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Github user HyukjinKwon commented on the issue:
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retest this please
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Github user HyukjinKwon commented on the issue:
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Yea, if there are more instances found, we better fix them together while
we are here.
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Github user shaneknapp commented on the issue:
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`./build/mvn -DskipTests -Phadoop2.6 -Pyarn -Phive -Phive-thriftserver
clean package` FTW.
(i do know that the -Phadoop2.6 is superfluous, but at this point...)
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Yea if there are more instances found, we better fix them together
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ok think i got it... :)
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Github user mgaido91 commented on a diff in the pull request:
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--- Diff:
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--- Diff: core/src/main/scala/org/apache/spark/rdd/RDD.scala ---
@@ -396,7 +396,16 @@ abstract class RDD[T: ClassTag](
* Return a new RDD containing
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Github user shaneknapp commented on the issue:
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making build config changes... forgot to add `-Pyarn` to the build target,
as well as making sure the correct python env is selected before running python
tests.
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Github user viirya commented on the issue:
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> @viirya is that effort going on? I can help with the work if you want.
Thanks.
@mgaido91 Yeah, I'm still working on it. One of the PRs #21537 is still
waiting for review.
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Thank you! @hvanhovell
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there's a bunch of stuff in the unittest logs that i could use some extra
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Github user Fokko commented on the issue:
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I don't really understand the error, can a Spark expert elaborate what's
going on here?
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SGTM too.
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Github user shaneknapp commented on the issue:
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hmm first build failed but i don't quite know what's up:
`skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'`
and:
```
(py3k) jenkins@ubuntu-testing:
Github user zsxwing commented on the issue:
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I also merged to branch-2.3.
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--- Diff: core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala ---
@@ -95,6 +95,18 @@ class RDDSuite extends SparkFunSuite with
SharedSparkContext {
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--- Diff: core/src/main/scala/org/apache/spark/rdd/RDD.scala ---
@@ -396,7 +396,16 @@ abstract class RDD[T: ClassTag](
* Return a new RDD containing
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master branch, i assume
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Thanks. Merging to master. I will try to merge to old branches and report
back.
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@@ -0,0 +1,111 @@
+/*
+ * Licensed to the Apache S
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+ * Licensed to the Apache S
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Looking at Jenkins though I can see somethings which don't appear to be
timeout failures, e.g.
> Traceback (most recent call last):
> File
"/home/jenkins/workspace/SparkPullRequestBui
Github user BryanCutler commented on the issue:
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Yup, that would run all the pyarrow tests
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Github user zsxwing commented on the issue:
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> IIUC streaming query always need to specify a checkpoint location?
You can use a batch query to read and write Kafka :) My point is if the
input and output data sources are not distributed
Github user squito commented on the issue:
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Thanks for detailed analysis @vincent-grosbois . I agree with everything,
but as you noted you won't hit this particular issue anymore with
`ChunkedByteBufferFileRegion`. Is there another use-case?
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Github user shaneknapp commented on the issue:
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@BryanCutler responding to the wall 'o text:
1) we are not moving to py3.5 until after 2.4 is cut
2) my 'testing' server has 3.5 and 0.10.0 installed, so i can create a job
that
Github user tgravescs commented on the issue:
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Ok, it seems like the proposal @squito had to sort on the
binary/serialized data seems like at least a good short term solution. any
sorting is going to definitely add overhead but at least its
Github user holdenk commented on the issue:
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So if the issue was a timeout last time, we can ask "Jenkins retest this
please" and see if not now works. If that isn't working for you another way we
can trigger Jenkins retest is to merge in mast
Github user mallman commented on the issue:
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>> @mallman, while we wait for the go-no-go, do you have the changes for
the next PR ready? Is there anything you need help with?
> I have the hack I used originally, but I haven't tried fin
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Wow, thanks @shaneknapp for helping to get this worked out! I think you're
plan to move to Python 3.5 sounds great, but it does make me a bit nervous
making a change like this at a a critical t
Github user yucai commented on the issue:
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@LantaoJin I realized the initial way had some issue, so I marked it as WIP
to refine and add test. It is different from your original implementation, so I
would like to use this one.
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Github user yucai commented on the issue:
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@cloud-fan Jira and 1st is from this one. It is critical to our 2.3
migration.
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> What happened to the other pr proposal of just using the hashPartitioner?
It's not able to handle the use case when we use `repartition()` to shuffle
skewed data, so we give up on tha
Github user cloud-fan commented on the issue:
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This PR looks identical to #22067 , which one is the first PR?
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retest this please.
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GitHub user squito opened a pull request:
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[SPARK-25088][CORE][MESOS][DOCS] Update Rest Server docs & defaults.
## What changes were proposed in this pull request?
(a) disabled rest submission server by default in standalone mode
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Still catching up on this and trying to understand all the cases.
What happened to the other pr proposal of just using the hashPartitioner?
Did we give up on that because of the skewed
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Github user srowen commented on the issue:
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Merged to master/2.3/2.2
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Github user asfgit closed the pull request at:
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Github user squito commented on the issue:
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> What if the user does't provide a distributed file system path? E.g., you
can read from Kafka and write them back to Kafka and such workloads don't need
a distributed file system in standalone mode
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Github user srowen commented on the issue:
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I'm going to ignore the failures as they can't be related
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Github user mn-mikke commented on a diff in the pull request:
https://github.com/apache/spark/pull/22017#discussion_r209311502
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala
---
@@ -231,6 +231,15 @@ object TypeCoercion {
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