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https://issues.apache.org/jira/browse/FLINK-5658?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15937672#comment-15937672
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ASF GitHub Bot commented on FLINK-5658:
---------------------------------------
Github user hongyuhong commented on a diff in the pull request:
https://github.com/apache/flink/pull/3386#discussion_r107583714
--- Diff:
flink-libraries/flink-table/src/test/scala/org/apache/flink/table/api/scala/stream/sql/SqlITCase.scala
---
@@ -317,4 +320,193 @@ class SqlITCase extends StreamingWithStateTestBase {
result.addSink(new StreamITCase.StringSink)
env.execute()
}
+
+ /** test sliding event-time unbounded window with partition by **/
+ @Test
+ def testUnboundedEventTimeRowWindowWithPartition(): Unit = {
+ val env = StreamExecutionEnvironment.getExecutionEnvironment
+ val tEnv = TableEnvironment.getTableEnvironment(env)
+ env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
+ env.setStateBackend(getStateBackend)
+ StreamITCase.testResults = mutable.MutableList()
+ env.setParallelism(1)
+
+ val sqlQuery = "SELECT a, b, c, " +
+ "SUM(b) over (" +
+ "partition by a order by rowtime() range between unbounded preceding
and current row), " +
+ "count(b) over (" +
+ "partition by a order by rowtime() range between unbounded preceding
and current row), " +
+ "avg(b) over (" +
+ "partition by a order by rowtime() range between unbounded preceding
and current row), " +
+ "max(b) over (" +
+ "partition by a order by rowtime() range between unbounded preceding
and current row), " +
+ "min(b) over (" +
+ "partition by a order by rowtime() range between unbounded preceding
and current row) " +
+ "from T1"
+
+ val t1 = env.addSource[(Int, Long, String)](new SourceFunction[(Int,
Long, String)] {
+ override def run(ctx: SourceContext[(Int, Long, String)]): Unit = {
+ ctx.collectWithTimestamp((1, 1L, "Hi"), 14000005L)
+ ctx.collectWithTimestamp((2, 1L, "Hello"), 14000000L)
+ ctx.collectWithTimestamp((3, 1L, "Hello"), 14000002L)
+ ctx.collectWithTimestamp((1, 2L, "Hello"), 14000003L)
+ ctx.collectWithTimestamp((1, 3L, "Hello world"), 14000004L)
+ ctx.collectWithTimestamp((3, 2L, "Hello world"), 14000007L)
+ ctx.collectWithTimestamp((2, 2L, "Hello world"), 14000008L)
+ ctx.emitWatermark(new Watermark(14000010L))
+ ctx.collectWithTimestamp((1, 4L, "Hello world"), 14000008L)
+ ctx.collectWithTimestamp((2, 3L, "Hello world"), 14000008L)
+ ctx.collectWithTimestamp((3, 3L, "Hello world"), 14000008L)
+ ctx.collectWithTimestamp((1, 5L, "Hello world"), 14000012L)
+ ctx.emitWatermark(new Watermark(14000020L))
+ ctx.collectWithTimestamp((1, 6L, "Hello world"), 14000021L)
+ ctx.collectWithTimestamp((1, 6L, "Hello world"), 14000019L)
+ ctx.collectWithTimestamp((2, 4L, "Hello world"), 14000018L)
+ ctx.collectWithTimestamp((3, 4L, "Hello world"), 14000018L)
+ ctx.collectWithTimestamp((2, 5L, "Hello world"), 14000022L)
+ ctx.collectWithTimestamp((3, 5L, "Hello world"), 14000022L)
+ ctx.collectWithTimestamp((1, 7L, "Hello world"), 14000024L)
+ ctx.collectWithTimestamp((1, 8L, "Hello world"), 14000023L)
+ ctx.collectWithTimestamp((1, 9L, "Hello world"), 14000021L)
+ ctx.emitWatermark(new Watermark(14000030L))
+ }
+
+ override def cancel(): Unit = {}
+ }).toTable(tEnv).as('a, 'b, 'c)
+
+ tEnv.registerTable("T1", t1)
+
+ val result = tEnv.sql(sqlQuery).toDataStream[Row]
+ result.addSink(new StreamITCase.StringSink)
+ env.execute()
+
+ val expected = mutable.MutableList(
+ "1,2,Hello,2,1,2,2,2",
+ "1,3,Hello world,5,2,2,3,2",
+ "1,1,Hi,6,3,2,3,1",
+ "2,1,Hello,1,1,1,1,1",
+ "2,2,Hello world,3,2,1,2,1",
+ "3,1,Hello,1,1,1,1,1",
+ "3,2,Hello world,3,2,1,2,1",
+ "1,5,Hello world,11,4,2,5,1",
+ "1,6,Hello world,17,5,3,6,1",
+ "1,9,Hello world,26,6,4,9,1",
+ "1,8,Hello world,34,7,4,9,1",
+ "1,7,Hello world,41,8,5,9,1",
+ "2,5,Hello world,8,3,2,5,1",
+ "3,5,Hello world,8,3,2,5,1"
+ )
+ assertEquals(expected.sorted, StreamITCase.testResults.sorted)
+ }
+
+ /** test sliding event-time unbounded window without partitiion by **/
+ @Test
+ def testUnboundedEventTimeRowWindowWithoutPartition(): Unit = {
+ val env = StreamExecutionEnvironment.getExecutionEnvironment
+ val tEnv = TableEnvironment.getTableEnvironment(env)
+ env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
+ env.setStateBackend(getStateBackend)
+ StreamITCase.testResults = mutable.MutableList()
+ env.setParallelism(1)
--- End diff --
Hi @fhueske, i think if we just set the source of parallelism to 1, it can
not work, cause DataStreamScan or DataStreamCalc will do source.map
transformation, after the transformation, the parallelism will not be 1, and
the data will not arrive as the order we expect, thus we cannot expect the
result, what do you think?
> Add event time OVER RANGE BETWEEN UNBOUNDED PRECEDING aggregation to SQL
> ------------------------------------------------------------------------
>
> Key: FLINK-5658
> URL: https://issues.apache.org/jira/browse/FLINK-5658
> Project: Flink
> Issue Type: Sub-task
> Components: Table API & SQL
> Reporter: Fabian Hueske
> Assignee: Yuhong Hong
>
> The goal of this issue is to add support for OVER RANGE aggregations on event
> 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 rowTime() RANGE BETWEEN UNBOUNDED
> PRECEDING AND CURRENT ROW) AS sumB,
> MIN(b) OVER (PARTITION BY c ORDER BY rowTime() 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 rowTime() as parameter. rowTime() is a
> parameterless scalar function that just indicates processing time mode.
> - bounded PRECEDING is not supported (see FLINK-5655)
> - 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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