milanisvet commented on code in PR #49571:
URL: https://github.com/apache/spark/pull/49571#discussion_r1930771259
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sql/core/src/main/scala/org/apache/spark/sql/execution/basicPhysicalOperators.scala:
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@@ -714,6 +717,129 @@ case class UnionExec(children: Seq[SparkPlan]) extends
SparkPlan {
copy(children = newChildren)
}
+/**
+ * The physical node for recursion. Currently only UNION ALL case is supported.
+ * 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.
+ *
+ * @param loopId The id of the loop.
+ * @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.
+ * @param output The output attributes of this loop.
+ * @param limit In case we have a plan with the limit node, it is pushed down
to UnionLoop and then
+ * transferred to UnionLoopExec, to stop the recursion after
specific amount of rows
+ * is generated.
+ * 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 {
+
+ // We store the initial and generated children of the loop in this buffer.
+ // Please note that all elements are cached because of performance reasons
as they are needed for
+ // next iteration.
+ @transient private val unionDFs = mutable.ArrayBuffer.empty[DataFrame]
+
+ override def innerChildren: Seq[QueryPlan[_]] = Seq(anchor, recursion)
+
+ override lazy val metrics = Map(
+ "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output
rows"))
+
+ /**
+ * 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 limit node above the plan and
execute
+ // the newly created plan.
+ val limitedPlan = Limit(Literal(currentLimit), plan)
Review Comment:
Changed back to Limit with other changes, another comments on limit below
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