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)
Github user maropu commented on the issue:
https://github.com/apache/spark/pull/21803
(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 jerryshao commented on a diff in the pull request:
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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
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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Github user jerryshao commented on a diff in the pull request:
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--- Diff:
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---
@@ -0,0 +1,49 @@
+/*
+ * Licensed
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
Github user SparkQA commented on the issue:
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Github user maropu commented on the issue:
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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
Github user HeartSaVioR commented on the issue:
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@tdas Addressed review comments. Please take a look again. Thanks in
advance!
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retest this please
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Github user HyukjinKwon commented on the issue:
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cc @hvanhovell too
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LGTM too
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Github user HeartSaVioR commented on a diff in the pull request:
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@@ -64,21 +66,143 @@ class StateStoreSuite
Github user maropu commented on the issue:
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Can you add tests? cc: @gatorsmile
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Github user BryanCutler commented on the issue:
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retest this please
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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?
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Github user viirya commented on the issue:
https://github.com/apache/spark/pull/20146
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 viirya commented on the issue:
https://github.com/apache/spark/pull/20146
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 markhamstra commented on the issue:
https://github.com/apache/spark/pull/21589
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
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Github user countmdm commented on the issue:
https://github.com/apache/spark/pull/21811
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
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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
Github user ssimeonov commented on the issue:
https://github.com/apache/spark/pull/21589
@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
Github user zsxwing commented on a diff in the pull request:
https://github.com/apache/spark/pull/21488#discussion_r203561847
--- Diff:
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---
@@ -115,7 +116,7 @@ private[kafka010] class
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
Github user markhamstra commented on the issue:
https://github.com/apache/spark/pull/21589
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
Github user ssimeonov commented on the issue:
https://github.com/apache/spark/pull/21589
@markhamstra even the words you are using indicate that you are missing the
intended audience.
> high-level, declarative abstraction that can be used to specify requested
Job
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Github user mgaido91 commented on the issue:
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LGTM
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Github user markhamstra commented on the issue:
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@ssimeonov the purpose of a public API is not to offer hack solutions to a
subset of problems. What is needed is a high-level, declarative abstraction
that can be used to specify requested Job
Github user ssimeonov commented on the issue:
https://github.com/apache/spark/pull/21589
@markhamstra the purpose of this PR is not to address the topic of dynamic
resource management in arbitrarily complex Spark environments. Most Spark users
do not operate in such environments. It
Github user markhamstra commented on the issue:
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No, defaultParallelism isn't more useful in that case, but that just starts
getting to my overall assessment of this JIRA and PR: It smells of defining the
problem to align with a preconception
Github user shaneknapp commented on the issue:
https://github.com/apache/spark/pull/21584
just wanted to briefly chime in:
TL;DR: this build will fail until the PRB is running on our ubuntu build
nodes.
we are currently blocked from testing this stuff w/the current
Github user viirya commented on the issue:
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LGTM
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Github user liyinan926 commented on the issue:
https://github.com/apache/spark/pull/21796
I think we decided not to take any new configuration options with
https://issues.apache.org/jira/browse/SPARK-24434 being worked on. @mccheah
@foxish.
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Github user ssimeonov commented on the issue:
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@mridulm your comments make an implicit assumption, which is quite
incorrect: that Spark users read the Spark codebase and/or are aware of Spark
internals. Please, consider this PR in the context
Github user MaxGekk commented on the issue:
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> it's not terribly useful to know, e.g., that there are 5 million cores in
the cluster if your Job is running in a scheduler pool that is restricted to
using far fewer CPUs via the pool's
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Github user MaxGekk commented on the issue:
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Please, look at this PR: https://github.com/apache/spark/pull/21810 . It
introduces `AvroOptions`.
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GitHub user MaxGekk opened a pull request:
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[SPARK-24854][SQL] Gathering all Avro options into the AvroOptions class
## What changes were proposed in this pull request?
In the PR, I propose to put all `Avro` options in new class
Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/21710#discussion_r203538149
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/r/PrefixSpanWrapper.scala ---
@@ -0,0 +1,34 @@
+/*
+ * Licensed to the Apache Software Foundation
Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/21761
Hi, @gatorsmile .
Could you review this PR? I'm wondering if we can have this for Spark 2.4
before branch-cut.
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Github user viirya commented on the issue:
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@hvanhovell Got it. Thanks for your explanation. LGTM.
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Github user mridulm commented on the issue:
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@MaxGekk We are going in circles.
I dont think this is a good api to expose currently - the data is available
through multiple other means as I detailed and while not a succinct oneliner,
it is
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Github user rxin commented on the issue:
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Let's remove it in 3.0 then. We can do it after 2.4 release.
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Github user BryanCutler commented on the issue:
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Thanks @HyukjinKwon! Any additional comments @holdenk @sethah @viirya
@felixcheung ?
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Github user squito commented on the issue:
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@mridulm @jerryshao @felixcheung last one in the 2GB block limit series.
just rebased to include the updates to
https://github.com/apache/spark/pull/21440. I will also run my tests on a
cluster
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