Github user witgo commented on the pull request:
https://github.com/apache/spark/pull/828#issuecomment-43589674
[The
code](https://github.com/witgo/spark/commit/6d7f2408a40bf4bb2889bf66fa61bced782cdefc#diff-2b593e0b4bd6eddab37f04968baa826c)
will make the checkpoint directory larger
Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/835#issuecomment-43592654
Looks good TD, thanks for doing this.
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Github user witgo closed the pull request at:
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Github user ueshin commented on the pull request:
https://github.com/apache/spark/pull/825#issuecomment-43599450
@rxin Thank you for your comment.
I checked the code #734, not deeply yet, though.
It seems like broadcast hash join is used only for `Inner` join so
broadcast
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GitHub user ueshin opened a pull request:
https://github.com/apache/spark/pull/836
[SPARK-1889] [SQL] Apply splitConjunctivePredicates to join condition while
finding join ke...
...ys.
When tables are equi-joined by multiple-keys `HashJoin` should be used, but
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Github user ghidi commented on the pull request:
https://github.com/apache/spark/pull/821#issuecomment-43603009
I changed Thread.getContextClassLoader with
Utils.getContextOrSparkClassLoader.
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Github user tdas commented on the pull request:
https://github.com/apache/spark/pull/821#issuecomment-43604360
Hey @ghidi
Sorry I should have mentioned. In order to speed up the process (so that I
can cut another RC for Spark 1.0), I cloned your branch and made the fix
GitHub user adrian-wang opened a pull request:
https://github.com/apache/spark/pull/837
add support for left semi join
Just submit another solution for #395
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Github user adrian-wang commented on the pull request:
https://github.com/apache/spark/pull/837#issuecomment-43605087
This is a solution with #418 from @marmbrus .
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Can one of the admins verify this patch?
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Github user witgo commented on the pull request:
https://github.com/apache/spark/pull/828#issuecomment-43608181
@mateiz @mengxr
I added a new operation `cachePoint` of RDD
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Github user cloud-fan commented on the pull request:
https://github.com/apache/spark/pull/791#issuecomment-43611475
As we know, memory store is used for add, read, remove blocks. Reading and
removing is quite simple, so let's focus on adding.
Adding may trigger dropping action,
Github user mridulm commented on the pull request:
https://github.com/apache/spark/pull/791#issuecomment-43618603
It is not MT safe because the PR is checking/modifiying shared state (like
dropping variable) in an unsafe manner.
I will comment in detail on the patch later today
Github user mridulm commented on a diff in the pull request:
https://github.com/apache/spark/pull/791#discussion_r12840780
--- Diff: core/src/main/scala/org/apache/spark/storage/MemoryStore.scala ---
@@ -166,45 +166,51 @@ private class MemoryStore(blockManager: BlockManager,
Github user mridulm commented on a diff in the pull request:
https://github.com/apache/spark/pull/791#discussion_r12840885
--- Diff: core/src/main/scala/org/apache/spark/storage/MemoryStore.scala ---
@@ -243,10 +250,13 @@ private class MemoryStore(blockManager: BlockManager,
Github user mridulm commented on a diff in the pull request:
https://github.com/apache/spark/pull/791#discussion_r12841208
--- Diff: core/src/main/scala/org/apache/spark/storage/MemoryStore.scala ---
@@ -166,45 +166,51 @@ private class MemoryStore(blockManager: BlockManager,
Github user tdas commented on the pull request:
https://github.com/apache/spark/pull/838#issuecomment-43654286
@marmbrust
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GitHub user tdas opened a pull request:
https://github.com/apache/spark/pull/838
[Hotfix] Blacklisted flaky HiveCompatibility test
`lateral_view_outer` query sometimes returns a different set of 10 rows.
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GitHub user andrewor14 opened a pull request:
https://github.com/apache/spark/pull/839
[Minor] Move JdbcRDDSuite to the correct package
It was in the wrong package
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Github user witgo commented on the pull request:
https://github.com/apache/spark/pull/828#issuecomment-43656940
Another [solution](https://github.com/witgo/spark/compare/cachePoint).
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Github user asfgit closed the pull request at:
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Github user douglaz commented on the pull request:
https://github.com/apache/spark/pull/813#issuecomment-43659430
It isn't just about lines of code, it is about pollution of code using
`asInstanceOf` and runtime errors because of this and wrong pattern matching on
Sequences.
Github user markhamstra commented on the pull request:
https://github.com/apache/spark/pull/813#issuecomment-43663175
To throw another wrench into the Union analogy, there is also the
little-used SparkContext#union, which has signatures for both Seq[RDD[T]] and
varags RDD[T].
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GitHub user codedeft opened a pull request:
https://github.com/apache/spark/pull/840
Adding OWL-QN optimizer for L1 regularizations. It can also handle L2 re...
Adding OWL-QN optimizer for L1 regularizations. It can also handle L2 and
L1 regularizations together (balanced with
Github user codedeft commented on the pull request:
https://github.com/apache/spark/pull/840#issuecomment-43665097
jira link :
https://issues.apache.org/jira/browse/SPARK-1892
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Can one of the admins verify this patch?
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Github user codedeft commented on the pull request:
https://github.com/apache/spark/pull/840#issuecomment-43666271
To clarify - it requires the latest breeze. The OWL-QN in breeze had bugs,
which I fixed. I'm not sure if David's published an official release yet but
it's in the
Github user tdas commented on the pull request:
https://github.com/apache/spark/pull/838#issuecomment-43667509
Jenkins, test this again.
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Github user codedeft commented on the pull request:
https://github.com/apache/spark/pull/840#issuecomment-43667582
I'll try to get David to publish the latest breeze and change the project
file to reference the latest breeze.
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GitHub user kanzhang opened a pull request:
https://github.com/apache/spark/pull/841
[SPARK-1822] SchemaRDD.count() should use optimizer
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Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/838#issuecomment-43672003
Jenkins, retest this please.
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Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/841#issuecomment-43673656
@marmbrus I tried to implement the formula you gave on the mailing list.
Not sure if I missed anything. Pls take a look. Note I changed Count() to
return Long to match
Github user ash211 commented on the pull request:
https://github.com/apache/spark/pull/841#issuecomment-43674365
Thanks for the contribution! Could use it in my own workflows.
Python ints are signed 32 bit numbers right? Should make that a long
explicitly unless python
Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/841#issuecomment-43675504
@ash211 In Python 2.X, it does promote an Int to Long when overflowing (it
still matters in doctests, where you have to be explicit about the result value
is 3 or 3L).
Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/760#issuecomment-43680692
IMHO, slicing a sequence shouldn't change its element values
(floating-point representations), same for ```take``` and ```drop```.
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Github user xiaocai00 commented on a diff in the pull request:
https://github.com/apache/spark/pull/734#discussion_r12868794
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala ---
@@ -142,6 +136,68 @@ case class HashJoin(
/**
* ::
GitHub user andrewor14 opened a pull request:
https://github.com/apache/spark/pull/842
[Minor] Correct example of creating a new SparkConf
The example code on the configuration page currently does not compile.
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Github user jaceklaskowski commented on a diff in the pull request:
https://github.com/apache/spark/pull/830#discussion_r12869294
--- Diff: docs/streaming-programming-guide.md ---
@@ -83,21 +82,21 @@ import org.apache.spark.streaming.api._
val ssc = new StreamingContext(local,
Github user jaceklaskowski commented on a diff in the pull request:
https://github.com/apache/spark/pull/830#discussion_r12870111
--- Diff: docs/streaming-programming-guide.md ---
@@ -83,21 +82,21 @@ import org.apache.spark.streaming.api._
val ssc = new StreamingContext(local,
Github user jaceklaskowski commented on a diff in the pull request:
https://github.com/apache/spark/pull/830#discussion_r12870184
--- Diff: docs/streaming-programming-guide.md ---
@@ -105,23 +104,22 @@ generating multiple new records from each record in
the source DStream. In this
Github user jaceklaskowski commented on a diff in the pull request:
https://github.com/apache/spark/pull/830#discussion_r12870325
--- Diff: docs/streaming-programming-guide.md ---
@@ -306,12 +304,16 @@ need to know to write your streaming applications.
## Linking
To
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GitHub user smungee opened a pull request:
https://github.com/apache/spark/pull/843
[SPARK-1250] Fixed misleading comments in bin/pyspark, bin/spark-class
Fixed a couple of misleading comments in bin/pyspark and bin/spark-class.
The comments make it seem like the script is looking
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Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/831#issuecomment-43694156
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Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/830#discussion_r12877300
--- Diff: docs/streaming-programming-guide.md ---
@@ -306,12 +305,16 @@ need to know to write your streaming applications.
## Linking
To write
Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/830#discussion_r12877362
--- Diff: docs/streaming-programming-guide.md ---
@@ -579,7 +582,7 @@ This is applied on a DStream containing words (say, the
`pairs` DStream containi
1)`
Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/830#discussion_r12877361
--- Diff: docs/streaming-programming-guide.md ---
@@ -355,21 +358,21 @@ object has to be created, which is the main entry
point of all Spark Streaming f
A
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