jingz-db commented on code in PR #49488: URL: https://github.com/apache/spark/pull/49488#discussion_r1947213556
########## sql/connect/client/jvm/src/test/scala/org/apache/spark/sql/connect/streaming/TransformWithStateConnectSuite.scala: ########## @@ -0,0 +1,489 @@ +/* + * 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.connect.streaming + +import java.io.{BufferedWriter, FileWriter} +import java.nio.file.{Files, Paths} +import java.sql.Timestamp + +import org.scalatest.concurrent.Eventually.eventually +import org.scalatest.concurrent.Futures.timeout +import org.scalatest.time.SpanSugar._ + +import org.apache.spark.internal.Logging +import org.apache.spark.sql.{DataFrame, Dataset, Encoders, Row} +import org.apache.spark.sql.connect.SparkSession +import org.apache.spark.sql.connect.test.{QueryTest, RemoteSparkSession} +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.streaming.{ListState, MapState, OutputMode, StatefulProcessor, StatefulProcessorWithInitialState, TimeMode, TimerValues, TTLConfig, ValueState} +import org.apache.spark.sql.types._ + +case class InputRowForConnectTest(key: String, value: String) +case class OutputRowForConnectTest(key: String, value: String) +case class StateRowForConnectTest(count: Long) + +// A basic stateful processor which will return the occurrences of key +class BasicCountStatefulProcessor + extends StatefulProcessor[String, InputRowForConnectTest, OutputRowForConnectTest] + with Logging { + @transient protected var _countState: ValueState[StateRowForConnectTest] = _ + + override def init(outputMode: OutputMode, timeMode: TimeMode): Unit = { + _countState = getHandle.getValueState[StateRowForConnectTest]( + "countState", + Encoders.product[StateRowForConnectTest], + TTLConfig.NONE) + } + + override def handleInputRows( + key: String, + inputRows: Iterator[InputRowForConnectTest], + timerValues: TimerValues): Iterator[OutputRowForConnectTest] = { + val count = + _countState.getOption().getOrElse(StateRowForConnectTest(0L)).count + inputRows.toSeq.length + _countState.update(StateRowForConnectTest(count)) + Iterator(OutputRowForConnectTest(key, count.toString)) + } +} + +// A stateful processor with initial state which will return the occurrences of key +class TestInitialStatefulProcessor + extends StatefulProcessorWithInitialState[ + String, + (String, String), + (String, String), + (String, String, String)] + with Logging { + @transient protected var _countState: ValueState[Long] = _ + + override def init(outputMode: OutputMode, timeMode: TimeMode): Unit = { + _countState = getHandle.getValueState[Long]("countState", Encoders.scalaLong, TTLConfig.NONE) + } + + override def handleInputRows( + key: String, + inputRows: Iterator[(String, String)], + timerValues: TimerValues): Iterator[(String, String)] = { + val count = _countState.getOption().getOrElse(0L) + inputRows.toSeq.length + _countState.update(count) + Iterator((key, count.toString)) + } + + override def handleInitialState( + key: String, + initialState: (String, String, String), + timerValues: TimerValues): Unit = { + val count = _countState.getOption().getOrElse(0L) + 1 + _countState.update(count) + } +} + +case class OutputEventTimeRow(key: String, outputTimestamp: Timestamp) + +// A stateful processor which will return timestamp of the first item from input rows +class ChainingOfOpsStatefulProcessor + extends StatefulProcessor[String, (String, Timestamp), OutputEventTimeRow] { + override def init(outputMode: OutputMode, timeMode: TimeMode): Unit = {} + + override def handleInputRows( + key: String, + inputRows: Iterator[(String, Timestamp)], + timerValues: TimerValues): Iterator[OutputEventTimeRow] = { + val timestamp = inputRows.next()._2 + Iterator(OutputEventTimeRow(key, timestamp)) + } +} + +// A basic stateful processor contains composite state variables and TTL +class TTLTestStatefulProcessor + extends StatefulProcessor[String, (String, String), (String, String)] { + import java.time.Duration + + @transient protected var countState: ValueState[Int] = _ + @transient protected var ttlCountState: ValueState[Int] = _ + @transient protected var ttlListState: ListState[Int] = _ + @transient protected var ttlMapState: MapState[String, Int] = _ + override def init(outputMode: OutputMode, timeMode: TimeMode): Unit = { + countState = getHandle.getValueState[Int]("countState", Encoders.scalaInt, TTLConfig.NONE) + ttlCountState = getHandle + .getValueState[Int]("ttlCountState", Encoders.scalaInt, TTLConfig(Duration.ofMillis(1000))) + ttlListState = getHandle + .getListState[Int]("ttlListState", Encoders.scalaInt, TTLConfig(Duration.ofMillis(1000))) + ttlMapState = getHandle.getMapState[String, Int]( + "ttlMapState", + Encoders.STRING, + Encoders.scalaInt, + TTLConfig(Duration.ofMillis(1000))) + } + override def handleInputRows( + key: String, + inputRows: Iterator[(String, String)], + timerValues: TimerValues): Iterator[(String, String)] = { + var count = 0 + var ttlCount = 0 + var ttlListStateCount = 0 + var ttlMapStateCount = 0 + if (countState.exists()) { + count = countState.get() + } + if (ttlCountState.exists()) { + ttlCount = ttlCountState.get() + } + if (ttlListState.exists()) { + for (value <- ttlListState.get()) { + ttlListStateCount += value + } + } + if (ttlMapState.exists()) { + ttlMapStateCount = ttlMapState.getValue(key) + } + for (_ <- inputRows) { + count += 1 + ttlCount += 1 + ttlListStateCount += 1 + ttlMapStateCount += 1 + } + countState.update(count) + if (key != "0") { + ttlCountState.update(ttlCount) + ttlListState.put(Array(ttlListStateCount, ttlListStateCount)) + ttlMapState.updateValue(key, ttlMapStateCount) + } + val output = List( + (s"count-$key", count.toString), + (s"ttlCount-$key", ttlCount.toString), + (s"ttlListState-$key", ttlListStateCount.toString), + (s"ttlMapState-$key", ttlMapStateCount.toString)) + output.iterator + } +} + +class TransformWithStateConnectSuite extends QueryTest with RemoteSparkSession with Logging { + val testData: Seq[(String, String)] = Seq(("a", "1"), ("b", "1"), ("a", "2")) + val twsAdditionalSQLConf = Seq( + "spark.sql.streaming.stateStore.providerClass" -> + "org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider", + "spark.sql.shuffle.partitions" -> "5", + "spark.sql.session.timeZone" -> "UTC", + "spark.sql.streaming.noDataMicroBatches.enabled" -> "false") + + test("transformWithState - streaming with state variable, case class type") { + withSQLConf(twsAdditionalSQLConf: _*) { + val session: SparkSession = spark + import session.implicits._ + + spark.sql("DROP TABLE IF EXISTS my_sink") + + withTempPath { dir => + val path = dir.getCanonicalPath + testData + .toDS() + .toDF("key", "value") + .repartition(3) + .write + .parquet(path) + + val testSchema = + StructType(Array(StructField("key", StringType), StructField("value", StringType))) + + val q = spark.readStream + .schema(testSchema) + .option("maxFilesPerTrigger", 1) + .parquet(path) + .as[InputRowForConnectTest] + .groupByKey(x => x.key) + .transformWithState[OutputRowForConnectTest]( + new BasicCountStatefulProcessor(), + TimeMode.None(), + OutputMode.Update()) + .writeStream + .format("memory") + .queryName("my_sink") + .start() + + try { + q.processAllAvailable() + eventually(timeout(30.seconds)) { + checkDataset( + spark.table("my_sink").toDF().as[(String, String)].orderBy("key"), + ("a", "1"), + ("a", "2"), + ("b", "1")) + } + } finally { + q.stop() + spark.sql("DROP TABLE IF EXISTS my_sink") + } + } + } + } + + test("transformWithState - streaming with initial state") { + withSQLConf(twsAdditionalSQLConf: _*) { + val session: SparkSession = spark + import session.implicits._ + + spark.sql("DROP TABLE IF EXISTS my_sink") + + withTempPath { dir => + val path = dir.getCanonicalPath + testData + .toDS() + .toDF("key", "value") + .repartition(3) + .write + .parquet(path) + + val testSchema = + StructType(Array(StructField("key", StringType), StructField("value", StringType))) + + val initDf = Seq(("init_1", "40.0", "a"), ("init_2", "100.0", "b")) + .toDS() + .groupByKey(x => x._3) + .mapValues(x => x) + + val q = spark.readStream + .schema(testSchema) + .option("maxFilesPerTrigger", 1) + .parquet(path) + .as[(String, String)] + .groupByKey(x => x._1) + .transformWithState( + new TestInitialStatefulProcessor(), + TimeMode.None(), + OutputMode.Update(), + initialState = initDf) + .writeStream + .format("memory") + .queryName("my_sink") + .start() + + try { + q.processAllAvailable() + eventually(timeout(30.seconds)) { + checkDataset( + spark.table("my_sink").toDF().as[(String, String)].orderBy("_1"), + ("a", "2"), + ("a", "3"), + ("b", "2")) + } + } finally { + q.stop() + spark.sql("DROP TABLE IF EXISTS my_sink") + } + } + } + } + + test("transformWithState - streaming with chaining of operators") { + withSQLConf(twsAdditionalSQLConf: _*) { + val session: SparkSession = spark + import session.implicits._ + + def timestamp(num: Int): Timestamp = { + new Timestamp(num * 1000) + } + + val checkResultFunc: (Dataset[Row], Long) => Unit = { (batchDF, batchId) => + val realDf = batchDF.orderBy("outputTimestamp").collect().toSet + if (batchId == 0) { Review Comment: haha this one is the scala translation of python chaining suite (as python "unit test" is actually integration test so they are easily used for connect suites as well) so no extra efforts is needed for understanding how this works! The original python suite is here: https://github.com/apache/spark/blob/master/python/pyspark/sql/tests/pandas/test_pandas_transform_with_state.py#L829 -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
