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GitHub user clockfly opened a pull request:
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[SPARK-16841][SQL] Improves the row level metrics performance when reading
Parquet table
## What changes were proposed in this pull request?
When reading from Parquet table, Spark updates
Github user nsyca commented on the issue:
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@hvanhovell,
First, my apologies for delaying the replies. I am travelling this week,
only getting spontaneous connections. Thank you for your explanation of the
implementation and the reason
Github user clockfly commented on the issue:
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@gatorsmile Thanks! updated.
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Github user gatorsmile commented on the issue:
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Maybe, we can correct the PR description and make it more accurate.
This PR avoids the extra memory copy when the vectorized parquet record
reader is not being used for reading a non-part
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LGTM
FYI, MySQL and PostgreSQL support NOW as a synonym of CURRENT_TIMESTAMP
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[SPARK-16320][SQL] Fix performance regression for parquet table with nested
fields
## What changes were proposed in this pull request?
For non-partitioned parquet table with nested column
Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/14439#discussion_r73064350
--- Diff:
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@@ -157,6 +145,26 @@ object TypeCoercion {
})
Github user yhuai commented on the issue:
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It will be good to summarize the behaviors of other systems in the
description. Let's also explain the behavioral change of this pr in the
description. So, others can understand its implication.
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Github user yinxusen commented on the issue:
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@MLnick They serve different purpose. This one is for users who have built
their tools upon it. The `LatentDirichletAllocationExample` is for ML docs.
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Can one of the admins verify this patch?
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[SPARK-16839] [SQL] redundant aliases after cleanupAliases
## What changes were proposed in this pull request?
a failing test, soon to add a proposed fix
## How was this patch tested?
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test this please
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Thanks! Merging to master and 2.0.
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Close this for the better PR https://github.com/apache/spark/pull/14439
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LGTM
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Merged to master/2.0
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Github user wangmiao1981 commented on the issue:
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@felixcheung Let me check the code of launching the sparkR shell. Can you
point me the code?
Thanks!
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Github user vlad17 commented on the issue:
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There may be small bug introduced here. Please see my comment inline:
https://github.com/apache/spark/pull/11443/files#diff-c3d0394b2fc08fb2842ff0362a5ac6c9R836
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--- Diff: sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala ---
@@ -745,6 +825,80 @@ class DataFrame private[sql](
}
/**
+
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Github user dongjoon-hyun commented on the issue:
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Oh, sorry for misunderstanding, @hvanhovell . I think we can integrate
`ResolveRelation` and this one. I'll be back after some testing in this
evening. Also for the common approach, I will bri
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GitHub user zsxwing opened a pull request:
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[SPARK-15869][Streaming]Fix a potential NPE in
StreamingJobProgressListener.getBatchUIData
## What changes were proposed in this pull request?
Moved `asScala` to a `map` to avoid NPE.
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Jenkins test this please
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Github user sameeragarwal commented on the issue:
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Just a minor nit. LGTM.
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--- Diff:
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@@ -576,4 +576,605 @@ class AggregateBenchmark
Github user junyangq commented on the issue:
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@felixcheung Sorry I still didn't get there. It seems that internally it
checks via `args.primaryResource`. I was wondering if there is a good way to
access to that in sparkR. Thanks!
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--- Diff:
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@@ -499,14 +499,16 @@ object SQLConf {
.intConf
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--- Diff: R/pkg/R/mllib.R ---
@@ -632,3 +659,106 @@ setMethod("predict", signature(object =
"AFTSurvivalRegressionModel"),
function(object
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@@ -660,36 +778,74 @@ case class HashAggregateE
Github user felixcheung commented on the issue:
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Correct, I don't think should name the function `sc.setLogLevel`
The message is coming from Scala - I changed the default logging level for
python and R recently so I think I know where it is
Github user dongjoon-hyun commented on the issue:
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Thank you, @hvanhovell !
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Github user rxin commented on the issue:
https://github.com/apache/spark/pull/14439
@yhuai do you have a concrete suggestion other than being careful here?
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Github user sameeragarwal commented on a diff in the pull request:
https://github.com/apache/spark/pull/14176#discussion_r73037506
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala
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@@ -459,52 +476,115 @@ case class HashAggregate
Github user ckadner commented on the issue:
https://github.com/apache/spark/pull/14398
LGTM
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Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/14308
Merged build finished. Test PASSed.
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Github user AmplabJenkins commented on the issue:
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/14308
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Github user sameeragarwal commented on a diff in the pull request:
https://github.com/apache/spark/pull/14176#discussion_r73036933
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala
---
@@ -279,9 +280,15 @@ case class HashAggregateEx
Github user zsxwing commented on the issue:
https://github.com/apache/spark/pull/14396
@JoshRosen LGTM for the current patch. However, I have a question about the
following description in your PR:
> At file download time, Spark tasks will fail with exceptions if an
executor a
Github user sameeragarwal commented on a diff in the pull request:
https://github.com/apache/spark/pull/14176#discussion_r73036686
--- Diff:
sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/FixedLengthRowBasedKeyValueBatch.java
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@@ -165,10 +165,16 @@ private
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/14182
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