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Github user oliverpierson commented on the pull request:
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Putting this up for review now. Tests are passing on my machine. Using
`approxQuantile` in DataFrame stats reduces amount of code required by a good
bit.
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Github user dongjoon-hyun commented on the pull request:
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`PySpark` failure is irrelevant for this PR, but I rebased this PR to the
master because this is still a problem.
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Github user HyukjinKwon commented on the pull request:
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@falaki Just let to know, I changed the name `CSVInferSchema` to
`InferSchema` mainly for consistent names for CSV and JSON data source but
maybe they might have
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Github user rxin commented on the pull request:
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I personally find it confusing having to reason about when we can
"head"/"collect"/"show" and when we cannot, and that's why the Scala/Python
version of the API didn't
Github user rxin commented on the pull request:
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"as we wanted to generate SQLs which is closer to the original SQL"
Why is this a goal? I worry about the fragility of this two cases, if we
really only need one
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--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
---
@@ -144,6 +146,56 @@ object EliminateSerialization extends
Github user sun-rui commented on the pull request:
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It seems better to keep SparkR as a base package providing core
functionalities, while visualization features can be implemented in other
packages based on SparkR.
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--- Diff:
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@@ -445,4 +461,86 @@ class LogicalPlanToSQLSuite extends
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--- Diff: mllib/src/main/scala/org/apache/spark/ml/tree/codeGenerator.scala
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@@ -0,0 +1,156 @@
+/*
+ * Licensed to the Apache Software Foundation
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OK, thanks for the explanation, I will close this PR.
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Github user sun-rui commented on the pull request:
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A column can be applied to different dataframes.
For example, if both df1 and df2 have a column named "col",then
col <- column("col")
collect(select(df1,
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[SPARK-13664][SQL] Cleanup Data Source resolution
Follow-up to #11509, that simply refactors the interface that we use when
resolving a pluggable `DataSource`.
- Multiple functions share the
Github user rxin commented on the pull request:
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But this can only be added to 2.0 (we won't be able to change an existing
release). If users already need to change the constructor in order to use it,
why don't they
Github user kiszk commented on a diff in the pull request:
https://github.com/apache/spark/pull/11301#discussion_r55307944
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpressionSet.scala
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@@ -50,7 +50,7 @@ object ExpressionSet {
class
Github user gatorsmile commented on a diff in the pull request:
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--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
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@@ -769,6 +770,28 @@ object CombineFilters extends
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@rxin HiveContext is heavily used by many users now, and many of them still
coupled with old spark version. As this change would be trivial but not
constructive, I think
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Thanks for the pull request. So we are actually going to deprecate
HiveContext because it has been one of the most confusing contexts in Spark.
See more in
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Github user asfgit closed the pull request at:
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Merging into master, thanks.
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[SPARK-13737][SQL][wip]Add getOrCreate method for HiveContext
There is a "getOrCreate" method in SQLContext, which is useful to
recoverable streaming application with SQL operation.
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[SPARK-529] [sql] Modify SQLConf to use new config API from core.
Because SQL keeps track of all known configs, some customization was
needed in SQLConf to allow that, since the core API does
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test this please
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Github user dilipbiswal commented on the pull request:
https://github.com/apache/spark/pull/11563#issuecomment-193542558
@rxin. Hi Reynold,
We have two cases to handle.
```SQL
SELECT explode(array(1,2,3)) FROM src
SELECT gentab2.* FROM t1 LATERAL VIEW
Github user frreiss commented on the pull request:
https://github.com/apache/spark/pull/9777#issuecomment-193542138
LGTM aside from that one typo
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--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala ---
@@ -132,8 +133,10 @@ class OneHotEncoder(override val uid: String) extends
Github user thunterdb commented on the pull request:
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@pravingadakh sorry for the delay. Would you mind resolving the conflicts?
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LGTM
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Github user zsxwing commented on the pull request:
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LGTM
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LGTM
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Github user cloud-fan commented on a diff in the pull request:
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--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
---
@@ -345,8 +343,6 @@ object ColumnPruning extends
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--- Diff:
core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala ---
@@ -125,16 +125,14 @@ private[spark] class AppClient(
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Github user zsxwing commented on the pull request:
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> So what happens now if the scheduled Runnable throws an exception?
Just go to `Thread.getDefaultUncaughtExceptionHandler()`.
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Github user jerryshao commented on the pull request:
https://github.com/apache/spark/pull/11518#issuecomment-193534057
Yes, I also tested with multiple attempts.
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Github user nongli commented on the pull request:
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sounds good.
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retest this please
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LGTM
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Github user markhamstra commented on the pull request:
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So what happens now if the scheduled Runnable throws an exception?
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