SPARK-2269 Refactor mesos scheduler resourceOffers and add unit test

Author: Timothy Chen <tnachen@gmail.com>

Closes #1487 from tnachen/resource_offer_refactor and squashes the following commits:

4ea5dec [Timothy Chen] Rebase from master and address comments
9ccab09 [Timothy Chen] Address review comments
e6494dc [Timothy Chen] Refactor class loading
8207428 [Timothy Chen] Refactor mesos scheduler resourceOffers and add unit test
[Release] Log build output for each distribution
Support cross building for Scala 2.11

Let's give this another go using a version of Hive that shades its JLine dependency.

Author: Prashant Sharma <prashant.s@imaginea.com>
Author: Patrick Wendell <pwendell@gmail.com>

Closes #3159 from pwendell/scala-2.11-prashant and squashes the following commits:

e93aa3e [Patrick Wendell] Restoring -Phive-thriftserver profile and cleaning up build script.
f65d17d [Patrick Wendell] Fixing build issue due to merge conflict
a8c41eb [Patrick Wendell] Reverting dev/run-tests back to master state.
7a6eb18 [Patrick Wendell] Merge remote-tracking branch 'apache/master' into scala-2.11-prashant
583aa07 [Prashant Sharma] REVERT ME: removed hive thirftserver
3680e58 [Prashant Sharma] Revert "REVERT ME: Temporarily removing some Cli tests."
935fb47 [Prashant Sharma] Revert "Fixed by disabling a few tests temporarily."
925e90f [Prashant Sharma] Fixed by disabling a few tests temporarily.
2fffed3 [Prashant Sharma] Exclude groovy from sbt build, and also provide a way for such instances in future.
8bd4e40 [Prashant Sharma] Switched to gmaven plus, it fixes random failures observer with its predecessor gmaven.
5272ce5 [Prashant Sharma] SPARK_SCALA_VERSION related bugs.
2121071 [Patrick Wendell] Migrating version detection to PySpark
b1ed44d [Patrick Wendell] REVERT ME: Temporarily removing some Cli tests.
1743a73 [Patrick Wendell] Removing decimal test that doesn't work with Scala 2.11
f5cad4e [Patrick Wendell] Add Scala 2.11 docs
210d7e1 [Patrick Wendell] Revert "Testing new Hive version with shaded jline"
48518ce [Patrick Wendell] Remove association of Hive and Thriftserver profiles.
e9d0a06 [Patrick Wendell] Revert "Enable thritfserver for Scala 2.10 only"
67ec364 [Patrick Wendell] Guard building of thriftserver around Scala 2.10 check
8502c23 [Patrick Wendell] Enable thritfserver for Scala 2.10 only
e22b104 [Patrick Wendell] Small fix in pom file
ec402ab [Patrick Wendell] Various fixes
0be5a9d [Patrick Wendell] Testing new Hive version with shaded jline
4eaec65 [Prashant Sharma] Changed scripts to ignore target.
5167bea [Prashant Sharma] small correction
a4fcac6 [Prashant Sharma] Run against scala 2.11 on jenkins.
80285f4 [Prashant Sharma] MAven equivalent of setting spark.executor.extraClasspath during tests.
034b369 [Prashant Sharma] Setting test jars on executor classpath during tests from sbt.
d4874cb [Prashant Sharma] Fixed Python Runner suite. null check should be first case in scala 2.11.
6f50f13 [Prashant Sharma] Fixed build after rebasing with master. We should use ${scala.binary.version} instead of just 2.10
e56ca9d [Prashant Sharma] Print an error if build for 2.10 and 2.11 is spotted.
937c0b8 [Prashant Sharma] SCALA_VERSION -> SPARK_SCALA_VERSION
cb059b0 [Prashant Sharma] Code review
0476e5e [Prashant Sharma] Scala 2.11 support with repl and all build changes.
[MLLIB] SPARK-4347: Reducing GradientBoostingSuite run time.

Before:
[info] GradientBoostingSuite:
[info] - Regression with continuous features: SquaredError (22 seconds, 115 milliseconds)
[info] - Regression with continuous features: Absolute Error (19 seconds, 330 milliseconds)
[info] - Binary classification with continuous features: Log Loss (19 seconds, 17 milliseconds)

After:
[info] - Regression with continuous features: SquaredError (7 seconds, 69 milliseconds)
[info] - Regression with continuous features: Absolute Error (4 seconds, 617 milliseconds)
[info] - Binary classification with continuous features: Log Loss (4 seconds, 658 milliseconds)

cc: mengxr, jkbradley

Author: Manish Amde <manish9ue@gmail.com>

Closes #3214 from manishamde/gbt_test_speedup and squashes the following commits:

8994552 [Manish Amde] reducing gbt test run times
[SPARK-3936] Add aggregateMessages, which supersedes mapReduceTriplets

aggregateMessages enables neighborhood computation similarly to mapReduceTriplets, but it introduces two API improvements:

1. Messages are sent using an imperative interface based on EdgeContext rather than by returning an iterator of messages.

2. Rather than attempting bytecode inspection, the required triplet fields must be explicitly specified by the user by passing a TripletFields object. This fixes SPARK-3936.

Additionally, this PR includes the following optimizations for aggregateMessages and EdgePartition:

1. EdgePartition now stores local vertex ids instead of global ids. This avoids hash lookups when looking up vertex attributes and aggregating messages.

2. Internal iterators in aggregateMessages are inlined into a while loop.

In total, these optimizations were tested to provide a 37% speedup on PageRank (uk-2007-05 graph, 10 iterations, 16 r3.2xlarge machines, sped up from 513 s to 322 s).

Subsumes apache/spark#2815. Also fixes SPARK-4173.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #3100 from ankurdave/aggregateMessages and squashes the following commits:

f5b65d0 [Ankur Dave] Address @rxin comments on apache/spark#3054 and apache/spark#3100
1e80aca [Ankur Dave] Add aggregateMessages, which supersedes mapReduceTriplets
194a2df [Ankur Dave] Test triplet iterator in EdgePartition serialization test
e0f8ecc [Ankur Dave] Take activeSet in ExistingEdgePartitionBuilder
c85076d [Ankur Dave] Readability improvements
b567be2 [Ankur Dave] iter.foreach -> while loop
4a566dc [Ankur Dave] Optimizations for mapReduceTriplets and EdgePartition
[SPARK-4355][MLLIB] fix OnlineSummarizer.merge when other.mean is zero

See inline comment about the bug. I also did some code clean-up. dbtsai I moved `update` to a private method of `MultivariateOnlineSummarizer`. I don't think it will cause performance regression, but it would be great if you have some time to test.

Author: Xiangrui Meng <meng@databricks.com>

Closes #3220 from mengxr/SPARK-4355 and squashes the following commits:

5ef601f [Xiangrui Meng] fix OnlineSummarizer.merge when other.mean is zero and some code clean-up
[SPARK-3530][MLLIB] pipeline and parameters with examples

This PR adds package "org.apache.spark.ml" with pipeline and parameters, as discussed on the JIRA. This is a joint work of jkbradley etrain shivaram and many others who helped on the design, also with help from  marmbrus and liancheng on the Spark SQL side. The design doc can be found at:

https://docs.google.com/document/d/1rVwXRjWKfIb-7PI6b86ipytwbUH7irSNLF1_6dLmh8o/edit?usp=sharing

**org.apache.spark.ml**

This is a new package with new set of ML APIs that address practical machine learning pipelines. (Sorry for taking so long!) It will be an alpha component, so this is definitely not something set in stone. The new set of APIs, inspired by the MLI project from AMPLab and scikit-learn, takes leverage on Spark SQL's schema support and execution plan optimization. It introduces the following components that help build a practical pipeline:

1. Transformer, which transforms a dataset into another
2. Estimator, which fits models to data, where models are transformers
3. Evaluator, which evaluates model output and returns a scalar metric
4. Pipeline, a simple pipeline that consists of transformers and estimators

Parameters could be supplied at fit/transform or embedded with components.

1. Param: a strong-typed parameter key with self-contained doc
2. ParamMap: a param -> value map
3. Params: trait for components with parameters

For any component that implements `Params`, user can easily check the doc by calling `explainParams`:

~~~
> val lr = new LogisticRegression
> lr.explainParams
maxIter: max number of iterations (default: 100)
regParam: regularization constant (default: 0.1)
labelCol: label column name (default: label)
featuresCol: features column name (default: features)
~~~

or user can check individual param:

~~~
> lr.maxIter
maxIter: max number of iterations (default: 100)
~~~

**Please start with the example code in test suites and under `org.apache.spark.examples.ml`, where I put several examples:**

1. run a simple logistic regression job

~~~
    val lr = new LogisticRegression()
      .setMaxIter(10)
      .setRegParam(1.0)
    val model = lr.fit(dataset)
    model.transform(dataset, model.threshold -> 0.8) // overwrite threshold
      .select('label, 'score, 'prediction).collect()
      .foreach(println)
~~~

2. run logistic regression with cross-validation and grid search using areaUnderROC (default) as the metric

~~~
    val lr = new LogisticRegression
    val lrParamMaps = new ParamGridBuilder()
      .addGrid(lr.regParam, Array(0.1, 100.0))
      .addGrid(lr.maxIter, Array(0, 5))
      .build()
    val eval = new BinaryClassificationEvaluator
    val cv = new CrossValidator()
      .setEstimator(lr)
      .setEstimatorParamMaps(lrParamMaps)
      .setEvaluator(eval)
      .setNumFolds(3)
    val bestModel = cv.fit(dataset)
~~~

3. run a pipeline that consists of a standard scaler and a logistic regression component

~~~
    val scaler = new StandardScaler()
      .setInputCol("features")
      .setOutputCol("scaledFeatures")
    val lr = new LogisticRegression()
      .setFeaturesCol(scaler.getOutputCol)
    val pipeline = new Pipeline()
      .setStages(Array(scaler, lr))
    val model = pipeline.fit(dataset)
    val predictions = model.transform(dataset)
      .select('label, 'score, 'prediction)
      .collect()
      .foreach(println)
~~~

4. a simple text classification pipeline, which recognizes "spark":

~~~
    val training = sparkContext.parallelize(Seq(
      LabeledDocument(0L, "a b c d e spark", 1.0),
      LabeledDocument(1L, "b d", 0.0),
      LabeledDocument(2L, "spark f g h", 1.0),
      LabeledDocument(3L, "hadoop mapreduce", 0.0)))
    val tokenizer = new Tokenizer()
      .setInputCol("text")
      .setOutputCol("words")
    val hashingTF = new HashingTF()
      .setInputCol(tokenizer.getOutputCol)
      .setOutputCol("features")
    val lr = new LogisticRegression()
      .setMaxIter(10)
    val pipeline = new Pipeline()
      .setStages(Array(tokenizer, hashingTF, lr))
    val model = pipeline.fit(training)
    val test = sparkContext.parallelize(Seq(
      Document(4L, "spark i j k"),
      Document(5L, "l m"),
      Document(6L, "mapreduce spark"),
      Document(7L, "apache hadoop")))
    model.transform(test)
      .select('id, 'text, 'prediction, 'score)
      .collect()
      .foreach(println)
~~~

Java examples are very similar. I put example code that creates a simple text classification pipeline in Scala and Java, where a simple tokenizer is defined as a transformer outside `org.apache.spark.ml`.

**What are missing now and will be added soon:**

1. ~~Runtime check of schemas. So before we touch the data, we will go through the schema and make sure column names and types match the input parameters.~~
2. ~~Java examples.~~
3. ~~Store training parameters in trained models.~~
4. (later) Serialization and Python API.

Author: Xiangrui Meng <meng@databricks.com>

Closes #3099 from mengxr/SPARK-3530 and squashes the following commits:

2cc93fd [Xiangrui Meng] hide APIs as much as I can
34319ba [Xiangrui Meng] use local instead local[2] for unit tests
2524251 [Xiangrui Meng] rename PipelineStage.transform to transformSchema
c9daab4 [Xiangrui Meng] remove mockito version
1397ab5 [Xiangrui Meng] use sqlContext from LocalSparkContext instead of TestSQLContext
6ffc389 [Xiangrui Meng] try to fix unit test
a59d8b7 [Xiangrui Meng] doc updates
977fd9d [Xiangrui Meng] add scala ml package object
6d97fe6 [Xiangrui Meng] add AlphaComponent annotation
731f0e4 [Xiangrui Meng] update package doc
0435076 [Xiangrui Meng] remove ;this from setters
fa21d9b [Xiangrui Meng] update extends indentation
f1091b3 [Xiangrui Meng] typo
228a9f4 [Xiangrui Meng] do not persist before calling binary classification metrics
f51cd27 [Xiangrui Meng] rename default to defaultValue
b3be094 [Xiangrui Meng] refactor schema transform in lr
8791e8e [Xiangrui Meng] rename copyValues to inheritValues and make it do the right thing
51f1c06 [Xiangrui Meng] remove leftover code in Transformer
494b632 [Xiangrui Meng] compure score once
ad678e9 [Xiangrui Meng] more doc for Transformer
4306ed4 [Xiangrui Meng] org imports in text pipeline
6e7c1c7 [Xiangrui Meng] update pipeline
4f9e34f [Xiangrui Meng] more doc for pipeline
aa5dbd4 [Xiangrui Meng] fix typo
11be383 [Xiangrui Meng] fix unit tests
3df7952 [Xiangrui Meng] clean up
986593e [Xiangrui Meng] re-org java test suites
2b11211 [Xiangrui Meng] remove external data deps
9fd4933 [Xiangrui Meng] add unit test for pipeline
2a0df46 [Xiangrui Meng] update tests
2d52e4d [Xiangrui Meng] add @AlphaComponent to package-info
27582a4 [Xiangrui Meng] doc changes
73a000b [Xiangrui Meng] add schema transformation layer
6736e87 [Xiangrui Meng] more doc / remove HasMetricName trait
80a8b5e [Xiangrui Meng] rename SimpleTransformer to UnaryTransformer
62ca2bb [Xiangrui Meng] check param parent in set/get
1622349 [Xiangrui Meng] add getModel to PipelineModel
a0e0054 [Xiangrui Meng] update StandardScaler to use SimpleTransformer
d0faa04 [Xiangrui Meng] remove implicit mapping from ParamMap
c7f6921 [Xiangrui Meng] move ParamGridBuilder test to ParamGridBuilderSuite
e246f29 [Xiangrui Meng] re-org:
7772430 [Xiangrui Meng] remove modelParams add a simple text classification pipeline
b95c408 [Xiangrui Meng] remove implicits add unit tests to params
bab3e5b [Xiangrui Meng] update params
fe0ee92 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-3530
6e86d98 [Xiangrui Meng] some code clean-up
2d040b3 [Xiangrui Meng] implement setters inside each class, add Params.copyValues [ci skip]
fd751fc [Xiangrui Meng] add java-friendly versions of fit and tranform
3f810cd [Xiangrui Meng] use multi-model training api in cv
5b8f413 [Xiangrui Meng] rename model to modelParams
9d2d35d [Xiangrui Meng] test varargs and chain model params
f46e927 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-3530
1ef26e0 [Xiangrui Meng] specialize methods/types for Java
df293ed [Xiangrui Meng] switch to setter/getter
376db0a [Xiangrui Meng] pipeline and parameters
[SPARK-3660][STREAMING] Initial RDD for updateStateByKey transformation

SPARK-3660 : Initial RDD for updateStateByKey transformation

I have added a sample StatefulNetworkWordCountWithInitial inspired by StatefulNetworkWordCount.

Please let me know if any changes are required.

Author: Soumitra Kumar <kumar.soumitra@gmail.com>

Closes #2665 from soumitrak/master and squashes the following commits:

ee8980b [Soumitra Kumar] Fixed copy/paste issue.
304f636 [Soumitra Kumar] Added simpler version of updateStateByKey API with initialRDD and test.
9781135 [Soumitra Kumar] Fixed test, and renamed variable.
3da51a2 [Soumitra Kumar] Adding updateStateByKey with initialRDD API to JavaPairDStream.
2f78f7e [Soumitra Kumar] Merge remote-tracking branch 'upstream/master'
d4fdd18 [Soumitra Kumar] Renamed variable and moved method.
d0ce2cd [Soumitra Kumar] Merge remote-tracking branch 'upstream/master'
31399a4 [Soumitra Kumar] Merge remote-tracking branch 'upstream/master'
4efa58b [Soumitra Kumar] [SPARK-3660][STREAMING] Initial RDD for updateStateByKey transformation
8f40ca0 [Soumitra Kumar] Merge remote-tracking branch 'upstream/master'
dde4271 [Soumitra Kumar] Merge remote-tracking branch 'upstream/master'
fdd7db3 [Soumitra Kumar] Adding support of initial value for state update. SPARK-3660 : Initial RDD for updateStateByKey transformation
[Test] Better exception message from SparkSubmitSuite

Before:
```
Exception in thread "main" java.lang.Exception: Could not load user defined classes inside of executors
	at org.apache.spark.deploy.JarCreationTest$.main(SparkSubmitSuite.scala:471)
	at org.apache.spark.deploy.JarCreationTest.main(SparkSubmitSuite.scala)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
```
After:
```
Exception in thread "main" java.lang.Exception: Could not load user class from jar:
java.lang.UnsupportedClassVersionError: SparkSubmitClassA : Unsupported major.minor version 51.0
	java.lang.ClassLoader.defineClass1(Native Method)
	java.lang.ClassLoader.defineClass(ClassLoader.java:643)
	...
	at org.apache.spark.deploy.JarCreationTest$.main(SparkSubmitSuite.scala:472)
	at org.apache.spark.deploy.JarCreationTest.main(SparkSubmitSuite.scala)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
```

Author: Andrew Or <andrew@databricks.com>

Closes #3212 from andrewor14/submit-suite-message and squashes the following commits:

7779248 [Andrew Or] Format exception
8fe6719 [Andrew Or] Better exception message from failed test
[SPARK-4281][Build] Package Yarn shuffle service into its own jar

This is another addendum to #3082, which added the Yarn shuffle service to run inside the NM. This PR makes the feature much more usable by packaging enough dependencies into the jar to run the service inside an NM. After these changes, the user can run `./make-distribution.sh` and find a `spark-network-yarn*.jar` in their `lib` directory. The equivalent change is done in SBT by making the `network-yarn` module an assembly project.

Author: Andrew Or <andrew@databricks.com>

Closes #3147 from andrewor14/yarn-shuffle-build and squashes the following commits:

bda58d0 [Andrew Or] Fix line too long
81e9705 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-shuffle-build
fb7f398 [Andrew Or] Rename jar to spark-{VERSION}-yarn-shuffle.jar
65db822 [Andrew Or] Actually mark slf4j as provided
abcefd1 [Andrew Or] Do the same for SBT
c653028 [Andrew Or] Package network-yarn and its dependencies
Internal cleanup for aggregateMessages

1. Add EdgeActiveness enum to represent activeness criteria more cleanly than using booleans.
2. Comments and whitespace.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #3231 from ankurdave/aggregateMessages-followup and squashes the following commits:

3d485c3 [Ankur Dave] Internal cleanup for aggregateMessages
[Release] Correct make-distribution.sh log path
[SPARK-3666] Extract interfaces for EdgeRDD and VertexRDD

This discourages users from calling the VertexRDD and EdgeRDD constructor and makes it easier for future changes to ensure backward compatibility.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #2530 from ankurdave/SPARK-3666 and squashes the following commits:

d681f45 [Ankur Dave] Define getPartitions and compute in abstract class for MIMA
1472390 [Ankur Dave] Merge remote-tracking branch 'apache-spark/master' into SPARK-3666
24201d4 [Ankur Dave] Merge remote-tracking branch 'apache-spark/master' into SPARK-3666
cbe15f2 [Ankur Dave] Remove specialized annotation from VertexRDD and EdgeRDD
931b587 [Ankur Dave] Use abstract class instead of trait for binary compatibility
9ba4ec4 [Ankur Dave] Mark (Vertex|Edge)RDDImpl constructors package-private
620e603 [Ankur Dave] Extract VertexRDD interface and move implementation to VertexRDDImpl
55b6398 [Ankur Dave] Extract EdgeRDD interface and move implementation to EdgeRDDImpl
[SPARK-4369] [MLLib] fix TreeModel.predict() with RDD

Fix  TreeModel.predict() with RDD, added tests for it.

(Also checked that other models don't have this issue)

Author: Davies Liu <davies@databricks.com>

Closes #3230 from davies/predict and squashes the following commits:

81172aa [Davies Liu] fix predict
[SPARK-2672] support compressed file in wholeTextFile

The wholeFile() can not read compressed files, it should be, just like textFile().

Author: Davies Liu <davies@databricks.com>

Closes #3005 from davies/whole and squashes the following commits:

a43fcfb [Davies Liu] remove semicolon
c83571a [Davies Liu] remove = if return type is Unit
83c844f [Davies Liu] Merge branch 'master' of github.com:apache/spark into whole
22e8b3e [Davies Liu] support compressed file in wholeTextFile
[Release] Bring audit scripts up-to-date

This involves a few main changes:
- Log all output message to the log file. Previously the log file
  was not useful because it did not indicate progress.
- Remove hive-site.xml in sbt_hive_app to avoid interference
- Add the appropriate repositories for new dependencies
[SPARK-4373][MLLIB] fix MLlib maven tests

We want to make sure there is at most one spark context inside the same jvm. JoshRosen

Author: Xiangrui Meng <meng@databricks.com>

Closes #3235 from mengxr/SPARK-4373 and squashes the following commits:

6574b69 [Xiangrui Meng] rename LocalSparkContext to MLlibTestSparkContext
913d48d [Xiangrui Meng] make sure there is at most one spark context inside the same jvm
[SPARK-4370] [Core] Limit number of Netty cores based on executor size

Author: Aaron Davidson <aaron@databricks.com>

Closes #3155 from aarondav/conf and squashes the following commits:

7045e77 [Aaron Davidson] Add mesos comment
4770f6e [Aaron Davidson] [SPARK-4370] [Core] Limit number of Netty cores based on executor size
[SPARK-4256] Make Binary Evaluation Metrics functions defined in cases where there ar...

...e 0 positive or 0 negative examples.

Author: Andrew Bullen <andrew.bullen@workday.com>

Closes #3118 from abull/master and squashes the following commits:

c2bf2b1 [Andrew Bullen] [SPARK-4256] Update Code formatting for BinaryClassificationMetricsSpec
36b0533 [Andrew Bullen] [SYMAN-4256] Extract BinaryClassificationMetricsSuite assertions into private method
4d2f79a [Andrew Bullen] [SPARK-4256] Refactor classification metrics tests - extract comparison functions in test
f411e70 [Andrew Bullen] [SPARK-4256] Define precision as 1.0 when there are no positive examples; update code formatting per pull request comments
d9a09ef [Andrew Bullen] Make Binary Evaluation Metrics functions defined in cases where there are 0 positive or 0 negative examples.
[SPARK-4348] [PySpark] [MLlib] rename random.py to rand.py

This PR rename random.py to rand.py to avoid the side affects of conflict with random module, but still keep the same interface as before.

```
>>> from pyspark.mllib.random import RandomRDDs
```

```
$ pydoc pyspark.mllib.random
Help on module random in pyspark.mllib:
NAME
    random - Python package for random data generation.

FILE
    /Users/davies/work/spark/python/pyspark/mllib/rand.py

CLASSES
    __builtin__.object
        pyspark.mllib.random.RandomRDDs

    class RandomRDDs(__builtin__.object)
     |  Generator methods for creating RDDs comprised of i.i.d samples from
     |  some distribution.
     |
     |  Static methods defined here:
     |
     |  normalRDD(sc, size, numPartitions=None, seed=None)
```

cc mengxr

reference link: http://xion.org.pl/2012/05/06/hacking-python-imports/

Author: Davies Liu <davies@databricks.com>

Closes #3216 from davies/random and squashes the following commits:

7ac4e8b [Davies Liu] rename random.py to rand.py