Github user cerisier commented on the issue:
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This is pure awesome. Any chance of this being revisited someday ?
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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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---
@@ -313,13 +313,32 @@ trait CheckAnalysis extends
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---
@@ -43,8 +43,127 @@ import
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Github user hvanhovell commented on the issue:
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Don't `Dataset`s and `Encoder`s make this less relevant? What would be the
use case here?
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Github user gatorsmile commented on the issue:
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@rxin So far, I think we do not provide such a capability for table-level
configuration. For `parquet`, the `DataFrameReader`'s option `mergeSchema` has
a higher priority than the global
Github user yhuai commented on a diff in the pull request:
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--- Diff:
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@@ -43,8 +43,128 @@ import
Github user dafrista commented on a diff in the pull request:
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--- Diff:
sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala ---
@@ -479,8 +340,299 @@ private[hive] trait HiveInspectors {
}
Github user liancheng commented on a diff in the pull request:
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@@ -43,8 +43,128 @@ import
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--- Diff:
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---
@@ -115,7 +115,7 @@ case class KeyRemoved(key:
Github user rxin commented on the issue:
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hm - what if i want to specify specific options when reading data from a
table?
e.g. whether to use the vectorized reader or not?
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Github user hvanhovell commented on the issue:
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This looks pretty good. What I am thinking is that generating an encoder
could create another nice performance speedup here.
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Github user liancheng commented on a diff in the pull request:
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@@ -590,6 +592,53 @@ class SparkSqlAstBuilder(conf: SQLConf)
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--- Diff:
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@@ -0,0 +1,69 @@
+/*
+ * Licensed to the Apache
Github user viirya commented on the issue:
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@hvanhovell @rxin Got it. Thanks! I will re-run the benchmark.
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Github user asfgit closed the pull request at:
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Github user yhuai commented on the issue:
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Thanks. I am merging this to master and branch 2.0.
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GitHub user sarutak opened a pull request:
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[SPARK-16061][SQL] The property "spark.sql.stateStore.maintenanceInterval"
should be renamed to "spark.streaming.stateStore.maintenanceInterval"
## What changes were proposed in this pull request?
Github user rxin commented on the issue:
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@viirya when you construct a performance benchmark, you would want to
minimize the overhead of things outside the code path you are testing. In this
case, a lot of the time were spent in the collect
Github user rxin commented on the issue:
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LGTM - merging in master/2.0.
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Github user hvanhovell commented on the issue:
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Would PR https://github.com/apache/spark/pull/13676 help to improve
performance?
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Github user hvanhovell commented on the issue:
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@viirya could you re-run the benchmarks without calling collect(). Do a
count or a simple aggregate instead, collect spends a tonne of time in
serializing results from `InternalRow` to `Row`.
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+/*
+ * Licensed to the Apache
Github user lianhuiwang commented on a diff in the pull request:
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Github user hvanhovell commented on the issue:
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@lianhuiwang thanks for updating the PR. Could you implement the Macro
removal by pattern matching on a (to be created) `MacroFunctionBuilder` class.
I feel this is simpler, and doesn't touch as
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GitHub user zsxwing opened a pull request:
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[SPARK-16050][Tests]Remove the flaky test: ConsoleSinkSuite
## What changes were proposed in this pull request?
ConsoleSinkSuite just collects content from stdout and compare them with
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/cc @marmbrus @brkyvz
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[SPARK-16060][SQL] Vectorized Orc reader
## What changes were proposed in this pull request?
Currently Orc reader in Spark SQL doesn't support vectorized reading. As
Hive Orc already
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Github user zsxwing commented on the issue:
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@zzcclp see https://issues.apache.org/jira/browse/SPARK-16017
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ping @cloud-fan
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@zsxwing , why does this pr be reverted in branch-1.6?
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LGTM
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Github user gatorsmile commented on the issue:
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Maybe I should move all the JDBC related API misuse issues into this PR.
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Github user gatorsmile commented on the issue:
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@rxin @liancheng @clockfly @yhuai Could you please also review this PR? I
found all of you recently reviewed the JDBC-related PRs. Thanks!
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@srowen Will submit more PRs about `JDBC`. The interface of
`DataFrameReader` and `DataFrameWriter` are not designed for `JDBC` data
sources. For Spark SQL beginners, they might hit various
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Github user shivaram commented on the issue:
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Thanks @NarineK -- cc @sun-rui for review
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https://github.com/apache/spark/pull/13748
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Github user rxin commented on the issue:
https://github.com/apache/spark/pull/13737
Why does Python need to load these test resources? I think the proper fix
is to get rid of that dependency. Otherwise we are making the test harness more
and more complicated and tighter coupling.
Github user NarineK commented on the issue:
https://github.com/apache/spark/pull/12836
Thanks for the quick response. I'll create one.
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/13760
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Github user vectorijk commented on the issue:
https://github.com/apache/spark/pull/12836
@NarineK I am not quite sure. Maybe you could create a new JIRA for
gapply's programming guide.
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