Github user markhamstra commented on the issue:
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It is precisely because the audience that I am concerned with is not
limited to just data scientists or notebook users and their particular needs
that I am far from convinced that exposing inter
Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/21802#discussion_r203560440
--- Diff: python/pyspark/sql/functions.py ---
@@ -2382,6 +2382,20 @@ def array_sort(col):
return Column(sc._jvm.functions.array_sort(_to_java_column(
Github user zsxwing commented on a diff in the pull request:
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--- Diff:
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---
@@ -115,7 +116,7 @@ private[kafka010] class Kafk
Github user ssimeonov commented on the issue:
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@markhamstra I am confused about your API evaluation criteria.
You are not arguing about the specific benefits these changes can provide
immediately to an increasing majority of Spark use
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GitHub user countmdm opened a pull request:
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[SPARK-24801][CORE] Avoid memory waste by empty byte[] arrays in
SaslEncryption$EncryptedMessage
## What changes were proposed in this pull request?
Initialize SaslEncryption$EncryptedMes
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Can one of the admins verify this patch?
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Yes.
On Wed, Jul 18, 2018 at 4:43 PM, UCB AMPLab
wrote:
> Can one of the admins verify this patch?
>
> â
> You are receiving this because you authored the thread.
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Github user markhamstra commented on the issue:
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I don't accept you assertions of what constitutes the majority and minority
of Spark users or use cases or their relative importance. As a long-time
maintainer of the Spark scheduler, it is also
Github user viirya commented on the issue:
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It shows AppVeyor build failed. But I can't find the failed part in the
details. Maybe I miss it? cc @felixcheung @HyukjinKwon
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Github user viirya commented on the issue:
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Seems it is timeout: `Build execution time has reached the maximum allowed
time for your plan (90 minutes).` Can we set up the building time?
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Github user BryanCutler commented on the issue:
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I don't think it's your PR, let's try again
On Tue, Jul 17, 2018, 5:54 PM cclauss wrote:
> Three (different) failures in a row. Should I break this PR into separate
> PRs?
Github user BryanCutler commented on the issue:
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retest this please
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Can you add tests? cc: @gatorsmile
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LGTM too
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cc @hvanhovell too
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retest this please
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@tdas Addressed review comments. Please take a look again. Thanks in
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> Except the TPC-DS queries, are we able to find some workloads that could
perform faster using the bytecode generated by the JDK compiler?
Since I don't have real workloads or non-TPCDS quer
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Github user ssimeonov commented on the issue:
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> Repartitioning based upon a snapshot of the number of cores available
cluster-wide is clearly not the correct thing to do in many instances and use
cases.
I wholeheartedly agree and I c
Github user jerryshao commented on a diff in the pull request:
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--- Diff:
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+/*
+ * Licensed
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Github user HyukjinKwon commented on the issue:
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I wouldn't argue who more take care of or represent users or not though.
That's easily biased. If there's a technical concern from a committer or PMC, I
wouldn't go for it.
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--- Diff: core/src/main/scala/org/apache/spark/storage/BlockManager.scala
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@@ -659,6 +659,11 @@ private[spark] class BlockManager(
* Get block
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Github user maropu commented on the issue:
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(As I described in the jira) What's this func is used for? Is this related
to the other work?
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GitHub user hehuiyuan opened a pull request:
https://github.com/apache/spark/pull/21812
SPARK UI K8S : this parameter's
illustration(spark.kubernetes.executor.label.[LabelName] )
[é¾æ¥å°åï¼](http://spark.apache.org/docs/2.3.0/running-on-kubernetes.html)
**
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Github user HyukjinKwon commented on the issue:
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Yea, that looks due to time limit. I am seeing sometimes it hit the time
limit issue.
The problem is, it's kind of difficult to increase the build time (see also
https://github.com/appv
Github user HyukjinKwon commented on the issue:
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Meanwhile, will try to take another look to reduce the time, or see if we
can split the test, or we can request the time limit increase again as a last
resort.
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Github user HyukjinKwon commented on the issue:
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Yea, it's internal to Spark. Might be good to keep it but that concern
should be secondary IMHO.
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--- Diff:
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@@ -145,7 +145,7 @@ abstract class NumericType extends AtomicTyp
Github user HyukjinKwon commented on the issue:
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Technical reason: It's kind of risky to rely on `CatalystScan` and
completely replace the interface. I think I already see some tests were
disabled here. Also, there look potential better sugges
Github user HyukjinKwon commented on the issue:
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BTW, datasource v2 is in progress too to allow more push downs (see
[SPARK-22386](https://issues.apache.org/jira/browse/SPARK-22386)). You might
want to take a look
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@hehuiyuan, please ask a question via a mailing list. See also
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