Github user rdblue commented on a diff in the pull request:
https://github.com/apache/spark/pull/17540#discussion_r113225697
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileFormatWriter.scala
---
@@ -161,50 +161,51 @@ object FileFormatWriter extends Logging {
}
}
- SQLExecution.withNewExecutionId(sparkSession, queryExecution) {
- // This call shouldn't be put into the `try` block below because it
only initializes and
- // prepares the job, any exception thrown from here shouldn't cause
abortJob() to be called.
- committer.setupJob(job)
-
- try {
- val rdd = if (orderingMatched) {
- queryExecution.toRdd
- } else {
- SortExec(
- requiredOrdering.map(SortOrder(_, Ascending)),
- global = false,
- child = queryExecution.executedPlan).execute()
- }
- val ret = new Array[WriteTaskResult](rdd.partitions.length)
- sparkSession.sparkContext.runJob(
- rdd,
- (taskContext: TaskContext, iter: Iterator[InternalRow]) => {
- executeTask(
- description = description,
- sparkStageId = taskContext.stageId(),
- sparkPartitionId = taskContext.partitionId(),
- sparkAttemptNumber = taskContext.attemptNumber(),
- committer,
- iterator = iter)
- },
- 0 until rdd.partitions.length,
- (index, res: WriteTaskResult) => {
- committer.onTaskCommit(res.commitMsg)
- ret(index) = res
- })
-
- val commitMsgs = ret.map(_.commitMsg)
- val updatedPartitions = ret.flatMap(_.updatedPartitions)
- .distinct.map(PartitioningUtils.parsePathFragment)
-
- committer.commitJob(job, commitMsgs)
- logInfo(s"Job ${job.getJobID} committed.")
- refreshFunction(updatedPartitions)
- } catch { case cause: Throwable =>
- logError(s"Aborting job ${job.getJobID}.", cause)
- committer.abortJob(job)
- throw new SparkException("Job aborted.", cause)
+ // During tests, make sure there is an execution ID.
+ SQLExecution.checkSQLExecutionId(sparkSession)
--- End diff --
@zsxwing, that and similar cases are what I was talking about earlier when
I said there are two physical plans. The inner `Dataset.ofRows` ends up
creating a completely separate plan.
Are you saying that adding `SparkListenerSQLExecutionStart` (and also end)
events will fix the metrics problem? I think it would at least require the
metrics work-around I added to `SQLListener`, since metrics are filtered out if
they aren't reported by the physical plan.
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