spark on yarn - yarn-client mode doesn't always exit immediately

https://spark-project.atlassian.net/browse/SPARK-1049

If you run in the yarn-client mode but you don't get all the workers you requested right away and then you exit your application, the application master stays around until it gets the number of workers you initially requested. This is a waste of resources.  The AM should exit immediately upon the client going away.

This fix simply checks to see if the driver closed while its waiting for the initial # of workers.

Author: Thomas Graves <tgraves@apache.org>

== Merge branch commits ==

commit 03f40a62584b6bdd094ba91670cd4aa6afe7cd81
Author: Thomas Graves <tgraves@apache.org>
Date:   Fri Jan 31 11:23:10 2014 -0600

    spark on yarn - yarn-client mode doesn't always exit immediately

remove actorToWorker in master.scala, which is actually not used

actorToWorker is actually not used in the code....just remove it

Author: CodingCat <zhunansjtu@gmail.com>

== Merge branch commits ==

commit 52656c2d4bbf9abcd8bef65d454badb9cb14a32c
Author: CodingCat <zhunansjtu@gmail.com>
Date:   Thu Feb 6 00:28:26 2014 -0500

    remove actorToWorker in master.scala, which is actually not used

Fixed warnings in test compilation.

This commit fixes two problems: a redundant import, and a
deprecated function.

Author: Kay Ousterhout <kayousterhout@gmail.com>

== Merge branch commits ==

commit da9d2e13ee4102bc58888df0559c65cb26232a82
Author: Kay Ousterhout <kayousterhout@gmail.com>
Date:   Wed Feb 5 11:41:51 2014 -0800

    Fixed warnings in test compilation.

    This commit fixes two problems: a redundant import, and a
    deprecated function.

Fix line end character stripping for Windows

LogQuery Spark example would produce unwanted result when run on Windows platform because of different, platform specific trailing line end characters (not only \n but \r too).

This fix makes use of Scala's standard library string functions to properly strip all trailing line end characters, letting Scala handle the platform specific stuff.

Author: Stevo Slavić <sslavic@gmail.com>

== Merge branch commits ==

commit 1e43ba0ea773cc005cf0aef78b6c1755f8e88b27
Author: Stevo Slavić <sslavic@gmail.com>
Date:   Wed Feb 5 14:48:29 2014 +0100

    Fix line end character stripping for Windows

    LogQuery Spark example would produce unwanted result when run on Windows platform because of different, platform specific trailing line end characters (not only \n but \r too).

    This fix makes use of Scala's standard library string functions to properly strip all trailing line end characters, letting Scala handle the platform specific stuff.

Fixed wrong path to compute-classpath.cmd

compute-classpath.cmd is in bin, not in sbin directory

Author: Stevo Slavić <sslavic@gmail.com>

== Merge branch commits ==

commit 23deca32b69e9429b33ad31d35b7e1bfc9459f59
Author: Stevo Slavić <sslavic@gmail.com>
Date:   Tue Feb 4 15:01:47 2014 +0100

    Fixed wrong path to compute-classpath.cmd

    compute-classpath.cmd is in bin, not in sbin directory

Fixed typo in scaladoc

Author: Stevo Slavić <sslavic@gmail.com>

== Merge branch commits ==

commit 0a77f789e281930f4168543cc0d3b3ffbf5b3764
Author: Stevo Slavić <sslavic@gmail.com>
Date:   Tue Feb 4 15:30:27 2014 +0100

    Fixed typo in scaladoc

 Refactor RDD sampling and add randomSplit to RDD (update)

Replace SampledRDD by PartitionwiseSampledRDD, which accepts a RandomSampler instance as input. The current sample with/without replacement can be easily integrated via BernoulliSampler and PoissonSampler. The benefits are:

1) RDD.randomSplit is implemented in the same way, related to https://github.com/apache/incubator-spark/pull/513
2) Stratified sampling and importance sampling can be implemented in the same manner as well.

Unit tests are included for samplers and RDD.randomSplit.

This should performance better than my previous request where the BernoulliSampler creates many Iterator instances:
https://github.com/apache/incubator-spark/pull/513

Author: Xiangrui Meng <meng@databricks.com>

== Merge branch commits ==

commit e8ce957e5f0a600f2dec057924f4a2ca6adba373
Author: Xiangrui Meng <meng@databricks.com>
Date:   Mon Feb 3 12:21:08 2014 -0800

    more docs to PartitionwiseSampledRDD

commit fbb4586d0478ff638b24bce95f75ff06f713d43b
Author: Xiangrui Meng <meng@databricks.com>
Date:   Mon Feb 3 00:44:23 2014 -0800

    move XORShiftRandom to util.random and use it in BernoulliSampler

commit 987456b0ee8612fd4f73cb8c40967112dc3c4c2d
Author: Xiangrui Meng <meng@databricks.com>
Date:   Sat Feb 1 11:06:59 2014 -0800

    relax assertions in SortingSuite because the RangePartitioner has large variance in this case

commit 3690aae416b2dc9b2f9ba32efa465ba7948477f4
Author: Xiangrui Meng <meng@databricks.com>
Date:   Sat Feb 1 09:56:28 2014 -0800

    test split ratio of RDD.randomSplit

commit 8a410bc933a60c4d63852606f8bbc812e416d6ae
Author: Xiangrui Meng <meng@databricks.com>
Date:   Sat Feb 1 09:25:22 2014 -0800

    add a test to ensure seed distribution and minor style update

commit ce7e866f674c30ab48a9ceb09da846d5362ab4b6
Author: Xiangrui Meng <meng@databricks.com>
Date:   Fri Jan 31 18:06:22 2014 -0800

    minor style change

commit 750912b4d77596ed807d361347bd2b7e3b9b7a74
Author: Xiangrui Meng <meng@databricks.com>
Date:   Fri Jan 31 18:04:54 2014 -0800

    fix some long lines

commit c446a25c38d81db02821f7f194b0ce5ab4ed7ff5
Author: Xiangrui Meng <meng@databricks.com>
Date:   Fri Jan 31 17:59:59 2014 -0800

    add complement to BernoulliSampler and minor style changes

commit dbe2bc2bd888a7bdccb127ee6595840274499403
Author: Xiangrui Meng <meng@databricks.com>
Date:   Fri Jan 31 17:45:08 2014 -0800

    switch to partition-wise sampling for better performance

commit a1fca5232308feb369339eac67864c787455bb23
Merge: ac712e48 cf6128f
Author: Xiangrui Meng <meng@databricks.com>
Date:   Fri Jan 31 16:33:09 2014 -0800

    Merge branch 'sample' of github.com:mengxr/incubator-spark into sample

commit cf6128fb672e8c589615adbd3eaa3cbdb72bd461
Author: Xiangrui Meng <meng@databricks.com>
Date:   Sun Jan 26 14:40:07 2014 -0800

    set SampledRDD deprecated in 1.0

commit f430f847c3df91a3894687c513f23f823f77c255
Author: Xiangrui Meng <meng@databricks.com>
Date:   Sun Jan 26 14:38:59 2014 -0800

    update code style

commit a8b5e2021a9204e318c80a44d00c5c495f1befb6
Author: Xiangrui Meng <meng@databricks.com>
Date:   Sun Jan 26 12:56:27 2014 -0800

    move package random to util.random

commit ab0fa2c4965033737a9e3a9bf0a59cbb0df6a6f5
Author: Xiangrui Meng <meng@databricks.com>
Date:   Sun Jan 26 12:50:35 2014 -0800

    add Apache headers and update code style

commit 985609fe1a55655ad11966e05a93c18c138a403d
Author: Xiangrui Meng <meng@databricks.com>
Date:   Sun Jan 26 11:49:25 2014 -0800

    add new lines

commit b21bddf29850a2c006a868869b8f91960a029322
Author: Xiangrui Meng <meng@databricks.com>
Date:   Sun Jan 26 11:46:35 2014 -0800

    move samplers to random.IndependentRandomSampler and add tests

commit c02dacb4a941618e434cefc129c002915db08be6
Author: Xiangrui Meng <meng@databricks.com>
Date:   Sat Jan 25 15:20:24 2014 -0800

    add RandomSampler

commit 8ff7ba3c5cf1fc338c29ae8b5fa06c222640e89c
Author: Xiangrui Meng <meng@databricks.com>
Date:   Fri Jan 24 13:23:22 2014 -0800

    init impl of IndependentlySampledRDD

Remove explicit conversion to PairRDDFunctions in cogroup()

As SparkContext._ is already imported, using the implicit conversion appears to make the code much cleaner. Perhaps there was some sinister reason for doing the conversion explicitly, however.

Author: Aaron Davidson <aaron@databricks.com>

== Merge branch commits ==

commit aa4a63f1bfd5b5178fe67364dd7ce4d84c357996
Author: Aaron Davidson <aaron@databricks.com>
Date:   Sun Feb 2 23:48:04 2014 -0800

    Remove explicit conversion to PairRDDFunctions in cogroup()

    As SparkContext._ is already imported, using the implicit conversion
    appears to make the code much cleaner. Perhaps there was some sinister
    reason for doing the converion explicitly, however.

Change the ⇒ character (maybe from scalariform) to => in Scala code for style consistency

Looks like there are some ⇒ Unicode character (maybe from scalariform) in Scala code.
This PR is to change it to => to get some consistency on the Scala code.

If we want to use ⇒ as default we could use sbt plugin scalariform to make sure all Scala code has ⇒ instead of =>

And remove unused imports found in TwitterInputDStream.scala while I was there =)

Author: Henry Saputra <hsaputra@apache.org>

== Merge branch commits ==

commit 29c1771d346dff901b0b778f764e6b4409900234
Author: Henry Saputra <hsaputra@apache.org>
Date:   Sat Feb 1 22:05:16 2014 -0800

    Change the ⇒ character (maybe from scalariform) to => in Scala code for style consistency.

Add GraphX to assembly/pom.xml

Author: Ankur Dave <ankurdave@gmail.com>

== Merge branch commits ==

commit bb0b33ef9eb1b3d4a4fc283d9abb2ece4abcac23
Author: Ankur Dave <ankurdave@gmail.com>
Date:   Fri Jan 31 15:24:52 2014 -0800

    Add GraphX to assembly/pom.xml

Added spark.shuffle.file.buffer.kb to configuration doc.

Author: Reynold Xin <rxin@apache.org>

== Merge branch commits ==

commit 0eea1d761ff772ff89be234e1e28035d54e5a7de
Author: Reynold Xin <rxin@apache.org>
Date:   Wed Jan 29 14:40:48 2014 -0800

    Added spark.shuffle.file.buffer.kb to configuration doc.

Issue with failed worker registrations

I've been going through the spark source after having some odd issues with workers dying and not coming back. After some digging (I'm very new to scala and spark) I believe I've found a worker registration issue. It looks to me like a failed registration follows the same code path as a successful registration which end up with workers believing they are connected (since they received a `RegisteredWorker` event) even tho they are not registered on the Master.

This is a quick fix that I hope addresses this issue (assuming I didn't completely miss-read the code and I'm about to look like a silly person :P)

I'm opening this pr now to start a chat with you guys while I do some more testing on my side :)

Author: Erik Selin <erik.selin@jadedpixel.com>

== Merge branch commits ==

commit 973012f8a2dcf1ac1e68a69a2086a1b9a50f401b
Author: Erik Selin <erik.selin@jadedpixel.com>
Date:   Tue Jan 28 23:36:12 2014 -0500

    break logwarning into two lines to respect line character limit.

commit e3754dc5b94730f37e9806974340e6dd93400f85
Author: Erik Selin <erik.selin@jadedpixel.com>
Date:   Tue Jan 28 21:16:21 2014 -0500

    add log warning when worker registration fails due to attempt to re-register on same address.

commit 14baca241fa7823e1213cfc12a3ff2a9b865b1ed
Author: Erik Selin <erik.selin@jadedpixel.com>
Date:   Wed Jan 22 21:23:26 2014 -0500

    address code style comment

commit 71c0d7e6f59cd378d4e24994c21140ab893954ee
Author: Erik Selin <erik.selin@jadedpixel.com>
Date:   Wed Jan 22 16:01:42 2014 -0500

    Make a failed registration not persist, not send a `RegisteredWordker` event and not run `schedule` but rather send a `RegisterWorkerFailed` message to the worker attempting to register.

Updated Spark Streaming Programming Guide

Here is the updated version of the Spark Streaming Programming Guide. This is still a work in progress, but the major changes are in place. So feedback is most welcome.

In general, I have tried to make the guide to easier to understand even if the reader does not know much about Spark. The updated website is hosted here -

http://www.eecs.berkeley.edu/~tdas/spark_docs/streaming-programming-guide.html

The major changes are:
- Overview illustrates the usecases of Spark Streaming - various input sources and various output sources
- An example right after overview to quickly give an idea of what Spark Streaming program looks like
- Made Java API and examples a first class citizen like Scala by using tabs to show both Scala and Java examples (similar to AMPCamp tutorial's code tabs)
- Highlighted the DStream operations updateStateByKey and transform because of their powerful nature
- Updated driver node failure recovery text to highlight automatic recovery in Spark standalone mode
- Added information about linking and using the external input sources like Kafka and Flume
- In general, reorganized the sections to better show the Basic section and the more advanced sections like Tuning and Recovery.

Todos:
- Links to the docs of external Kafka, Flume, etc
- Illustrate window operation with figure as well as example.

Author: Tathagata Das <tathagata.das1565@gmail.com>

== Merge branch commits ==

commit 18ff10556570b39d672beeb0a32075215cfcc944
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date:   Tue Jan 28 21:49:30 2014 -0800

    Fixed a lot of broken links.

commit 34a5a6008dac2e107624c7ff0db0824ee5bae45f
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date:   Tue Jan 28 18:02:28 2014 -0800

    Updated github url to use SPARK_GITHUB_URL variable.

commit f338a60ae8069e0a382d2cb170227e5757cc0b7a
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date:   Mon Jan 27 22:42:42 2014 -0800

    More updates based on Patrick and Harvey's comments.

commit 89a81ff25726bf6d26163e0dd938290a79582c0f
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date:   Mon Jan 27 13:08:34 2014 -0800

    Updated docs based on Patricks PR comments.

commit d5b6196b532b5746e019b959a79ea0cc013a8fc3
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date:   Sun Jan 26 20:15:58 2014 -0800

    Added spark.streaming.unpersist config and info on StreamingListener interface.

commit e3dcb46ab83d7071f611d9b5008ba6bc16c9f951
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date:   Sun Jan 26 18:41:12 2014 -0800

    Fixed docs on StreamingContext.getOrCreate.

commit 6c29524639463f11eec721e4d17a9d7159f2944b
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date:   Thu Jan 23 18:49:39 2014 -0800

    Added example and figure for window operations, and links to Kafka and Flume API docs.

commit f06b964a51bb3b21cde2ff8bdea7d9785f6ce3a9
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date:   Wed Jan 22 22:49:12 2014 -0800

    Fixed missing endhighlight tag in the MLlib guide.

commit 036a7d46187ea3f2a0fb8349ef78f10d6c0b43a9
Merge: eab351d a1cd1851
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date:   Wed Jan 22 22:17:42 2014 -0800

    Merge remote-tracking branch 'apache/master' into docs-update

commit eab351d05c0baef1d4b549e1581310087158d78d
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date:   Wed Jan 22 22:17:15 2014 -0800

    Update Spark Streaming Programming Guide.

Switch from MUTF8 to UTF8 in PySpark serializers.

This fixes SPARK-1043, a bug introduced in 0.9.0 where PySpark couldn't serialize strings > 64kB.

This fix was written by @tyro89 and @bouk in #512. This commit squashes and rebases their pull request in order to fix some merge conflicts.

This fixes SPARK-1043, a bug introduced in 0.9.0
where PySpark couldn't serialize strings > 64kB.

This fix was written by @tyro89 and @bouk in #512.
This commit squashes and rebases their pull request
in order to fix some merge conflicts.

Allow files added through SparkContext.addFile() to be overwritten

This is useful for the cases when a file needs to be refreshed and downloaded by the executors periodically. For example, a possible use case is: the driver periodically renews a Hadoop delegation token and writes it to a token file. The token file needs to be downloaded by the executors whenever it gets renewed. However, the current implementation throws an exception when the target file exists and its contents do not match those of the new source. This PR adds an option to allow files to be overwritten to support use cases similar to the above.

modified SparkPluginBuild.scala to use https protocol for accessing gith...

We cannot build Spark behind a proxy although we execute sbt with -Dhttp(s).proxyHost -Dhttp(s).proxyPort -Dhttp(s).proxyUser -Dhttp(s).proxyPassword options.
It's because of using git protocol to clone junit_xml_listener.git.
I could build after modifying SparkPluginBuild.scala.

I reported this issue to JIRA.
https://spark-project.atlassian.net/browse/SPARK-1046

Replace the check for None Option with isDefined and isEmpty in Scala code

Propose to replace the Scala check for Option "!= None" with Option.isDefined and "=== None" with Option.isEmpty.

I think this, using method call if possible then operator function plus argument, will make the Scala code easier to read and understand.

Pass compile and tests.

Choose initial user/item vectors uniformly on the unit sphere

...rather than within the unit square to possibly avoid bias in the initial state and improve convergence.

The current implementation picks the N vector elements uniformly at random from [0,1). This means they all point into one quadrant of the vector space. As N gets just a little large, the vector tend strongly to point into the "corner", towards (1,1,1...,1). The vectors are not unit vectors either.

I suggest choosing the elements as Gaussian ~ N(0,1) and normalizing. This gets you uniform random choices on the unit sphere which is more what's of interest here. It has worked a little better for me in the past.

This is pretty minor but wanted to warm up suggesting a few tweaks to ALS.
Please excuse my Scala, pretty new to it.

Author: Sean Owen <sowen@cloudera.com>

== Merge branch commits ==

commit 492b13a7469e5a4ed7591ee8e56d8bd7570dfab6
Author: Sean Owen <sowen@cloudera.com>
Date:   Mon Jan 27 08:05:25 2014 +0000

    Style: spaces around binary operators

commit ce2b5b5a4fefa0356875701f668f01f02ba4d87e
Author: Sean Owen <sowen@cloudera.com>
Date:   Sun Jan 19 22:50:03 2014 +0000

    Generate factors with all positive components, per discussion in https://github.com/apache/incubator-spark/pull/460

commit b6f7a8a61643a8209e8bc662e8e81f2d15c710c7
Author: Sean Owen <sowen@cloudera.com>
Date:   Sat Jan 18 15:54:42 2014 +0000

    Choose initial user/item vectors uniformly on the unit sphere rather than within the unit square to possibly avoid bias in the initial state and improve convergence

Fix PySpark hang when input files are deleted (SPARK-1025)

This pull request addresses [SPARK-1025](https://spark-project.atlassian.net/browse/SPARK-1025), an issue where PySpark could hang if its input files were deleted.

Fix ClassCastException in JavaPairRDD.collectAsMap() (SPARK-1040)

This fixes [SPARK-1040](https://spark-project.atlassian.net/browse/SPARK-1040), an issue where JavaPairRDD.collectAsMap() could sometimes fail with ClassCastException.  I applied the same fix to the Spark Streaming Java APIs.  The commit message describes the fix in more detail.

I also increased the verbosity of JUnit test output under SBT to make it easier to verify that the Java tests are actually running.

This fixes an issue where collectAsMap() could
fail when called on a JavaPairRDD that was derived
by transforming a non-JavaPairRDD.

The root problem was that we were creating the
JavaPairRDD's ClassTag by casting a
ClassTag[AnyRef] to a ClassTag[Tuple2[K2, V2]].
To fix this, I cast a ClassTag[Tuple2[_, _]]
instead, since this actually produces a ClassTag
of the appropriate type because ClassTags don't
capture type parameters:

scala> implicitly[ClassTag[Tuple2[_, _]]] == implicitly[ClassTag[Tuple2[Int, Int]]]
res8: Boolean = true

scala> implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[Tuple2[Int, Int]]] == implicitly[ClassTag[Tuple2[Int, Int]]]
res9: Boolean = false

Upgrade junit-interface plugin from 0.9 to 0.10.

I noticed that the JavaAPISuite tests didn't
appear to display any output locally or under
Jenkins, making it difficult to know whether they
were running.  This change increases the verbosity
to more closely match the ScalaTest tests.

Deprecate mapPartitionsWithSplit in PySpark (SPARK-1026)

This commit deprecates `mapPartitionsWithSplit` in PySpark (see [SPARK-1026](https://spark-project.atlassian.net/browse/SPARK-1026) and removes the remaining references to it from the docs.

Also, replace the last reference to it in the docs.

This fixes SPARK-1026.

Fix bug on read-side of external sort when using Snappy.

This case wasn't handled correctly and this patch fixes it.

Remove Hadoop object cloning and warn users making Hadoop RDD's.

The code introduced in #359 used Hadoop's WritableUtils.clone() to
duplicate objects when reading from Hadoop files. Some users have
reported exceptions when cloning data in various file formats,
including Avro and another custom format.

This patch removes that functionality to ensure stability for the
0.9 release. Instead, it puts a clear warning in the documentation
that copying may be necessary for Hadoop data sets.

Fix two bugs in PySpark cartesian(): SPARK-978 and SPARK-1034

This pull request fixes two bugs in PySpark's `cartesian()` method:

- [SPARK-978](https://spark-project.atlassian.net/browse/SPARK-978): PySpark's cartesian method throws ClassCastException exception
- [SPARK-1034](https://spark-project.atlassian.net/browse/SPARK-1034): Py4JException on PySpark Cartesian Result

The JIRAs have more details describing the fixes.

This case wasn't handled correctly and this patch fixes it.

The code introduced in #359 used Hadoop's WritableUtils.clone() to
duplicate objects when reading from Hadoop files. Some users have
reported exceptions when cloning data in verious file formats,
including Avro and another custom format.

This patch removes that functionality to ensure stability for the
0.9 release. Instead, it puts a clear warning in the documentation
that copying may be necessary for Hadoop data sets.

Extending Java API coverage

Hi,

I have added three new methods to JavaRDD.

Please review and merge.

Replace commons-math with jblas in SVDPlusPlus