cloud-fan commented on a change in pull request #33002:
URL: https://github.com/apache/spark/pull/33002#discussion_r657610207
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File path:
core/src/main/scala/org/apache/spark/internal/io/HadoopMapReduceCommitProtocol.scala
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@@ -152,12 +153,21 @@ class HadoopMapReduceCommitProtocol(
tmpOutputPath
}
- protected def getFilename(taskContext: TaskAttemptContext, ext: String):
String = {
- // The file name looks like
part-00000-2dd664f9-d2c4-4ffe-878f-c6c70c1fb0cb_00003-c000.parquet
- // Note that %05d does not truncate the split number, so if we have more
than 100000 tasks,
+ protected def getFilename(ext: String): String = {
+ // Use the Spark task attempt ID which is unique within the write job, so
that file writes never
+ // collide if the file name also includes job ID. The Hadoop task id is
equivalent to Spark's
+ // partitionId, which is not unique within the write job, for cases like
task retry or
+ // speculative tasks.
+ // NOTE: this is not necessary for certain Hadoop output committers, as
they will create a
+ // unique staging directory for each task attempt, so we don't need to
worry about file name
+ // collision between different task attempts, and using Hadoop task
ID/Spark partition ID is
+ // also fine. For extra safety and consistency with the streaming side, we
always use the
+ // Spark task attempt ID here.
+ val taskId = TaskContext.get.taskAttemptId()
+ // The file name looks like
part-00000-2dd664f9-d2c4-4ffe-878f-c6c70c1fb0cb_00003.gz.parquet
+ // Note that %05d does not truncate the taskId, so if we have more than
100000 tasks,
// the file name is fine and won't overflow.
- val split = taskContext.getTaskAttemptID.getTaskID.getId
- f"part-$split%05d-$jobId$ext"
+ f"part-$taskId%05d-$jobId$ext"
Review comment:
Spark has: job id -> stage id -> partition ID
job id is simply a UUID
stage id is an integer starting from 0, globally unique within the spark
application
partition ID is an integer starting from 0, unique within a stage
Eash task does not only have partition ID, but also has attempt ID, which is
an integer starting from 0, globally unique within the spark application. There
is also an attempt number, which starts from 0 and increases by one for each
attempt of this task.
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