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?
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