Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/3809#discussion_r114444069
--- Diff:
flink-libraries/flink-table/src/test/scala/org/apache/flink/table/api/scala/stream/table/DStreamUDAGGITCase.scala
---
@@ -0,0 +1,309 @@
+/*
+ * 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 java.math.BigDecimal
+
+import org.apache.flink.api.java.typeutils.RowTypeInfo
+import org.apache.flink.api.scala._
+import
org.apache.flink.streaming.api.functions.AssignerWithPunctuatedWatermarks
+import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
+import org.apache.flink.streaming.api.TimeCharacteristic
+import org.apache.flink.table.utils.TableTestBase
+import org.apache.flink.streaming.api.datastream.{DataStream =>
JDataStream}
+import
org.apache.flink.streaming.api.environment.{StreamExecutionEnvironment =>
JavaExecutionEnv}
+import org.apache.flink.streaming.api.scala.{DataStream,
StreamExecutionEnvironment => ScalaExecutionEnv}
+import org.apache.flink.streaming.api.watermark.Watermark
+import
org.apache.flink.table.api.java.utils.UserDefinedAggFunctions.{WeightedAvg,
WeightedAvgWithMerge, WeightedAvgWithRetract}
+import org.apache.flink.table.api.scala._
+import
org.apache.flink.table.api.scala.stream.table.DStreamUDAGGITCase.TimestampAndWatermarkWithOffset
+import org.apache.flink.table.api.scala.stream.utils.StreamITCase
+import org.apache.flink.table.api.{SlidingWindow, TableEnvironment, Types}
+import org.apache.flink.streaming.util.StreamingMultipleProgramsTestBase
+import org.apache.flink.table.functions.aggfunctions.CountAggFunction
+import org.apache.flink.types.Row
+import org.junit.Assert._
+import org.junit.Test
+import org.mockito.Mockito.{mock, when}
+
+import scala.collection.mutable
+
+/**
+ * We only test some aggregations until better testing of constructed
DataStream
+ * programs is possible.
+ */
+class DStreamUDAGGITCase
+ extends StreamingMultipleProgramsTestBase {
+
+ val data = List(
+ //('long, 'int, 'double, 'float, 'bigdec, 'string)
+ (1000L, 1, 1d, 1f, new BigDecimal("1"), "Hello"),
+ (2000L, 2, 2d, 2f, new BigDecimal("2"), "Hello"),
+ (3000L, 3, 3d, 3f, new BigDecimal("3"), "Hello"),
+ (5000L, 5, 5d, 5f, new BigDecimal("5"), "Hi"),
+ (6000L, 6, 6d, 6f, new BigDecimal("6"), "Hi"),
+ (7000L, 7, 7d, 7f, new BigDecimal("7"), "Hi"),
+ (8000L, 8, 8d, 8f, new BigDecimal("8"), "Hello"),
+ (9000L, 9, 9d, 9f, new BigDecimal("9"), "Hello"),
+ (4000L, 4, 4d, 4f, new BigDecimal("4"), "Hello"),
+ (10000L, 10, 10d, 10f, new BigDecimal("10"), "Hi"),
+ (11000L, 11, 11d, 11f, new BigDecimal("11"), "Hi"),
+ (12000L, 12, 12d, 12f, new BigDecimal("12"), "Hi"),
+ (16000L, 16, 16d, 16f, new BigDecimal("16"), "Hello"))
+
+ @Test
+ def testUdaggSlidingWindowGroupedAggregate(): Unit = {
+ val env = StreamExecutionEnvironment.getExecutionEnvironment
+ env.setParallelism(1)
+ val tEnv = TableEnvironment.getTableEnvironment(env)
+ StreamITCase.testResults = mutable.MutableList()
+
+ val stream = env.fromCollection(data).map(t => (t._1, t._2, t._3,
t._4, t._6))
+ val table = stream.toTable(tEnv, 'long, 'int, 'double, 'float, 'string)
+
+ val countFun = new CountAggFunction
+
+ val weightAvgFun = new WeightedAvg
+
+ val windowedTable = table
+ .window(Slide over 4.rows every 2.rows as 'w)
+ .groupBy('w, 'string)
+ .select(
+ 'string,
+ countFun('float),
+ 'double.sum,
+ weightAvgFun('long, 'int),
+ weightAvgFun('int, 'int))
+
+ val results = windowedTable.toDataStream[Row]
+ results.addSink(new StreamITCase.StringSink)
+ env.execute()
+
+ val expected = Seq(
+ "Hello,2,3.0,1666,1", "Hi,2,11.0,5545,5", "Hello,4,14.0,5571,5",
+ "Hello,4,24.0,7083,7", "Hi,4,28.0,7500,7", "Hi,4,40.0,10350,10")
+ assertEquals(expected, StreamITCase.testResults)
+ }
+
+ @Test
+ def testUdaggSessionWindowGroupedAggregate(): Unit = {
+ val env = StreamExecutionEnvironment.getExecutionEnvironment
+ env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
+ env.setParallelism(1)
+ val tEnv = TableEnvironment.getTableEnvironment(env)
+ StreamITCase.testResults = mutable.MutableList()
+
+ val countFun = new CountAggFunction
+
+ val weightAvgWithMergeFun = new WeightedAvgWithMerge
+
+ val stream = env
+ .fromCollection(data)
+ .assignTimestampsAndWatermarks(new
TimestampAndWatermarkWithOffset(10000))
+ val table = stream.toTable(tEnv, 'long, 'int, 'double, 'float,
'bigdec, 'string)
+
+ val windowedTable = table
+ .window(Session withGap 5.second on 'rowtime as 'w)
+ .groupBy('w, 'string)
+ .select(
+ 'string,
+ countFun('bigdec),
+ 'float.sum,
+ weightAvgWithMergeFun('long, 'int),
+ weightAvgWithMergeFun('int, 'int))
+
+ val results = windowedTable.toDataStream[Row]
+ results.addSink(new StreamITCase.StringSink)
+ env.execute()
+
+ val expected = Seq("Hello,6,27.0,6481,6", "Hi,6,51.0,9313,9",
"Hello,1,16.0,16000,16")
+ assertEquals(expected, StreamITCase.testResults)
+ }
+
+ @Test
+ def testUdaggProcTimeUnBoundedPartitionedRowOver(): Unit = {
+
+ val env = StreamExecutionEnvironment.getExecutionEnvironment
+ env.setParallelism(1)
+ val tEnv = TableEnvironment.getTableEnvironment(env)
+ StreamITCase.testResults = mutable.MutableList()
+ StreamITCase.clear
+ val stream = env.fromCollection(data).map(t => (t._4, t._1, t._2,
t._3, t._6))
+ val table = stream.toTable(tEnv, 'float, 'long, 'int, 'double, 'string)
+
+ val countFun = new CountAggFunction
+
+ val weightedAvgWithRetractFun = new WeightedAvgWithRetract
+
+ val windowedTable = table
+ .window(
+ Over partitionBy 'string orderBy 'proctime preceding UNBOUNDED_ROW
as 'w)
+ .select(
+ 'string,
+ 'float,
+ countFun('string) over 'w,
+ 'double.sum over 'w,
+ weightedAvgWithRetractFun('long, 'int) over 'w,
+ weightedAvgWithRetractFun('int, 'int) over 'w)
+
+ val results = windowedTable.toDataStream[Row]
+ results.addSink(new StreamITCase.StringSink)
+ env.execute()
+
+ val expected = Seq(
+ "Hello,1.0,1,1.0,1000,1", "Hello,2.0,2,3.0,1666,1",
"Hello,3.0,3,6.0,2333,2",
+ "Hi,5.0,1,5.0,5000,5", "Hi,6.0,2,11.0,5545,5",
"Hi,7.0,3,18.0,6111,6",
+ "Hello,8.0,4,14.0,5571,5", "Hello,9.0,5,23.0,6913,6",
"Hello,4.0,6,27.0,6481,6",
+ "Hi,10.0,4,28.0,7500,7", "Hi,11.0,5,39.0,8487,8",
"Hi,12.0,6,51.0,9313,9",
+ "Hello,16.0,7,43.0,10023,10")
+ assertEquals(expected, StreamITCase.testResults)
+ }
+
+ @Test
+ def testUdaggProcTimeUnBoundedPartitionedRowOverSQL(): Unit = {
+
+ val env = StreamExecutionEnvironment.getExecutionEnvironment
+ env.setParallelism(1)
+ val tEnv = TableEnvironment.getTableEnvironment(env)
+ StreamITCase.testResults = mutable.MutableList()
+ StreamITCase.clear
+ val stream = env.fromCollection(data).map(t => (t._4, t._1, t._2,
t._3, t._6))
+ val table = stream.toTable(tEnv, 'f, 'l, 'i, 'd, 's)
+
+ tEnv.registerTable("T1", table)
+ tEnv.registerFunction("countFun", new CountAggFunction)
+ tEnv.registerFunction("wAvgWithRetract", new WeightedAvgWithRetract)
+ val sqlQuery = "SELECT " +
+ "s, " +
+ "f, " +
+ "countFun(i) OVER (PARTITION BY s ORDER BY ProcTime() RANGE
UNBOUNDED preceding)," +
+ "sum(d) OVER (PARTITION BY s ORDER BY ProcTime() RANGE UNBOUNDED
preceding)," +
+ "wAvgWithRetract(l,i) OVER (PARTITION BY s ORDER BY ProcTime() RANGE
UNBOUNDED preceding)," +
+ "wAvgWithRetract(i,i) OVER (PARTITION BY s ORDER BY ProcTime() RANGE
UNBOUNDED preceding)" +
+ "from T1"
+
+ val results = tEnv.sql(sqlQuery).toDataStream[Row]
+ results.addSink(new StreamITCase.StringSink)
+ env.execute()
+
+ val expected = Seq(
+ "Hello,1.0,1,1.0,1000,1", "Hello,2.0,2,3.0,1666,1",
"Hello,3.0,3,6.0,2333,2",
+ "Hi,5.0,1,5.0,5000,5", "Hi,6.0,2,11.0,5545,5",
"Hi,7.0,3,18.0,6111,6",
+ "Hello,8.0,4,14.0,5571,5", "Hello,9.0,5,23.0,6913,6",
"Hello,4.0,6,27.0,6481,6",
+ "Hi,10.0,4,28.0,7500,7", "Hi,11.0,5,39.0,8487,8",
"Hi,12.0,6,51.0,9313,9",
+ "Hello,16.0,7,43.0,10023,10")
+ assertEquals(expected, StreamITCase.testResults)
+ }
+
+ @Test
+ def testUdaggGroupedAggregateSQL(): Unit = {
+
+ val env = StreamExecutionEnvironment.getExecutionEnvironment
+ env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
+ env.setParallelism(1)
+ val tEnv = TableEnvironment.getTableEnvironment(env)
+ StreamITCase.testResults = mutable.MutableList()
+ StreamITCase.clear
+ val stream = env
+ .fromCollection(data)
+ .assignTimestampsAndWatermarks(new
TimestampAndWatermarkWithOffset(10000))
+ .map(t => (t._4, t._1, t._2, t._3, t._6))
+ val table = stream.toTable(tEnv, 'f, 'l, 'i, 'd, 's)
+
+ tEnv.registerTable("T1", table)
+ tEnv.registerFunction("countFun", new CountAggFunction)
+ tEnv.registerFunction("wAvgWithRetract", new WeightedAvgWithRetract)
+ val sqlQuery =
+ "SELECT s, countFun(i), SUM(d)" +
+ "FROM T1 " +
+ "GROUP BY s, TUMBLE(rowtime(), INTERVAL '5' SECOND)"
+
+ val results = tEnv.sql(sqlQuery).toDataStream[Row]
+ results.addSink(new StreamITCase.StringSink)
+ env.execute()
+
+ val expected = Seq(
+ "Hello,4,10.0", "Hi,3,18.0", "Hello,2,17.0", "Hi,3,33.0",
"Hello,1,16.0")
+ assertEquals(expected, StreamITCase.testResults)
+ }
+
+ @Test
+ def testUdaggJavaAPI(): Unit = {
+ // mock
+ val ds = mock(classOf[DataStream[Row]])
+ val jDs = mock(classOf[JDataStream[Row]])
+ val typeInfo = new RowTypeInfo(Seq(Types.INT, Types.LONG,
Types.STRING): _*)
+ when(ds.javaStream).thenReturn(jDs)
+ when(jDs.getType).thenReturn(typeInfo)
+ // Scala environment
+ val env = mock(classOf[ScalaExecutionEnv])
+ val tableEnv = TableEnvironment.getTableEnvironment(env)
+ val in1 = ds.toTable(tableEnv).as('int, 'long, 'string)
+
+ // Java environment
+ val javaEnv = mock(classOf[JavaExecutionEnv])
+ val javaTableEnv = TableEnvironment.getTableEnvironment(javaEnv)
+ val in2 = javaTableEnv.fromDataStream(jDs).as("int, long, string")
+
+ // Java API
+ javaTableEnv.registerFunction("myCountFun", new CountAggFunction)
+ javaTableEnv.registerFunction("weightAvgFun", new WeightedAvg)
+ var javaTable = in2.window((new SlidingWindow(4.rows, 2.rows)).as("w"))
+ .groupBy("w, string")
+ .select(
+ "string, " +
+ "myCountFun(string), " +
+ "int.sum, " +
+ "weightAvgFun(long, int), " +
+ "weightAvgFun(int, int)")
+
+ // Scala API
+ val myCountFun = new CountAggFunction
+ val weightAvgFun = new WeightedAvg
+ var scalaTable = in1.window(Slide over 4.rows every 2.rows as 'w)
+ .groupBy('w, 'string)
+ .select(
+ 'string, myCountFun('string), 'int.sum, weightAvgFun('long, 'int),
+ weightAvgFun('int, 'int))
+
+ val helper = new TableTestBase
+ helper.verifyTableEquals(scalaTable, javaTable)
+ }
+}
+
+object DStreamUDAGGITCase {
+
+ class TimestampAndWatermarkWithOffset(offset: Int)
--- End diff --
Can be removed if we use `.assignAscendingTimestamps(_._1)`
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
