[
https://issues.apache.org/jira/browse/FLINK-6228?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15978299#comment-15978299
]
ASF GitHub Bot commented on FLINK-6228:
---------------------------------------
Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/3743#discussion_r112636908
--- Diff:
flink-libraries/flink-table/src/test/scala/org/apache/flink/table/api/scala/stream/table/OverWindowITCase.scala
---
@@ -0,0 +1,265 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.flink.table.api.scala.stream.table
+
+import org.apache.flink.api.scala._
+import org.apache.flink.streaming.api.TimeCharacteristic
+import org.apache.flink.streaming.api.functions.source.SourceFunction
+import
org.apache.flink.streaming.api.functions.source.SourceFunction.SourceContext
+import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
+import org.apache.flink.streaming.api.watermark.Watermark
+import org.apache.flink.table.api.TableEnvironment
+import org.apache.flink.table.api.scala._
+import
org.apache.flink.table.api.scala.stream.table.OverWindowITCase.{RowTimeSourceFunction}
+import org.apache.flink.table.api.scala.stream.utils.{StreamITCase,
StreamingWithStateTestBase}
+import org.apache.flink.types.Row
+import org.junit.Assert._
+import org.junit.Test
+
+import scala.collection.mutable
+
+class OverWindowITCase extends StreamingWithStateTestBase {
+
+ @Test
+ def testProcTimeUnBoundedPartitionedRowOver(): Unit = {
+
+ val data = List(
+ (1L, 1, "Hello"),
+ (2L, 2, "Hello"),
+ (3L, 3, "Hello"),
+ (4L, 4, "Hello"),
+ (5L, 5, "Hello"),
+ (6L, 6, "Hello"),
+ (7L, 7, "Hello World"),
+ (8L, 8, "Hello World"),
+ (20L, 20, "Hello World"))
+
+ val env = StreamExecutionEnvironment.getExecutionEnvironment
+ env.setParallelism(1)
+ val tEnv = TableEnvironment.getTableEnvironment(env)
+ StreamITCase.testResults = mutable.MutableList()
+ StreamITCase.clear
+ val stream = env.fromCollection(data)
+ val table = stream.toTable(tEnv, 'a, 'b, 'c)
+
+ val windowedTable = table
+ .window(
+ Over partitionBy 'c orderBy 'procTime preceding UNBOUNDED_ROW
following CURRENT_ROW as 'w)
+ .select('c, 'b.count over 'w as 'mycount)
+ .select('c, 'mycount)
+
+ val results = windowedTable.toDataStream[Row]
+ results.addSink(new StreamITCase.StringSink)
+ env.execute()
+
+ val expected = Seq(
+ "Hello World,1", "Hello World,2", "Hello World,3",
+ "Hello,1", "Hello,2", "Hello,3", "Hello,4", "Hello,5", "Hello,6")
+ assertEquals(expected.sorted, StreamITCase.testResults.sorted)
+ }
+
+ @Test
+ def testRowTimeUnBoundedPartitionedRangeOver(): Unit = {
+ val env = StreamExecutionEnvironment.getExecutionEnvironment
+ val tEnv = TableEnvironment.getTableEnvironment(env)
+ env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
+ env.setStateBackend(getStateBackend)
+ StreamITCase.testResults = mutable.MutableList()
+ StreamITCase.clear
+ env.setParallelism(1)
+
+ val data = Seq(
+ Left(14000005L, (1, 1L, "Hi")),
+ Left(14000000L, (2, 1L, "Hello")),
+ Left(14000002L, (1, 1L, "Hello")),
+ Left(14000002L, (1, 2L, "Hello")),
+ Left(14000002L, (1, 3L, "Hello world")),
+ Left(14000003L, (2, 2L, "Hello world")),
+ Left(14000003L, (2, 3L, "Hello world")),
+ Right(14000020L),
+ Left(14000021L, (1, 4L, "Hello world")),
+ Left(14000022L, (1, 5L, "Hello world")),
+ Left(14000022L, (1, 6L, "Hello world")),
+ Left(14000022L, (1, 7L, "Hello world")),
+ Left(14000023L, (2, 4L, "Hello world")),
+ Left(14000023L, (2, 5L, "Hello world")),
+ Right(14000030L)
+ )
+ val table = env
+ .addSource(new RowTimeSourceFunction[(Int, Long, String)](data))
+ .toTable(tEnv).as('a, 'b, 'c)
+
+ val windowedTable = table
+ .window(Over partitionBy 'a orderBy 'rowtime preceding
UNBOUNDED_RANGE following
+ CURRENT_RANGE as 'w)
+ .select(
+ 'a, 'b, 'c,
+ 'b.sum over 'w,
+ 'b.count over 'w,
+ 'b.avg over 'w,
+ 'b.max over 'w,
+ 'b.min over 'w)
+
+ val result = windowedTable.toDataStream[Row]
+ result.addSink(new StreamITCase.StringSink)
+ env.execute()
+
+ val expected = mutable.MutableList(
+ "1,1,Hello,6,3,2,3,1",
+ "1,2,Hello,6,3,2,3,1",
+ "1,3,Hello world,6,3,2,3,1",
+ "1,1,Hi,7,4,1,3,1",
+ "2,1,Hello,1,1,1,1,1",
+ "2,2,Hello world,6,3,2,3,1",
+ "2,3,Hello world,6,3,2,3,1",
+ "1,4,Hello world,11,5,2,4,1",
+ "1,5,Hello world,29,8,3,7,1",
+ "1,6,Hello world,29,8,3,7,1",
+ "1,7,Hello world,29,8,3,7,1",
+ "2,4,Hello world,15,5,3,5,1",
+ "2,5,Hello world,15,5,3,5,1"
+ )
+
+ assertEquals(expected.sorted, StreamITCase.testResults.sorted)
+ }
+
+ @Test
+ def testProcTimeBoundedPartitionedRangeOver(): Unit = {
+
+ val data = List(
+ (1, 1L, 0, "Hallo", 1L),
+ (2, 2L, 1, "Hallo Welt", 2L),
+ (2, 3L, 2, "Hallo Welt wie", 1L),
+ (3, 4L, 3, "Hallo Welt wie gehts?", 2L),
+ (3, 5L, 4, "ABC", 2L),
+ (3, 6L, 5, "BCD", 3L),
+ (4, 7L, 6, "CDE", 2L),
+ (4, 8L, 7, "DEF", 1L),
+ (4, 9L, 8, "EFG", 1L),
+ (4, 10L, 9, "FGH", 2L),
+ (5, 11L, 10, "GHI", 1L),
+ (5, 12L, 11, "HIJ", 3L),
+ (5, 13L, 12, "IJK", 3L),
+ (5, 14L, 13, "JKL", 2L),
+ (5, 15L, 14, "KLM", 2L))
+
+ val env = StreamExecutionEnvironment.getExecutionEnvironment
+ env.setStateBackend(getStateBackend)
+ val tEnv = TableEnvironment.getTableEnvironment(env)
+ env.setParallelism(1)
+ StreamITCase.testResults = mutable.MutableList()
+
+ val stream = env.fromCollection(data)
+ val table = stream.toTable(tEnv).as('a, 'b, 'c, 'd, 'e)
+
+ val windowedTable = table
+ .window(Over partitionBy 'a orderBy 'proctime preceding 4.rows
following CURRENT_ROW as 'w)
+ .select('a, 'c.sum over 'w, 'c.min over 'w)
+ val result = windowedTable.toDataStream[Row]
+ result.addSink(new StreamITCase.StringSink)
+ env.execute()
+
+ val expected = mutable.MutableList(
+ "1,0,0",
+ "2,1,1",
+ "2,3,1",
+ "3,3,3",
+ "3,7,3",
+ "3,12,3",
+ "4,6,6",
+ "4,13,6",
+ "4,21,6",
+ "4,30,6",
+ "5,10,10",
+ "5,21,10",
+ "5,33,10",
+ "5,46,10",
+ "5,60,10")
+
+ assertEquals(expected.sorted, StreamITCase.testResults.sorted)
+ }
+
+ @Test
+ def testRowTimeBoundedPartitionedRowOver(): Unit = {
--- End diff --
Fair enough. The `OverWindowTest` should be sufficient.
However, I would keep a few integration tests for the Table API as well.
> Integrating the OVER windows in the Table API
> ---------------------------------------------
>
> Key: FLINK-6228
> URL: https://issues.apache.org/jira/browse/FLINK-6228
> Project: Flink
> Issue Type: Sub-task
> Components: Table API & SQL
> Reporter: sunjincheng
> Assignee: sunjincheng
>
> Syntax:
> {code}
> table
> .overWindows(
> (Rows|Range [ partitionBy value_expression , ... [ n ]] [ orderBy
> order_by_expression]
> (preceding
> UNBOUNDED|value_specification.(rows|milli|second|minute|hour|day|month|year)|CURRENTROW)
> [following
> UNBOUNDED|value_specification.(rows|milli|second|minute|hour|day|month|year)|CURRENTROW]
> as alias,...[n])
> )
> .select( [col1,...[n]], (agg(col1) OVER overWindowAlias, … [n])
> {code}
> Implement restrictions:
> * 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 Before the
> [FLINK-5884|https://issues.apache.org/jira/browse/FLINK-5884] implementation
> orderBy may only have ‘rowtime/’proctime(for stream)/‘specific-time-field(for
> batch).
> * FOLLOWING is not supported.
> User interface design document [See |
> https://docs.google.com/document/d/13Z-Ovx3jwtmzkSweJyGkMy751BouhuJ29Y1CTNZt2DI/edit#]
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