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Going to re-open a PR.
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--- Diff: sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
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@@ -298,6 +298,13 @@ class SQLQuerySuite extends QueryTest with
BeforeAndA
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This also contains https://github.com/apache/spark/pull/5646
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[SPARK-7069][SQL] Rename NativeType -> AtomicType
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--- Diff: sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
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BeforeAndAfterAll
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i am closing this.
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Github user harishreedharan commented on the pull request:
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I have done testing by making sure the app runs longer than the expiration
time, but not killing the executors. I will test that scenario too and report
back. In
Github user scwf commented on the pull request:
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Get it. interesting case!
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Github user zsxwing commented on the pull request:
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@tdas, I have a question about the failure case: If a Spark job throws an
exception, it will propagate to `ssc.awaitTermination()`. In such case, I
suppose the user will
Github user adrian-wang commented on the pull request:
https://github.com/apache/spark/pull/5612#issuecomment-95456832
select a1 from t1 left semi join t3 on t1.a1 = t3.a3 and t1.a1>t3.b3 - 100;
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--- Diff:
yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala
---
@@ -0,0 +1,203 @@
+/*
+ * Licensed to the Apach
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--- Diff:
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@@ -121,8 +125,7 @@ class CoarseGrainedSched
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--- Diff:
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@@ -0,0 +1,203 @@
+/*
+ * Licensed to the Apach
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+/*
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Github user vinodkc commented on the pull request:
https://github.com/apache/spark/pull/5633#issuecomment-95446962
@marmbrus,
Have you tried MySQL and Postgres integration tests for this PR ?
https://github.com/apache/spark/pull/5543
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Github user chenghao-intel commented on the pull request:
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Assume we have data t1(2), t3((2,2), (2,1))
`select a1 from t1 left semi join t3 on t1.a1 = t3.a3 and t1.a1>=t3.b3`
will only outputs 2.
However, if we
Github user chenghao-intel commented on a diff in the pull request:
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--- Diff: sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala ---
@@ -36,6 +36,8 @@ private[spark] object SQLConf {
val PARQUET_INT96_AS_T
Github user liancheng commented on the pull request:
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LGTM pending Jenkins.
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--- Diff: sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala ---
@@ -36,6 +36,8 @@ private[spark] object SQLConf {
val PARQUET_INT96_AS_TIMESTAMP
Github user mengxr commented on the pull request:
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LGTM
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GitHub user mengxr opened a pull request:
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[SPARK-7070][MLLIB] LDA.setBeta should call setTopicConcentration.
@jkbradley
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Github user scwf commented on the pull request:
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in this case `select a1 from t1 left semi join t3 on t1.a1 = t3.a3` will
not go into hash join, it should go with left semi join since there is no
additional condition
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Github user adrian-wang commented on the pull request:
https://github.com/apache/spark/pull/5612#issuecomment-95440920
This is still not right...
table t1 (a1 int)
table t3 (a3 int, b3 int)
t1: {1,2,3}
t3: {(2,1),(2,2),(2,2)}
select a1 from t1 left semi join t3
Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/5646#discussion_r28937160
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala ---
@@ -48,8 +48,10 @@ private[parquet] case class ParquetTypeInfo(
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--- Diff: sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala ---
@@ -36,6 +36,8 @@ private[spark] object SQLConf {
val PARQUET_INT96_AS_T
Github user scwf commented on the pull request:
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Yes, i understand that when no additional condition it go the old way. i
mean when there are additional conditions, your broadcasting hashmap may be
much bigger since you al
Github user liancheng commented on a diff in the pull request:
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--- Diff:
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@@ -48,8 +48,10 @@ private[parquet] case class ParquetTypeInfo(
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--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala ---
@@ -190,13 +205,11 @@ object DataType {
equalsIgnoreNullability
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@@ -41,6 +41,21 @@ import org.apache.spark.util.Utils
object DataType {
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GitHub user rxin opened a pull request:
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[MLlib] Add support for BooleanType to VectorAssembler.
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[SPARK-6612] [MLLib] [PySpark] Python KMeans parity
The following items are added to Python kmeans:
kmeans - setEpsilon, setInitializationSteps
KMeansModel - computeCost, k
You can me
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[SPARK-7068][SQL] Remove PrimitiveType
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--- Diff: sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala ---
@@ -36,6 +36,8 @@ private[spark] object SQLConf {
val PARQUET_INT96_AS_TIMESTAMP
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Github user tdas commented on the pull request:
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@marmbrus Mind if I merge this? This is blocking a number of PRs.
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Github user tmyklebu commented on the pull request:
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Do the MySQL and Postgres integration tests both pass with this change?
It's not a security issue per se. The trouble is that JDBC's security
stuff "fails close
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Jenkins, retest this please.
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Github user pwendell commented on the pull request:
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If you have time to do an iteration in the next day or two, it would be
helpful. We can continue to go back and fourth ideally over the next week
to have it ready
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https://github.com/apache/spark/pull/5612#issuecomment-95423015
table t1 (a1 int)
table t2 (a2 int)
t1: {1,2,3}
t2: {2,2,2}
select a1 from t1 left semi join t2 on t1.a1 = t2.a2;
should output {2}
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/5643#issuecomment-95422651
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Github user adrian-wang commented on the pull request:
https://github.com/apache/spark/pull/5643#issuecomment-95422614
@scwf Of course we can go the old way when there's no additional conditions.
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Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/5641#issuecomment-95422599
Jenkins, test this please.
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Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/5633#issuecomment-95422532
This is how we usually do class loading, but IIRC, there is an issue with
certain JDBC drivers where they need to be loaded from the primordial
classloader or else there
Github user punya commented on the pull request:
https://github.com/apache/spark/pull/5547#issuecomment-95420852
Also, if you're looking for a reasonable path to panning/zooming, you might
want to take a look at http://plottablejs.org/.
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/5643#issuecomment-95416494
I am not sure it is suitable to broadcast a hashmap contains key and
related rows, this maybe much bigger than the old hashset, may cause OOM issue.
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/5612#issuecomment-95415298
>>>
If you do shuffleHashJoin and then filter the result, you may get wrong
results for the join if the right table contains rows with same key.
can you give a e
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4980#issuecomment-95413864
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Github user baishuo commented on the pull request:
https://github.com/apache/spark/pull/4980#issuecomment-95413614
thank you @srowen
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Github user zhzhan commented on the pull request:
https://github.com/apache/spark/pull/5637#issuecomment-95413475
Retest it please
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Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/5645#issuecomment-95413174
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Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/5645#issuecomment-95413177
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Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/5645#issuecomment-95413168
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