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Github user viirya commented on the issue:
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> Compared to the error values from the test failures above, they match up
until the 10th batch but then these continue until the 16th where it has a
timeout
I suspect that might because as
Github user HeartSaVioR commented on the issue:
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@gaborgsomogyi
That's really huge... Could you share how you tested? Like which FS
(local/HDFS/S3/etc), directory structure, count of files... That would help me
understanding the impact an
Github user heary-cao commented on a diff in the pull request:
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--- Diff:
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@@ -167,64 +167,67 @@ private void writeSortedFile(boolean isLas
Github user maropu commented on the issue:
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In short, we need to run the tests on the three patterns below, right?
- wholeStage=true, factoryMode=CODEGEN_ONLY (default behaviour in Spark)
- wholeStage=false, factoryMode=CODEGEN_ONLY
Github user rxin commented on the issue:
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@xuanyuanking can you separate the prs to rename read side metric and the
write side change?
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@@ -38,12 +38,18 @@ case class CollectLimitExec(limit: Int, child:
SparkPlan)
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retest this please
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Thanks @vanzin I updated the title
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I think so, don't know if @cloud-fan or @mgaido91 has other opinions?
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Sp
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The current post hook is not enough for the use case you assume?
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Looks some classes, for instance, `KafkaUtils` exposes this (I guess
mistakenly?). Let's don't bother this and close this PR.
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Github user xuanyuanking commented on the issue:
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```
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.
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Github user maropu commented on the issue:
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Anyway, if we can accept the additional test time, I think it is the best
to run the tests on all the 4 patterns above for strict checks.
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--- Diff: python/pyspark/sql/tests/test_pandas_udf_window.py ---
@@ -87,8 +96,34 @@ def ordered_window(self):
def unpartitioned_window(self):
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--- Diff: python/pyspark/sql/tests/test_pandas_udf_window.py ---
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foo_udf = pandas_udf
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def pandas_sc
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Github user Ngone51 commented on the issue:
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> I mean if node blacklisting in Spark would be perfectly aligned to YARN
then it would be just redundant to have it in Spark in the first place.
This change seems result in *perfectly* ali
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> Are you seeing actual issues with this blacklisting when it shouldn't?
Unfortunately, no. @tgravescs @squito
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> it looks like its only going to blacklist the node for the AM, not other
nodes for general containers.
@squito Yarn have blacklist for AM when config
`am-scheduling.node-blacklisting-enab
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@HyukjinKwon Close this PR. Thank you!
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@kiszk Can you close this?
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SPARK-26285: accumulator metrics sources for LongAccumulator and Doubâ¦
â¦leAccumulator
## What changes were proposed in this pull request?
This PR implements metric sources for
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@maropu, Thanks for your question. Yes. Thats correct.
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What's a concrete example? IMHO the current proposed API is some
complicated/cumbersome to users and I feel its error-prone.
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Today in Spark, the extension points API `injectOptimizerRule` method
allows the rules to be injected at the end in
`extendedOperatorOptimizationRules` and this becomes 2 batches separated by
remo
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retest this please
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