------
Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/3589#discussion_r108791475
--- Diff:
flink-libraries/flink-table/src/test/scala/org/apache/flink/table/runtime/dataset/DataSetWindowAggregateITCase.scala
---
@@ -354,4 +354,94 @@ class DataSetWindowAggregateITCase(
val results = windowedTable.toDataSet[Row].collect()
TestBaseUtils.compareResultAsText(results.asJava, expected)
}
+
+ @Test
+ def testEventTimeSlidingGroupWindowOverCountOverlappingFullPane(): Unit
= {
+ // please keep this test in sync with the DataStream processing-time
variant
+ val env = ExecutionEnvironment.getExecutionEnvironment
+ val tEnv = TableEnvironment.getTableEnvironment(env)
+
+ val table = env
+ .fromCollection(data)
+ .toTable(tEnv, 'long, 'int, 'double, 'float, 'bigdec, 'string)
+
+ val windowedTable = table
+ .window(Slide over 4.rows every 2.rows on 'long as 'w)
+ .groupBy('string, 'w)
+ .select('string, 'int.count)
+
+ val expected =
+ "Hello world,2\n" +
+ "Hello,2"
--- End diff --
shouldn't this be "Hello,4"? There are four rows with `'string = "Hello"`
in the data set. Shouldn't those be aggregated in a sliding count window of
size 4?
> Add sliding group-windows for batch tables
> ------------------------------------------
>
> Key: FLINK-5047
> URL: https://issues.apache.org/jira/browse/FLINK-5047
> Project: Flink
> Issue Type: Sub-task
> Components: Table API & SQL
> Reporter: Jark Wu
> Assignee: Timo Walther
>
> 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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