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Github user hhbyyh commented on a diff in the pull request:
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--- Diff: docs/ml-frequent-pattern-mining.md ---
@@ -0,0 +1,87 @@
+---
+layout: global
+title: Frequent Pattern Mining
+displayTitle:
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Github user vanzin commented on the issue:
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Options put the burden on the user to figure things out (do I need to set
this or not?). If you want to investigate whether you can trim more data (e.g.
internal metrics that just mirror stuff
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ok to test
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Github user asfgit closed the pull request at:
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Github user gatorsmile commented on the issue:
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Thanks! Merging to master/2.2
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Thanks! Merging to master/2.2
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Github user jisookim0513 commented on the issue:
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@vanzin @ajbozarth if you guys think having an option to skip logging
internal accumulators (in my case I don't use the SQL UI) and completely
getting rid of updated block statues are not
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Github user zero323 commented on a diff in the pull request:
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--- Diff: R/pkg/R/column.R ---
@@ -302,3 +301,65 @@ setMethod("otherwise",
jc <- callJMethod(x@jc, "otherwise", value)
Github user tgravescs commented on the issue:
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We see the same issue on some of our clusters. I was planning on doing 2
things. Something like this to reduce that memory usage and then on the other
side you could change the shuffle fetcher
Github user jisookim0513 commented on a diff in the pull request:
https://github.com/apache/spark/pull/16714#discussion_r113776549
--- Diff: core/src/main/scala/org/apache/spark/util/JsonProtocol.scala ---
@@ -343,10 +376,14 @@ private[spark] object JsonProtocol {
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Github user vanzin commented on the issue:
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I think so. Just replying to the question.
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Github user ajbozarth commented on the issue:
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Didn't #17412 already get rid of block statuses though?
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GitHub user anabranch opened a pull request:
https://github.com/apache/spark/pull/17787
[SS] Bug in KafkaWriter Looks at Unanalyzed Plans
## What changes were proposed in this pull request?
This now asserts that a plan has been analyzed before reading the schema.
Github user vanzin commented on the issue:
https://github.com/apache/spark/pull/16714
After having played with some of this code for other reasons, at least some
of the internal accumulators are needed to rebuild the SQL UI.
As far as logging updated block statuses, I still
Github user zero323 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17783#discussion_r113773816
--- Diff: R/pkg/R/column.R ---
@@ -302,3 +301,65 @@ setMethod("otherwise",
jc <- callJMethod(x@jc, "otherwise", value)
Github user jisookim0513 commented on the issue:
https://github.com/apache/spark/pull/16714
I would still like not to have internal accumulators in the event logs, as
well as updated block statuses. @vanzin would you be ok with eliminating all
internal accumulators and have an option
Github user zero323 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17783#discussion_r113772682
--- Diff: R/pkg/R/functions.R ---
@@ -3803,3 +3803,41 @@ setMethod("repeat_string",
jc <- callJStatic("org.apache.spark.sql.functions",
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Github user asfgit closed the pull request at:
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Github user ajbozarth commented on the issue:
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Is this still an issue or did #17412 fix this?
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Github user vanzin commented on the issue:
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Thanks. Merging to master / 2.2.
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Github user gatorsmile commented on the issue:
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The JIRA https://issues.apache.org/jira/browse/SPARK-20495 was just opened.
Please feel free to submit the PR
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Github user koeninger commented on the issue:
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LGTM pending jason's comments on tests
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Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/17771
Could you update the PR title to
```
[SPARK-20471]Remove AggregateBenchmark testsuite warning: Two level hashmap
is disabled but vectorized hashmap is enabled
```
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Github user asfgit closed the pull request at:
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Github user dbtsai commented on the issue:
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LGTM. Merged into master and branch 2.2. Thanks.
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Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/17742#discussion_r113762883
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -276,18 +277,39 @@ object
Github user MLnick commented on a diff in the pull request:
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--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -297,23 +319,10 @@ object
Github user MLnick commented on a diff in the pull request:
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--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -277,39 +277,38 @@ object
Github user MLnick commented on a diff in the pull request:
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--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -277,39 +277,38 @@ object
Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/17742#discussion_r113761692
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -277,39 +277,38 @@ object
Github user MLnick commented on the issue:
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@mpjlu yeah we can do the ML version in a follow up PR that is ok (I can
help if needed).
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Jenkins, ok to test.
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Jenkins, add to whitelist.
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Github user JoshRosen commented on the issue:
https://github.com/apache/spark/pull/17766
Per my comments on the earlier email discussion thread, this seems fine to
me.
(I'm not totally MIA; I just got back from vacation and busy getting caught
up).
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Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/1
LGTM pending Jenkins
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Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/17783#discussion_r113754078
--- Diff: R/pkg/R/column.R ---
@@ -302,3 +301,65 @@ setMethod("otherwise",
jc <- callJMethod(x@jc, "otherwise", value)
Github user srowen commented on the issue:
https://github.com/apache/spark/pull/17778
@janewangfb that's because `array_contains` is a Hive function
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Github user JasonMWhite commented on the issue:
https://github.com/apache/spark/pull/17774
Tests have some fairly repetitive code, but not sure if that's a problem or
not. Looks good to me.
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Github user shubhamchopra commented on the issue:
https://github.com/apache/spark/pull/17673
@Krimit @MLnick @hhbyyh I am working on getting your earlier queries
answered.
@Krimit Thanks for looking into the code, I will try to get the code-review
feedback incorporated in a
Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/17783#discussion_r113753986
--- Diff: R/pkg/R/functions.R ---
@@ -3803,3 +3803,41 @@ setMethod("repeat_string",
jc <- callJStatic("org.apache.spark.sql.functions",
Github user kiszk commented on the issue:
https://github.com/apache/spark/pull/17773
Yes, I think it should see `capacity`. I will create a PR tomorrow for
`OffHeapColumnVector`.
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Github user JasonMWhite commented on a diff in the pull request:
https://github.com/apache/spark/pull/17774#discussion_r113753764
--- Diff:
external/kafka-0-10/src/test/scala/org/apache/spark/streaming/kafka010/DirectKafkaStreamSuite.scala
---
@@ -617,6 +617,94 @@ class
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Github user janewangfb commented on the issue:
https://github.com/apache/spark/pull/17778
@srowen We have array_contains UDF. I think it is nice to have one that
removes all the duplicated elements.
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Github user rednaxelafx commented on the issue:
https://github.com/apache/spark/pull/1
Thanks for the review! I'll update the patch again.
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Github user vanzin commented on the issue:
https://github.com/apache/spark/pull/17766
Josh seems MIA, @srowen care to take a look?
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Github user rednaxelafx commented on a diff in the pull request:
https://github.com/apache/spark/pull/1#discussion_r113751399
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
---
@@ -89,6 +89,22 @@ object Cast {
case _ =>
Github user arzt commented on the issue:
https://github.com/apache/spark/pull/17774
I changed the max messages per partition to be at least 1. Agreed?
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Github user rednaxelafx commented on a diff in the pull request:
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--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
---
@@ -165,6 +181,14 @@ case class Cast(child: Expression,
Github user AmplabJenkins commented on the issue:
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Can one of the admins verify this patch?
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GitHub user dgshep opened a pull request:
https://github.com/apache/spark/pull/17786
[SPARK-20483] Mesos Coarse mode may starve other Mesos frameworks
Set max cores to a multiple of the smallestExecutorSize
## What changes were proposed in this pull request?
Set
Github user steveloughran commented on a diff in the pull request:
https://github.com/apache/spark/pull/12004#discussion_r113750071
--- Diff: pom.xml ---
@@ -1145,6 +1150,70 @@
+
+
--- End diff --
I'm
Github user steveloughran commented on a diff in the pull request:
https://github.com/apache/spark/pull/12004#discussion_r113749971
--- Diff: pom.xml ---
@@ -621,6 +621,11 @@
${fasterxml.jackson.version}
+
Github user steveloughran commented on a diff in the pull request:
https://github.com/apache/spark/pull/12004#discussion_r113749781
--- Diff: cloud/pom.xml ---
@@ -0,0 +1,117 @@
+
+
+http://maven.apache.org/POM/4.0.0;
+
Github user steveloughran commented on a diff in the pull request:
https://github.com/apache/spark/pull/12004#discussion_r113749812
--- Diff: docs/storage-openstack-swift.md ---
@@ -19,41 +20,32 @@ Although not mandatory, it is recommended to configure
the proxy server of Swift
Github user steveloughran commented on a diff in the pull request:
https://github.com/apache/spark/pull/12004#discussion_r113749428
--- Diff: cloud/pom.xml ---
@@ -0,0 +1,158 @@
+
+
+http://maven.apache.org/POM/4.0.0;
Github user JasonMWhite commented on the issue:
https://github.com/apache/spark/pull/17774
I think @koeninger's suggestion is valid. `effectiveRateLimitPerPartition`
is the upper bound on the number of messages per partition per second, and
`maxMessagesPerPartition` sets an upper
Github user koeninger commented on the issue:
https://github.com/apache/spark/pull/17774
Have you read the function def clamp?
Rate limit of 1 should not imply an attempt to grab 1 message even if it
doesn't exist.
On Apr 27, 2017 11:01, "Sebastian Arzt"
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17191
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