anishshri-db commented on code in PR #49488:
URL: https://github.com/apache/spark/pull/49488#discussion_r1924447293
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala:
##########
@@ -525,6 +551,71 @@ private class KeyValueGroupedDatasetImpl[K, V, IK, IV](
}
}
+ override protected[sql] def transformWithStateHelper[U: Encoder, S: Encoder](
+ statefulProcessor: StatefulProcessor[K, V, U],
+ timeMode: TimeMode,
+ outputMode: OutputMode,
+ initialState: Option[KeyValueGroupedDataset[K, S]] = None,
+ eventTimeColumnName: String = ""): Dataset[U] = {
+ val outputEncoder = agnosticEncoderFor[U]
+ val stateEncoder = agnosticEncoderFor[S]
+
+ val inputEncoders: Seq[AgnosticEncoder[_]] = Seq(kEncoder, stateEncoder,
ivEncoder)
+ val dummyGroupingFunc = SparkUserDefinedFunction(
+ function = UdfUtils.noOp[K, U](),
+ inputEncoders = inputEncoders,
+ outputEncoder = outputEncoder)
+ val udf = dummyGroupingFunc
+ .apply(inputEncoders.map(_ => col("*")): _*)
+ .expr
+ .getCommonInlineUserDefinedFunction
+
+ val initialStateImpl = if (initialState.isDefined) {
+ assert(initialState.get.isInstanceOf[KeyValueGroupedDatasetImpl[K, S, _,
_]])
+ initialState.get.asInstanceOf[KeyValueGroupedDatasetImpl[K, S, _, _]]
+ } else {
+ null
+ }
+ val statefulProcessorStr = if (!initialState.isDefined) {
Review Comment:
nit: pls add newline here
##########
connector/connect/client/jvm/src/test/scala/org/apache/spark/sql/streaming/TransformWithStateConnectSuite.scala:
##########
@@ -0,0 +1,347 @@
+/*
+ * 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 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, SparkSession}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.{QueryTest, RemoteSparkSession}
+import org.apache.spark.sql.types._
+
+// A basic stateful processor which will return the occurrences of key
+class BasicCountStatefulProcessor
+ extends StatefulProcessor[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))
+ }
+}
+
+// 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))
+ }
+}
+
+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")
+
+ test("transformWithState - streaming with state variable") {
Review Comment:
Are we covering all composite types ? Also, are we covering TTL ? Also -
cases where the state is written using `case class` to make sure that the serde
is working as expected ?
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