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Github user xuanyuanking commented on the issue: https://github.com/apache/spark/pull/23207 ``` the code looks much cleaner now! ``` Sorry for the original rush and code, I should and will pay more attention on coding clean and more discussion on optional implementation. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/23207 the code looks much cleaner now! --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user xuanyuanking commented on the issue: https://github.com/apache/spark/pull/23207 ``` Can we put the above in a closure and pass it into shuffle dependency? Then in SQL we just put the above in SQL using custom metrics. ``` Yea, the commit of a780b70 achieve this by adding `ShuffleWriteProcessor` abstract. And the read metrics rename reverted in 7d104eb, will do it and display change in another pr. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user rxin commented on the issue:
https://github.com/apache/spark/pull/23207
```var writer: ShuffleWriter[Any, Any] = null
try {
val manager = SparkEnv.get.shuffleManager
writer = manager.getWriter[Any, Any](
dep.shuffleHandle, partitionId, context,
context.taskMetrics().shuffleWriteMetrics)
writer.write(rdd.iterator(partition, context).asInstanceOf[Iterator[_
<: Product2[Any, Any]]])
writer.stop(success = true).get
} catch {
case e: Exception =>
try {
if (writer != null) {
writer.stop(success = false)
}
} catch {
case e: Exception =>
log.debug("Could not stop writer", e)
}
throw e
}```
Can we put the above in a closure and pass it into shuffle dependency? Then
in SQL we just put the above in SQL using custom metrics.
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Github user xuanyuanking commented on the issue: https://github.com/apache/spark/pull/23207 ``` can you separate the prs to rename read side metric and the write side change? ``` No problem, next commit will revert the changes of rename read side. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user rxin commented on the issue: https://github.com/apache/spark/pull/23207 @xuanyuanking can you separate the prs to rename read side metric and the write side change? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user xuanyuanking commented on the issue: https://github.com/apache/spark/pull/23207 Thanks for your reply Wenchen, there's a sketch doc assigned in JIRA:https://docs.google.com/document/d/1DX0gLkpk_NCE5MwI1_m4gnA2rLdjDkynZ02u2VWDR-8/edit ``` IMO shuffle write metrics is hard, as an RDD can have shuffle dependencies with multiple upstream RDDs. That said, in general the shuffle write metrics should belong to the upstream RDDs. ``` That's right and that's also what I try to do at first, logically upstream operator trigger shuffle write, and first attempt implementation is also changed SparkPlan base class to achieve this. ``` In Spark SQL, it's a little simpler, as the ShuffledRowRDD always have only one child, so it's reasonable to say that shuffle write metrics belong to ShuffledRowRDD. ``` Yes, maybe this also the suggestion by Reynold, ShuffleExchangeExec has only one child, we can do a simplify on the implementation. But as the shuffle write metrics are updated by task inner, so the core module still need some changes. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/23207 Can you share some ideas about it? IMO shuffle write metrics is hard, as an RDD can have shuffle dependencies with multiple upstream RDDs. That said, in general the shuffle write metrics should belong to the upstream RDDs. In Spark SQL, it's a little simpler, as the `ShuffledRowRDD` always have only one child, so it's reasonable to say that shuffle write metrics belong to `ShuffledRowRDD`. That said, we need to design a not-so-general shuffle write metrics API in Spark core, which will only be used in Spark SQL. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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