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I don't really have a readily available cluster that can easily put that
much pressure on the listener bus; I tried with the one I have and monitored
the listener bus metrics, and found pretty much w
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retest this please
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+/*
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lgtm
do you know how much better this makes it? Eg., if we had an existing case
where things go haywire in dynamic allocation because of this -- we could see
if after this change, the only
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+/*
+ * Licensed to the Apache Software Foundati
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You just need to fix `InsertIntoHiveTable `. It is not related to
`Insertintohadoopfsrelationcommand.scala`. In your test cases, you just need to
set `spark.sql.hive.convertMetastoreParquet` to `
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[SPARK-21998][SQL] SortMergeJoinExec did not calculate its outputOrdering
correctly during physical planning
## What changes were proposed in this pull request?
Right now the calculatio
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Table-level metadata has the highest priority. In the table-level metadata,
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specified, we need to check the session-l
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[SPARK-21928][CORE] Set classloader on SerializerManager private kryo
## What changes were proposed in this pull request?
We have to make sure thatthat SerializerManager's private instance o
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retest this please
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I'll check this out now
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@gatorsmile Thanks a lot for your help!!!
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@tdas Addressed
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The issue is the in the trait `setXXX` returns `this.type` which in Java in
the concrete class doesn't work, so the `setXXX` methods need to be implemented
in the concrete subclass. See the decision
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Github user MLnick commented on the issue:
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I'm sorry but I still don't understand the intention here. You can already
train on a Wikipedia dump (or any other dataset) by passing that dataset as the
input DataFrame to Word2Vec.
If you
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The error logs for test build #81683 state that method
this(Long,Int,Int,Long,Long,Long,Long,Long,Long)Unit in class
org.apache.spark.status.api.v1.ExecutorStageSummary does not have a
correspon
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Github user LeoIV commented on the issue:
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At the moment, it is not possible to improve a models accuracy by
incorporating additional data. I think this should be supported since it can
increase a classifiers performance significantly. With th
Github user MLnick commented on the issue:
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Hi there - I don't see the value here of adding a few words in a String
array to the training. You're effectively adding a second (non-distributed,
therefore limited in size) corpus to the training.
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Github user SparkQA commented on the issue:
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Github user yanboliang commented on a diff in the pull request:
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--- Diff: python/pyspark/ml/evaluation.py ---
@@ -328,6 +329,77 @@ def setParams(self, predictionCol="prediction",
labelCol="label",
kwargs
Github user SparkQA commented on the issue:
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Github user WeichenXu123 commented on the issue:
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Jenkins, test this please.
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Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/15544#discussion_r139713569
--- Diff:
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproxCountDistinctForIntervalsSuite.scala
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@@ -0,0 +1,206 @
Github user yanboliang commented on the issue:
https://github.com/apache/spark/pull/19156
@WeichenXu123 Sorry for late response, really busy in these days. I will
take a look in a few days. Thanks for your patience.
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Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/15544#discussion_r139713018
--- Diff:
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproxCountDistinctForIntervalsSuite.scala
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@@ -0,0 +1,206 @
Github user WeichenXu123 commented on the issue:
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@yanboliang Are you still working on this ?
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Github user cloud-fan commented on a diff in the pull request:
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--- Diff:
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