Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/20490#discussion_r166374405
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/WriteToDataSourceV2.scala
---
@@ -117,20 +118,43 @@ object DataWritingSparkTask extends Logging {
writeTask: DataWriterFactory[InternalRow],
context: TaskContext,
iter: Iterator[InternalRow]): WriterCommitMessage = {
- val dataWriter = writeTask.createDataWriter(context.partitionId(),
context.attemptNumber())
+ val stageId = context.stageId()
+ val partId = context.partitionId()
+ val attemptId = context.attemptNumber()
+ val dataWriter = writeTask.createDataWriter(partId, attemptId)
// write the data and commit this writer.
Utils.tryWithSafeFinallyAndFailureCallbacks(block = {
iter.foreach(dataWriter.write)
- logInfo(s"Writer for partition ${context.partitionId()} is
committing.")
- val msg = dataWriter.commit()
- logInfo(s"Writer for partition ${context.partitionId()} committed.")
+
+ val msg = if (writeTask.useCommitCoordinator) {
+ val coordinator = SparkEnv.get.outputCommitCoordinator
--- End diff --
Yea it makes sense to use a commit coordinator by default, but I think we
need to carefully design the API to introduce the concept of commit
coordinator, just a `boolean useCommitCoordinator()` seems not enough. We also
need to update the documentation of the write APIs, to clearly specify in which
phase the commit coordinator is involved and how it works.
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