HeartSaVioR commented on a change in pull request #26218: [SPARK-29562][sql]
Speed up and slim down metric aggregation in SQL listener.
URL: https://github.com/apache/spark/pull/26218#discussion_r337908742
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLAppStatusListener.scala
##########
@@ -425,12 +432,72 @@ private class LiveExecutionData(val executionId: Long)
extends LiveEntity {
}
private class LiveStageMetrics(
- val stageId: Int,
- var attemptId: Int,
- val accumulatorIds: Set[Long],
- val taskMetrics: ConcurrentHashMap[Long, LiveTaskMetrics])
-
-private class LiveTaskMetrics(
- val ids: Array[Long],
- val values: Array[Long],
- val succeeded: Boolean)
+ val attemptId: Int,
+ val numTasks: Int,
+ val accumulatorIds: Set[Long]) {
+
+ /**
+ * Mapping of task IDs to the partition index they're computing. Note this
may contain more
+ * elements than the stage's number of tasks, if speculative execution is on.
+ */
+ private val taskIndices = new OpenHashMap[Long, Int]()
+
+ /** Bit set tracking which partition indices have been successfully
computed. */
+ private val completedParts = new mutable.BitSet()
+
+ /**
+ * Task metrics values for the stage. Maps the metric ID to the metric
values for each
+ * partition. For each metric ID, there will be the same number of values as
the number
+ * of partitions. This relies on `SQLMetrics.stringValue` treating 0 as a
neutral value,
+ * independent of the actual metric type.
+ */
+ private val taskMetrics = new ConcurrentHashMap[Long, Array[Long]]()
+
+ def registerTask(taskId: Long, partIdx: Int): Unit = {
+ taskIndices.update(taskId, partIdx)
+ }
+
+ def updateTaskMetrics(
+ taskId: Long,
+ eventPartIdx: Int,
+ finished: Boolean,
+ accumUpdates: Seq[AccumulableInfo]): Unit = {
+ val partIdx = if (eventPartIdx == -1) {
Review comment:
nit: might be better to have constant for `-1` to denote unknown partition
index.
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