cloud-fan commented on code in PR #49955:
URL: https://github.com/apache/spark/pull/49955#discussion_r1959756734
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/basicPhysicalOperators.scala:
##########
@@ -714,6 +717,177 @@ case class UnionExec(children: Seq[SparkPlan]) extends
SparkPlan {
copy(children = newChildren)
}
+/**
+ * The physical node for recursion. Currently only UNION ALL case is supported.
+ * For the details about the execution, look at the comment above doExecute
function.
+ *
+ * A simple recursive query:
+ * {{{
+ * WITH RECURSIVE t(n) AS (
+ * SELECT 1
+ * UNION ALL
+ * SELECT n+1 FROM t WHERE n < 5)
+ * SELECT * FROM t;
+ * }}}
+ * Corresponding logical plan for the recursive query above:
+ * {{{
+ * WithCTE
+ * :- CTERelationDef 0, false
+ * : +- SubqueryAlias t
+ * : +- Project [1#0 AS n#3]
+ * : +- UnionLoop 0
+ * : :- Project [1 AS 1#0]
+ * : : +- OneRowRelation
+ * : +- Project [(n#1 + 1) AS (n + 1)#2]
+ * : +- Filter (n#1 < 5)
+ * : +- SubqueryAlias t
+ * : +- Project [1#0 AS n#1]
+ * : +- UnionLoopRef 0, [1#0], false
+ * +- Project [n#3]
+ * +- SubqueryAlias t
+ * +- CTERelationRef 0, true, [n#3], false, false
+ * }}}
+ *
+ * @param loopId This is id of the CTERelationDef containing the recursive
query. Its value is
+ * first passed down to UnionLoop when creating it, and then to
UnionLoopExec in
+ * SparkStrategies.
+ * @param anchor The logical plan of the initial element of the loop.
+ * @param recursion The logical plan that describes the recursion with an
[[UnionLoopRef]] node.
+ * CTERelationRef, which is marked as recursive, gets
substituted with
+ * [[UnionLoopRef]] in ResolveWithCTE.
+ * Both anchor and recursion are marked with @transient
annotation, so that they
+ * are not serialized.
+ * @param output The output attributes of this loop.
+ * @param limit If defined, the total number of rows output by this operator
will be bounded by
+ * limit.
+ * Its value is pushed down to UnionLoop in Optimizer in case
Limit node is present
+ * in the logical plan and then transferred to UnionLoopExec in
SparkStrategies.
+ * Note here: limit can be applied in the main query calling the
recursive CTE, and not
+ * inside the recursive term of recursive CTE.
+ */
+case class UnionLoopExec(
+ loopId: Long,
+ @transient anchor: LogicalPlan,
+ @transient recursion: LogicalPlan,
+ override val output: Seq[Attribute],
+ limit: Option[Int] = None) extends LeafExecNode {
+
+ override def innerChildren: Seq[QueryPlan[_]] = Seq(anchor, recursion)
+
+ override lazy val metrics = Map(
+ "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output
rows"),
+ "numRecursiveLoops" -> SQLMetrics.createMetric(sparkContext, "number of
recursive loops"))
+
+ /**
+ * This function executes the plan (optionally with appended limit node) and
caches the result,
+ * with the caching mode specified in config.
+ */
+ private def executeAndCacheAndCount(
+ plan: LogicalPlan, currentLimit: Int) = {
+ // In case limit is defined, we create a (global) limit node above the
plan and execute
+ // the newly created plan.
+ // Note here: global limit requires coordination (shuffle) between
partitions.
+ val planOrLimitedPlan = if (limit.isDefined) {
+ Limit(Literal(currentLimit), plan)
+ } else {
+ plan
+ }
+ val df = Dataset.ofRows(session, planOrLimitedPlan)
+ val cachedDF = df.repartition()
+ val count = cachedDF.count()
+ (cachedDF, count)
+ }
+
+ /**
+ * In the first iteration, anchor term is executed.
+ * Then, in each following iteration, the UnionLoopRef node is substituted
with the plan from the
+ * previous iteration, and such plan is executed.
+ * After every iteration, the dataframe is repartitioned.
+ * The recursion stops when the generated dataframe is empty, or either the
limit or
+ * the specified maximum depth from the config is reached.
+ */
+ override protected def doExecute(): RDD[InternalRow] = {
+ val executionId =
sparkContext.getLocalProperty(SQLExecution.EXECUTION_ID_KEY)
+ val numOutputRows = longMetric("numOutputRows")
+ val numRecursiveLoops = longMetric("numRecursiveLoops")
+ val levelLimit = conf.getConf(SQLConf.CTE_RECURSION_LEVEL_LIMIT)
+
+ // currentLimit is initialized from the limit argument, and in each step
it is decreased by
+ // the number of rows generated in that step.
+ // If limit is not passed down, currentLimit is set to be zero and won't
be considered in the
+ // condition of while loop down (limit.isEmpty will be true).
+ var currentLimit = limit.getOrElse(0)
+ val unionChildren = mutable.ArrayBuffer.empty[LogicalRDD]
+
+ var (prevDF, prevCount) = executeAndCacheAndCount(anchor, currentLimit)
+
+ var currentLevel = 1
+
+ // Main loop for obtaining the result of the recursive query.
+ while (prevCount > 0 && (limit.isEmpty || currentLimit > 0)) {
Review Comment:
```suggestion
while (prevCount > 0 && currentLimit > 0) {
```
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/basicPhysicalOperators.scala:
##########
@@ -714,6 +717,177 @@ case class UnionExec(children: Seq[SparkPlan]) extends
SparkPlan {
copy(children = newChildren)
}
+/**
+ * The physical node for recursion. Currently only UNION ALL case is supported.
+ * For the details about the execution, look at the comment above doExecute
function.
+ *
+ * A simple recursive query:
+ * {{{
+ * WITH RECURSIVE t(n) AS (
+ * SELECT 1
+ * UNION ALL
+ * SELECT n+1 FROM t WHERE n < 5)
+ * SELECT * FROM t;
+ * }}}
+ * Corresponding logical plan for the recursive query above:
+ * {{{
+ * WithCTE
+ * :- CTERelationDef 0, false
+ * : +- SubqueryAlias t
+ * : +- Project [1#0 AS n#3]
+ * : +- UnionLoop 0
+ * : :- Project [1 AS 1#0]
+ * : : +- OneRowRelation
+ * : +- Project [(n#1 + 1) AS (n + 1)#2]
+ * : +- Filter (n#1 < 5)
+ * : +- SubqueryAlias t
+ * : +- Project [1#0 AS n#1]
+ * : +- UnionLoopRef 0, [1#0], false
+ * +- Project [n#3]
+ * +- SubqueryAlias t
+ * +- CTERelationRef 0, true, [n#3], false, false
+ * }}}
+ *
+ * @param loopId This is id of the CTERelationDef containing the recursive
query. Its value is
+ * first passed down to UnionLoop when creating it, and then to
UnionLoopExec in
+ * SparkStrategies.
+ * @param anchor The logical plan of the initial element of the loop.
+ * @param recursion The logical plan that describes the recursion with an
[[UnionLoopRef]] node.
+ * CTERelationRef, which is marked as recursive, gets
substituted with
+ * [[UnionLoopRef]] in ResolveWithCTE.
+ * Both anchor and recursion are marked with @transient
annotation, so that they
+ * are not serialized.
+ * @param output The output attributes of this loop.
+ * @param limit If defined, the total number of rows output by this operator
will be bounded by
+ * limit.
+ * Its value is pushed down to UnionLoop in Optimizer in case
Limit node is present
+ * in the logical plan and then transferred to UnionLoopExec in
SparkStrategies.
+ * Note here: limit can be applied in the main query calling the
recursive CTE, and not
+ * inside the recursive term of recursive CTE.
+ */
+case class UnionLoopExec(
+ loopId: Long,
+ @transient anchor: LogicalPlan,
+ @transient recursion: LogicalPlan,
+ override val output: Seq[Attribute],
+ limit: Option[Int] = None) extends LeafExecNode {
+
+ override def innerChildren: Seq[QueryPlan[_]] = Seq(anchor, recursion)
+
+ override lazy val metrics = Map(
+ "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output
rows"),
+ "numRecursiveLoops" -> SQLMetrics.createMetric(sparkContext, "number of
recursive loops"))
+
+ /**
+ * This function executes the plan (optionally with appended limit node) and
caches the result,
+ * with the caching mode specified in config.
+ */
+ private def executeAndCacheAndCount(
+ plan: LogicalPlan, currentLimit: Int) = {
+ // In case limit is defined, we create a (global) limit node above the
plan and execute
+ // the newly created plan.
+ // Note here: global limit requires coordination (shuffle) between
partitions.
+ val planOrLimitedPlan = if (limit.isDefined) {
Review Comment:
maybe `currentLimit > 0`?
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