HeartSaVioR commented on code in PR #38405: URL: https://github.com/apache/spark/pull/38405#discussion_r1006535882
########## sql/core/src/test/scala/org/apache/spark/sql/streaming/MultiStatefulOperatorsSuite.scala: ########## @@ -0,0 +1,400 @@ +/* + * 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.spark.sql.streaming + +import org.scalatest.BeforeAndAfter + +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.execution.streaming.MemoryStream +import org.apache.spark.sql.execution.streaming.state.StateStore +import org.apache.spark.sql.functions._ + +// Tests for the multiple stateful operators support. +class MultiStatefulOperatorsSuite + extends StreamTest with StateStoreMetricsTest with BeforeAndAfter { + + import testImplicits._ + + before { + SparkSession.setActiveSession(spark) // set this before force initializing 'joinExec' + spark.streams.stateStoreCoordinator // initialize the lazy coordinator + } + + after { + StateStore.stop() + } + + test("window agg -> window agg, append mode") { + withSQLConf("spark.sql.streaming.unsupportedOperationCheck" -> "false") { + val inputData = MemoryStream[Int] + + val stream = inputData.toDF() + .withColumn("eventTime", timestamp_seconds($"value")) + .withWatermark("eventTime", "0 seconds") + .groupBy(window($"eventTime", "5 seconds") as 'window) + .agg(count("*") as 'count) + .groupBy(window($"window", "10 seconds")) + .agg(count("*") as 'count, sum("count") as 'sum) + .select($"window".getField("start").cast("long").as[Long], + $"count".as[Long], $"sum".as[Long]) + + testStream(stream)( + AddData(inputData, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21), + // op1 W (0, 0) + // agg: [10, 15) 5, [15, 20) 5, [20, 25) 2 + // output: None + // state: [10, 15) 5, [15, 20) 5, [20, 25) 2 + // op2 W (0, 0) + // agg: None + // output: None + // state: None + + // no-data batch triggered + + // op1 W (0, 21) + // agg: None + // output: [10, 15) 5, [15, 20) 5 + // state: [20, 25) 2 + // op2 W (0, 21) + // agg: [10, 20) (2, 10) + // output: [10, 20) (2, 10) + // state: None + CheckNewAnswer((10, 2, 10)), // W (21, 21) [20, 25) 2 ... W (0, 21) None // [10, 20) 2 + assertNumStateRows(Seq(0, 1)), + assertNumRowsDroppedByWatermark(Seq(0, 0)), + + AddData(inputData, 22, 23, 24, 25, 26, 27, 28, 29), + // op1 W (21, 21) + // agg: [20, 25) 5, [25, 30) 4 + // output: None + // state: [20, 25) 5, [25, 30) 4 + // op2 W (21, 21) + // agg: None + // output: None + // state: None + + // no-data batch triggered + + // op1 W (21, 29) + // agg: None + // output: [20, 25) 5 + // state: [25, 30) 4 + // op2 W (20, 25) + // agg: [20, 30) (1, 5) + // output: None + // state: [20, 30) (1, 5) + CheckNewAnswer(), + assertNumStateRows(Seq(1, 1)), + assertNumRowsDroppedByWatermark(Seq(0, 0)), + + // Move the watermark. + AddData(inputData, 30, 31), + // op1 W (29, 29) + // agg: [25, 30) 5 [30, 35) 2 + // output: None + // state: [25, 30) 5 [30, 35) 2 + // op2 W (29, 29) + // agg: None + // output: None + // state: [20, 30) (1, 5) + + // no-data batch triggered + + // op1 W (29, 31) + // agg: None + // output: [25, 30) 5 + // state: [30, 35) 2 + // op2 W (29, 31) + // agg: [20, 30) (1, 5) + // output: [20, 30) (1, 5) + // state: None + CheckNewAnswer((20, 2, 10)), + assertNumStateRows(Seq(0, 1)), + assertNumRowsDroppedByWatermark(Seq(0, 0)) + ) + } + } + + test("agg -> agg -> agg, append mode") { + withSQLConf("spark.sql.streaming.unsupportedOperationCheck" -> "false") { + val inputData = MemoryStream[Int] + + val stream = inputData.toDF() + .withColumn("eventTime", timestamp_seconds($"value")) + .withWatermark("eventTime", "0 seconds") + .groupBy(window($"eventTime", "5 seconds") as 'window) + .agg(count("*") as 'count) + .groupBy(window(window_time($"window"), "10 seconds")) + .agg(count("*") as 'count, sum("count") as 'sum) + .groupBy(window(window_time($"window"), "20 seconds")) + .agg(count("*") as 'count, sum("sum") as 'sum) + .select( + $"window".getField("start").cast("long").as[Long], + $"window".getField("end").cast("long").as[Long], + $"count".as[Long], $"sum".as[Long]) + + testStream(stream)( + AddData(inputData, 0 to 37: _*), + // op1 W (0, 0) + // agg: [0, 5) 5, [5, 10) 5, [10, 15) 5, [15, 20) 5, [20, 25) 5, [25, 30) 5, [30, 35) 5, + // [35, 40) 3 + // output: None + // state: [0, 5) 5, [5, 10) 5, [10, 15) 5, [15, 20) 5, [20, 25) 5, [25, 30) 5, [30, 35) 5, + // [35, 40) 3 + // op2 W (0, 0) + // agg: None + // output: None + // state: None + + // no-data batch triggered + + // op1 W (37, 37) + // agg: None + // output: [0, 5) 5, [5, 10) 5, [10, 15) 5, [15, 20) 5, [20, 25) 5, [25, 30) 5, [30, 35) 5 + // state: [35, 40) 3 + // op2 W (0, 37) + // agg: [0, 10) (2, 10), [10, 20) (2, 10), [20, 30) (2, 10), [30, 40) (1, 5) + // output: [0, 10) (2, 10), [10, 20) (2, 10), [20, 30) (2, 10) + // state: [30, 40) (1, 5) + // op3 W (0, 37) + // agg: [0, 20) (2, 20), [20, 40) (1, 10) + // output: [0, 20) (2, 20) + // state: [20, 40) (1, 10) + CheckNewAnswer((0, 20, 2, 20)), + assertNumStateRows(Seq(1, 1, 1)), + assertNumRowsDroppedByWatermark(Seq(0, 0, 0)), + + AddData(inputData, 30 to 60: _*), + // op1 W (37, 37) + // dropped rows: [30, 35), 1 row <= note that 35, 36, 37 are still in effect + // agg: [35, 40) 8, [40, 45) 5, [45, 50) 5, [50, 55) 5, [55, 60) 5, [60, 65) 1 + // output: None + // state: [35, 40) 8, [40, 45) 5, [45, 50) 5, [50, 55) 5, [55, 60) 5, [60, 65) 1 + // op2 W (37, 37) + // output: None + // state: [30, 40) (1, 8) + // op3 W (37, 37) + // output: None + // state: [20, 40) (1, 10) + + // no-data batch + // op1 W (60, 60) + // output: [35, 40) 8, [40, 45) 5, [45, 50) 5, [50, 55) 5, [55, 60) 5 + // state: [60, 65) 1 + // op2 W (60, 60) + // agg: [30, 40) (2, 13), [40, 50) (2, 10), [50, 60), (2, 10) + // output: [30, 40) (2, 13), [40, 50) (2, 10), [50, 60), (2, 10) + // state: None + // op3 W (60, 60) + // agg: [20, 40) (2, 23), [40, 60) (2, 20) + // output: [20, 40) (2, 23), [40, 60) (2, 20) + // state: None + + CheckNewAnswer((20, 40, 2, 23), (40, 60, 2, 20)), + assertNumStateRows(Seq(0, 0, 1)), + assertNumRowsDroppedByWatermark(Seq(0, 0, 1)) + ) + } + } + + test("stream deduplication -> aggregation, append mode") { + withSQLConf("spark.sql.streaming.unsupportedOperationCheck" -> "false") { + val inputData = MemoryStream[Int] + + val deduplication = inputData.toDF() + .withColumn("eventTime", timestamp_seconds($"value")) + .withWatermark("eventTime", "10 seconds") + .dropDuplicates("value", "eventTime") + + val windowedAggregation = deduplication + .groupBy(window($"eventTime", "5 seconds") as 'window) + .agg(count("*") as 'count, sum("value") as 'sum) + .select($"window".getField("start").cast("long").as[Long], + $"count".as[Long]) + + testStream(windowedAggregation)( + // FIXME: we should revisit our watermark condition... we don't allow watermark to be + // set to negative and ensure it's at least 0, but then the inputs with timestamp 0 + // as event time can be dropped. + AddData(inputData, 1 to 15: _*), + // op1 W (0, 0) + // input: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 + // deduplicated: None + // output: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 + // state: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 + // op2 W (0, 0) + // agg: [0, 5) 4, [5, 10) 5 [10, 15) 5, [15, 20) 1 + // output: None + // state: [0, 5) 4, [5, 10) 5 [10, 15) 5, [15, 20) 1 + + // no-data batch triggered + + // op1 W (5, 5) + // agg: None + // output: None + // state: 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 + // op2 W (0, 5) + // agg: None + // output: [0, 5) 4 + // state: [5, 10) 5 [10, 15) 5, [15, 20) 1 + CheckNewAnswer((0, 4)), + assertNumStateRows(Seq(3, 10)), + assertNumRowsDroppedByWatermark(Seq(0, 0)) + ) + } + } + + test("join -> window agg, append mode") { + withSQLConf("spark.sql.streaming.unsupportedOperationCheck" -> "false") { + val input1 = MemoryStream[Int] + val inputDF1 = input1.toDF + .withColumnRenamed("value", "value1") + .withColumn("eventTime1", timestamp_seconds($"value1")) + .withWatermark("eventTime1", "0 seconds") + + val input2 = MemoryStream[Int] + val inputDF2 = input2.toDF + .withColumnRenamed("value", "value2") + .withColumn("eventTime2", timestamp_seconds($"value2")) + .withWatermark("eventTime2", "0 seconds") + + val stream = inputDF1.join(inputDF2, expr("eventTime1 = eventTime2"), "inner") + .groupBy(window($"eventTime1", "5 seconds") as 'window) + .agg(count("*") as 'count) + .select($"window".getField("start").cast("long").as[Long], $"count".as[Long]) + + testStream(stream)( + AddData(input1, 1, 2, 3, 4), + AddData(input2, 1, 2, 3, 4), + + // op1 W (0, 0) + // join output: (1, 1), (2, 2), (3, 3), (4, 4) + // state: (1, 1), (2, 2), (3, 3), (4, 4) + // op2 W (0, 0) + // agg: [0, 5) 4 + // output: None + // state: [0, 5) 4 + + // no-data batch triggered + + // op1 W (0, 4) + // join output: None + // state: (4, 4) + // agg: [0, 5) 4 + // output: None + // state: [0, 5) 4 + CheckNewAnswer(), + assertNumStateRows(Seq(1, 0)), + assertNumRowsDroppedByWatermark(Seq(0, 0)), + + // Move the watermark + AddData(input1, 5), + AddData(input2, 5), + + // op1 W (4, 5) + // join output: (5, 5) + // state: (5, 5) + // op2 W (4, 5) + // agg: [5, 10) 1 + // output: (0, 5) 4 + // state: [5, 10) 1 + + // no-data batch triggered + + // op1 W (5, 5) + // join output: None + // state: (5, 5) + // agg: None + // output: None + // state: [5, 10) 1 + CheckNewAnswer((0, 4)), + assertNumStateRows(Seq(1, 0)), + assertNumRowsDroppedByWatermark(Seq(0, 0)) + ) + } + } + + test("aggregation -> stream deduplication, append mode") { + withSQLConf("spark.sql.streaming.unsupportedOperationCheck" -> "false") { + val inputData = MemoryStream[Int] + + val aggStream = inputData.toDF() + .withColumn("eventTime", timestamp_seconds($"value")) + .withWatermark("eventTime", "0 seconds") + .groupBy(window($"eventTime", "5 seconds") as 'window) + .agg(count("*") as 'count) + .withColumn("windowEnd", expr("window.end")) + + // dropDuplicates from aggStream without event time column for dropDuplicates - the + // state does not get trimmed due to watermark advancement. + val dedupNoEventTime = aggStream + .dropDuplicates("count", "windowEnd") + .select( + $"windowEnd".cast("long").as[Long], + $"count".as[Long]) + + testStream(dedupNoEventTime)( + AddData(inputData, 1, 5, 10, 15), + CheckNewAnswer((5, 1), (10, 1), (15, 1)), Review Comment: Shall we add the same level of trace comment here for W1 and W2 like we did for above test cases? -- This is an automated message from the Apache Git Service. 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