[GitHub] [spark] xuanyuanking commented on a change in pull request #31570: [SPARK-10816][SS] SessionWindow support for Structure Streaming

[GitBox <gi...@apache.org>]

xuanyuanking commented on a change in pull request #31570:
URL: https://github.com/apache/spark/pull/31570#discussion_r596491649



##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/StateStoreHandler.scala
##########
@@ -0,0 +1,65 @@
+/*
+ * 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.execution.streaming.state
+
+import org.apache.hadoop.conf.Configuration
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.execution.streaming.StatefulOperatorStateInfo
+import org.apache.spark.sql.types.StructType
+
+trait StateStoreType
+
+/** Helper trait for invoking common functionalities of a state store. */
+abstract class StateStoreHandler extends Logging {

Review comment:
       It would be great if we can describe a little more how we reuse the original streaming join `StateStoreHandler`. Seems it's an important implementation detail.

##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/AggUtils.scala
##########
@@ -262,6 +262,17 @@ object AggUtils {
    *  - PartialMerge (now there is at most 1 tuple per group)
    *  - StateStoreSave (saves the tuple for the next batch)
    *  - Complete (output the current result of the aggregation)
+   *
+   * Plans a streaming aggregation with Session Window using the following progression:
+   *  - (Shuffle + Session Window Assignment, see `SessionWindowExec`)
+   *  - Partial Aggregation (now there is at most 1 tuple per group)
+   *  - SessionStateStoreRestore (now there is 1 tuple from this batch + optionally one from

Review comment:
       Yes. If we see a bottleneck in the future. We can add the pre-shuffle PartialMerge back.

##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/SessionWindowMergeExec.scala
##########
@@ -0,0 +1,180 @@
+/*
+ * 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.execution
+
+import scala.collection.mutable
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, Distribution, Partitioning}
+
+/**
+ * The physical plan for streaming query, merge session window after restore from state store.
+ * Note: the end time of window that restore from statestore has already contain session windowGap
+ *
+ * @param windowExpressions
+ * @param sessionSpec
+ * @param child
+ */
+case class SessionWindowMergeExec(
+    windowExpressions: NamedExpression,
+    sessionSpec: Seq[Expression],
+    child: SparkPlan)
+  extends UnaryExecNode {
+
+  override def requiredChildDistribution: Seq[Distribution] = {
+    ClusteredDistribution(sessionSpec) :: Nil
+  }
+
+  // Data should be sorted, so we can merge session window directly.
+  // TODO: use this requirement for simplicity, not necessary to sort the whole dataset,
+  // try better way later.

Review comment:
       Seems we can already get rid of this ordering. Due to the session window in state store is in order.

##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/SessionWindowExec.scala
##########
@@ -0,0 +1,203 @@
+/*
+ * 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.execution
+
+import scala.collection.mutable
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection
+import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, Distribution, Partitioning}
+
+/**
+ * Used for calculating the session window start and end for each row, so this plan requires
+ * child distributed by sessionSpec and sorted by time column in each part. The value for
+ * window start is time value of the first row in this window, the value for window end is
+ * time value of the last row plus the windowGap.
+ *
+ * @param windowExpressions session window expression for the exec node.
+ * @param sessionSpec the partition key of this session window, it is the rest column of
+ *                    groupingExpr in parent aggregate node.
+ * @param windowGap window gap in micro second.
+ * @param child child plan for this node.
+ */
+case class SessionWindowExec(
+    windowExpressions: NamedExpression,
+    timeColumn: Expression,
+    sessionSpec: Seq[Expression],
+    windowGap: Long,
+    child: SparkPlan)
+  extends UnaryExecNode {
+
+  override def requiredChildDistribution: Seq[Distribution] = {
+    ClusteredDistribution(sessionSpec) :: Nil
+  }
+
+  override def requiredChildOrdering: Seq[Seq[SortOrder]] =
+    Seq(sessionSpec.map(SortOrder(_, Ascending)) :+ SortOrder(timeColumn, Ascending))
+
+  override def producedAttributes: AttributeSet = AttributeSet(windowExpressions.toAttribute)
+
+  override def output: Seq[Attribute] = child.output ++ Seq(windowExpressions.toAttribute)
+
+  override def outputPartitioning: Partitioning = child.outputPartitioning
+
+  override def outputOrdering: Seq[SortOrder] = child.outputOrdering
+
+  /**
+   * Produces the result of the query as an `RDD[InternalRow]`
+   *
+   * Overridden by concrete implementations of SparkPlan.
+   */
+  override protected def doExecute(): RDD[InternalRow] = {

Review comment:
       This is another difference with the @HeartSaVioR's. We use the pattern of SQL window function, while Jungtaek's one is a sub-class for aggregation iterator.
   Pros:
   Less code change. Don't mess up with aggregation logic.
   Cons:
   We keep the original row for a while. Might involve more memory cost.




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