Github user cloud-fan commented on a diff in the pull request:
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--- Diff:
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@@ -180,35 +214,28 @@ abstract class
Github user kiszk commented on the issue:
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Ah, I understand my misunderstanding. In this discussion, you mean
"intermediate array" is "new int[]".
Yes, let me do it.
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Github user wzhfy commented on a diff in the pull request:
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--- Diff:
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@@ -180,35 +214,28 @@ abstract class StatisticsCollectionTestBase
Github user rxin commented on a diff in the pull request:
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Github user cloud-fan commented on the issue:
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I think it's nothing to do with physical operators, we are talking about
`CreateArray` right? Avoiding to create the intermedia primitive array should
be faster generally.
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Github user kiszk commented on the issue:
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I understand what you want to do. I agree with this in this case. This is
because the child of `Project` is an array creation.
== Physical Plan ==
*Project [array((value#2 + 1.1),
Github user wzhfy commented on a diff in the pull request:
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Github user zsxwing commented on a diff in the pull request:
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--- Diff:
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---
@@ -214,6 +228,10 @@ class StreamExecution(
Github user viirya commented on the issue:
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Yea we can avoid the intermediate primitive array. Maybe we can benchmark
against it also.
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Github user cloud-fan commented on the issue:
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Actually I can't reproduce this issue locally, but by looking at the logs,
I'm 90% percent sure this is the cause. The only way to verify it may be
merging and checking the jenkins maven status
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Can one of the admins verify this patch?
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Github user cloud-fan commented on the issue:
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In `CreateArray` we can just evaluate each expression and write their
results to unsafe array with the writer, no intermediate data need to be
created.
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Github user davies commented on the issue:
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Pushing down predicates into data source is also during optimization in
planner, I think this one is not the first that do optimization outside
Optimizer.
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Github user davies commented on the issue:
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The reason we move the PythonUDFEvaluator from logical plan into physical
plan, because this one-off break many things, many rules need to treat
specially.
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Merging to master and 2.1.
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Github user zsxwing commented on a diff in the pull request:
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Github user viirya commented on the issue:
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If we add a logical node for python evaluator, we'd push down the Filter
through it, so the optimizer rule won't combine two Filter into one again?
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@@ -214,6 +228,10 @@ class StreamExecution(
Github user davies commented on the issue:
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@cloud-fan It's not trivial to do this in optimizer, for example, we should
split one Filter into two, that will conflict with another optimizer rule, that
combine two filter into one.
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Github user kiszk commented on the issue:
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@cloud-fan Yeah, to keep primitive array and to use `UnsafeArrayWrite` can
avoid extra bulkcopy.
To achieve this avoidance, I think that we need to create
`GenericArrayData` after enabling
Github user viirya commented on the issue:
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How do we make sure the fixed test passes Maven-based tests?
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Github user cloud-fan commented on a diff in the pull request:
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--- Diff:
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@@ -166,6 +166,30 @@ class InsertIntoHiveTableSuite
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It's a little hacky to me that we do optimization in a planner. How hard is
it if we introduce a logical node for python evaluator? We can define an
interface in catalyst, e.g.
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--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/execution/python/BatchEvalPythonExecSuite.scala
---
@@ -0,0 +1,109 @@
+/*
+ * Licensed to
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Github user maropu commented on the issue:
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Thanks for these comments! ya, I do not like this approach, too. But, since
those who reshard streams always hit this issue and resharding is important for
load-balancing in Kinesis streams
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--- Diff:
external/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
---
@@ -225,6 +225,74 @@ abstract class
Github user cloud-fan commented on the issue:
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retest this please
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Github user cloud-fan commented on the issue:
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creating a primitive array and setting element values is also element-wise
copy right? And we need an extra bulkcopy to write the primitive array to
unsafe array. By using unsafe array writer, we
Github user wangmiao1981 commented on the issue:
https://github.com/apache/spark/pull/16222
@mengxr
I used the following R code and glmnet to check whether `regParam = 0.5`
fits a good model.
> iris2 <- iris[iris$Species %in% c("versicolor", "virginica"), ]
>
Github user maropu commented on a diff in the pull request:
https://github.com/apache/spark/pull/16213#discussion_r91829740
--- Diff:
external/kinesis-asl/src/main/java/com/amazonaws/services/kinesis/clientlibrary/lib/worker/CheckpointerShim.java
---
@@ -0,0 +1,43 @@
+/*
Github user kapilsingh5050 commented on a diff in the pull request:
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--- Diff:
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
---
@@ -98,20 +98,15 @@ class
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/16228#discussion_r91829681
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/estimation/JoinEstimation.scala
---
@@ -0,0 +1,175 @@
+/*
+ *
Github user cloud-fan commented on a diff in the pull request:
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@@ -98,20 +98,15 @@ class
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--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/Statistics.scala
---
@@ -213,7 +214,8 @@ object ColumnStat extends Logging
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--- Diff:
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---
@@ -418,4 +431,19 @@ class HiveCommandSuite extends
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--- Diff:
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@@ -133,6 +133,79 @@ class StatisticsCollectionSuite extends
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cc @srowen @kapilsingh5050 @ueshin @viirya
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[SQL][minor] simplify a test to fix the maven tests
## What changes were proposed in this pull request?
After https://github.com/apache/spark/pull/15620 , all of the Maven-based
2.0
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--- Diff:
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@@ -133,6 +133,79 @@ class StatisticsCollectionSuite extends
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--- Diff:
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@@ -418,4 +431,19 @@ class HiveCommandSuite extends
Github user cloud-fan commented on the issue:
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The changes LGTM, as we do propagate the `isSrcLocal` incorrectly. It would
be better if we can also fix the inconsistent behavior of `LOAD DATA` between
spark and hive, and improve the test
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--- Diff:
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@@ -213,7 +214,8 @@ object ColumnStat extends Logging {
Github user cloud-fan commented on a diff in the pull request:
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@@ -418,4 +431,19 @@ class HiveCommandSuite extends
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--- Diff: R/pkg/vignettes/sparkr-vignettes.Rmd ---
@@ -768,8 +768,46 @@ newDF <- createDataFrame(data.frame(x = c(1.5, 3.2)))
Github user rxin commented on a diff in the pull request:
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@@ -97,6 +97,27 @@ class
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Github user cloud-fan commented on the issue:
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ok then the parser rule looks good to me, my only concern is the new APIs
in `ExternalCatalog`, I don't think they are necessary, @jiangxb1987 what's the
motivation you added them?
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Github user ericl commented on the issue:
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LGTM
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[SPARK-18815] [SQL] Fix NPE when collecting column stats for string/binary
column having only null values
## What changes were proposed in this pull request?
During column stats collection,
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Merging into master, branch-2.1
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Github user ericl commented on the issue:
https://github.com/apache/spark/pull/15936
This seems to be causing https://issues.apache.org/jira/browse/SPARK-18814
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Github user wzhfy commented on a diff in the pull request:
https://github.com/apache/spark/pull/16228#discussion_r91826623
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/estimation/JoinEstimation.scala
---
@@ -0,0 +1,175 @@
+/*
+ *
Github user wzhfy commented on a diff in the pull request:
https://github.com/apache/spark/pull/16228#discussion_r91826564
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/estimation/JoinEstimation.scala
---
@@ -0,0 +1,175 @@
+/*
+ *
Github user wzhfy commented on the issue:
https://github.com/apache/spark/pull/16228
@Tagar Thanks for sharing this information. Yes, it would be better to use
PK/FK, but it won't be done in this pr, and we need to implement PK/FK
constraints in Spark first.
> the
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/16230
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Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/16230
Merged build finished. Test FAILed.
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Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/16230
Test FAILed.
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