Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/15307#discussion_r83082993
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamExecution.scala
---
@@ -516,12 +568,127 @@ class StreamExecution(
""".stripMargin
}
- private def toInfo: StreamingQueryInfo = {
- new StreamingQueryInfo(
- this.name,
- this.id,
- this.sourceStatuses,
- this.sinkStatus)
+ /**
+ * Report row metrics of the executed trigger
+ * @param triggerExecutionPlan Execution plan of the trigger
+ * @param triggerLogicalPlan Logical plan of the trigger, generated from
the query logical plan
+ * @param sourceToDF Source to DataFrame returned by the source.getBatch
in this trigger
+ */
+ private def reportNumRows(
+ triggerExecutionPlan: SparkPlan,
+ triggerLogicalPlan: LogicalPlan,
+ sourceToDF: Map[Source, DataFrame]): Unit = {
+ // 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 with 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 = sourceToDF.flatMap { case (source, df) =>
+ df.logicalPlan.collectLeaves().map { leaf => leaf -> source }
+ }
+ val allLogicalPlanLeaves = triggerLogicalPlan.collectLeaves() //
includes non-streaming sources
+ val allExecPlanLeaves = triggerExecutionPlan.collectLeaves()
+ val sourceToNumInputRows: Map[Source, Long] =
+ 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 {
+ if (!metricWarningLogged) {
+ 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" +
+ s" of the execution plan:\n" +
+ s"logical plan leaves: ${toString(allLogicalPlanLeaves)}\n" +
+ s"execution plan leaves: ${toString(allExecPlanLeaves)}\n")
+ metricWarningLogged = true
+ }
+ Map.empty
+ }
+ val numOutputRows =
triggerExecutionPlan.metrics.get("numOutputRows").map(_.value)
+ val stateNodes = triggerExecutionPlan.collect {
+ case p if p.isInstanceOf[StateStoreSaveExec] => p
+ }
+
+ streamMetrics.reportNumInputRows(sourceToNumInputRows)
+ stateNodes.zipWithIndex.foreach { case (s, i) =>
+ streamMetrics.reportTriggerStatus(
+ NUM_TOTAL_STATE_ROWS(i + 1),
+ s.metrics.get("numTotalStateRows").map(_.value).getOrElse(0L))
+ streamMetrics.reportTriggerStatus(
+ NUM_UPDATED_STATE_ROWS(i + 1),
+ s.metrics.get("numUpdatedStateRows").map(_.value).getOrElse(0L))
+ }
+ updateStatus()
+ }
+
+ private def reportTimeTaken[T](triggerStatusKey: String)(body: => T): T
= {
+ val startTime = triggerClock.getTimeMillis()
+ val result = body
+ val endTime = triggerClock.getTimeMillis()
+ streamMetrics.reportTriggerStatus(triggerStatusKey, math.max(endTime -
startTime, 0))
+ updateStatus()
+ result
+ }
+
+ private def reportTimeTaken[T](source: Source, triggerStatusKey:
String)(body: => T): T = {
+ val startTime = triggerClock.getTimeMillis()
+ val result = body
+ val endTime = triggerClock.getTimeMillis()
+ streamMetrics.reportSourceTriggerStatus(
+ source, triggerStatusKey, math.max(endTime - startTime, 0))
+ updateStatus()
+ result
+ }
+
+ private def reportTimestamp(triggerStatusKey: String): Unit = {
+ streamMetrics.reportTriggerStatus(triggerStatusKey,
triggerClock.getTimeMillis)
+ updateStatus()
+ }
--- End diff --
What decides when these utility functions are defined here vs in the
`StreamMetrics` class?
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