Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/15307#discussion_r81875491
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamExecution.scala
---
@@ -525,8 +645,62 @@ class StreamExecution(
case object TERMINATED extends State
}
-object StreamExecution {
+object StreamExecution extends Logging {
private val _nextId = new AtomicLong(0)
+ /**
+ * Get the number of input rows from the executed plan of the trigger
+ * @param triggerExecutionPlan Execution plan of the trigger
+ * @param triggerLogicalPlan Logical plan of the trigger, generated from
the query logical plan
+ * @param sourceToDataframe Source to DataFrame returned by the
source.getBatch in this trigger
+ */
+ def getNumInputRowsFromTrigger(
+ triggerExecutionPlan: SparkPlan,
+ triggerLogicalPlan: LogicalPlan,
+ sourceToDataframe: Map[Source, DataFrame]): Map[Source, Long] = {
+
+ // We want to associate execution plan leaves to sources that generate
them, so that we match
+ // the their metrics (e.g. numOutputRows) to the sources. To do this
we do the following.
+ // Consider the translation from the streaming logical plan to the
final executed plan.
+ //
+ // streaming logical plan (with sources) <==> trigger's logical plan
<==> executed plan
+ //
+ // 1. We keep track of streaming sources associated with each leaf in
the trigger's logical plan
+ // - Each logical plan leaf will be associated with a single
streaming source.
+ // - There can be multiple logical plan leaves associated a
streaming source.
+ // - There can be leaves not associated with any streaming source,
because they were
+ // generated from a batch source (e.g. stream-batch joins)
+ //
+ // 2. Assuming that the executed plan has same number of leaves in the
same order as that of
+ // the trigger logical plan, we associate executed plan leaves with
corresponding
+ // streaming sources.
+ //
+ // 3. For each source, we sum the metrics of the associated execution
plan leaves.
+ //
+ val logicalPlanLeafToSource = sourceToDataframe.flatMap { case
(source, df) =>
+ df.logicalPlan.collectLeaves().map { leaf => leaf -> source }
+ }
+ val allLogicalPlanLeaves = triggerLogicalPlan.collectLeaves() //
includes non-streaming sources
+ val allExecPlanLeaves = triggerExecutionPlan.collectLeaves()
+ if (allLogicalPlanLeaves.size == allExecPlanLeaves.size) {
+ val execLeafToSource =
allLogicalPlanLeaves.zip(allExecPlanLeaves).flatMap {
+ case (lp, ep) => logicalPlanLeafToSource.get(lp).map { source =>
ep -> source }
+ }
+ val sourceToNumInputRows = execLeafToSource.map { case (execLeaf,
source) =>
+ val numRows =
execLeaf.metrics.get("numOutputRows").map(_.value).getOrElse(0L)
+ source -> numRows
+ }
+ sourceToNumInputRows.groupBy(_._1).mapValues(_.map(_._2).sum) // sum
up rows for each source
+ } else {
+ def toString[T](seq: Seq[T]): String = s"(size = ${seq.size}),
${seq.mkString(", ")}"
+ logWarning(
+ "Could not report metrics as number leaves in trigger logical plan
did not match that" +
--- End diff --
Seems this is going to flood the logs if its ever triggered.
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