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Github user skonto commented on the pull request:
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I verified the example for spark version spark 1.5.1. It seems to work
fine. It makes possible the bidirectional communication between mesos
master/notebook within a
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Github user tmnd1991 commented on the pull request:
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I explained it on the Jira issue, I'm going to explain it again here:
Since `spark.kryoserializer.buffer.max` defaults to 64MB, I decided to
increase the number of
Github user radekg commented on the pull request:
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@skonto no, the NettyRpcEnv has not been tested yet, let me have a look
today / tomorrow. I'll post an update here.
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Github user srowen commented on the pull request:
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@tmnd1991 Yes, I know. I'm suggesting you explain it not in the JIRA or PR,
but briefly as comments in the code.
`1 << 25` is also 32MB in that it's 2^25. It
Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/9843#discussion_r45962467
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala ---
@@ -218,7 +218,7 @@ private object IDFModel {
newValues(k) =
Github user hvanhovell commented on the pull request:
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I wonder if this is an actuall improvement. Doing back to back projections
also incur a codegen and a runtime cost; which can easily be higher than the
cost of
Github user feynmanliang commented on the pull request:
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LGTM, though I didn't know what was wrong with the old PR so this should
get a second set of eyes
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Github user viirya commented on the pull request:
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@hvanhovell thanks for comment.
It might depend on the real use case. But as I can tell, ScalaUDF will
involve transforming internal row to external
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/9993#discussion_r45965159
--- Diff: sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala ---
@@ -233,6 +236,25 @@ class UDFSuite extends QueryTest with SharedSQLContext
{
Github user tmnd1991 commented on the pull request:
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I adjusted the code as you suggested
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Github user jbonofre commented on the pull request:
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By the way, what do you think about discussing this on the mailing list in
order to see what the users think about this (the Apache way ;)) ?
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[SPARK-11997][SQL] NPE when save a DataFrame as parquet and partitioned by
long column
Check for partition column null-ability while building the partition spec.
You can merge this pull
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@davies Can you please let me know your comments.
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@davies @rxin
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Github user cloud-fan commented on the pull request:
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I think it's hard to tell which one is more expensive, an extra project or
an extra udf evaluation? Maybe we shoud let user to decide it, use `udf((x:
Int) => x + 1).
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@jbonofre all of the messages are sent to reviews@spark.apache.org, for
those that wish to follow. Significant discussions can be pushed over to dev@
or user@, if
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@cloud-fan Hmm, I think that is reverted already?
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Besides, from semantics, I think it is confusing because it is not actually
nondeterministic.
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Thoughts ? Do we discuss this on the mailing lists or it's worth to keep it
here and close this PR ?
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[SPARK-11551][DOC][Example]Replace example code in ml-features.md using
include_example
Made new patch contaning only markdown examples moved to exmaple/folder.
Ony three java code were
Github user hhbyyh commented on the pull request:
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@feynmanliang Oh I forgot to add //Examples on and off in JavaLDAExample in
the old PR.
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[SPARK-12010][SQL] Spark JDBC requires support for column-name-free INSERT
syntax
This PR move the generation of the SQL Insert Statement into JdbcDialect
and allows dialects to add column names
GitHub user liancheng opened a pull request:
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[SPARK-12012] [SQL] Show more comprehensive PhysicalRDD metadata when
visualizing SQL query plan
This PR adds a `private[sql]` method `metadata` to `SparkPlan`, which can
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cc @andrewor14 @yhuai
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Thank you @vanzin for your help. I commited the nits in the documentation.
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GitHub user feynmanliang opened a pull request:
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[SPARK-11960][MLlib][Doc]User guide for streaming tests
CC @jkbradley @mengxr @josepablocam
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Github user BenFradet commented on the pull request:
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If you introduce a new configuration property, don't forget to add it to
the docs.
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[SPARK-11958] [SPARK-11957] [ML] [Doc] SQLTransformer user guide and
example code
SQLTransformer user guide and example code
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Jenkins, test this please.
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--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala ---
@@ -105,7 +105,7 @@ object PCA extends DefaultParamsReadable[PCA] {
@Experimental
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Github user mnd999 commented on the pull request:
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Did this ever make it to spark-packages.org? If not, it's a shame, but
would not be entirely surprising given how this PR was handled.
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GitHub user yinxusen opened a pull request:
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[SPARK-11987] Python API update for ChiSqSelector and QuantileDiscretizer
https://issues.apache.org/jira/browse/SPARK-11987
For `QuantileDiscretizer`, I add a `QuantileDiscretizerModel`
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--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/BisectingKMeans.scala ---
@@ -407,7 +407,7 @@ private object BisectingKMeans extends
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This should be all for visibility review.
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Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/9990#discussion_r45999453
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala ---
@@ -45,9 +45,10 @@ import org.apache.spark.sql.execution.datasources._
import
Github user davies commented on the pull request:
https://github.com/apache/spark/pull/9990#issuecomment-159967811
@zsxwing The one in SQLContext have a `clearActive` APIs, so I think don't
need WeakRef there.
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