[SPARK-4562] [MLlib] speedup vector

This PR change the underline array of DenseVector to numpy.ndarray to avoid the conversion, because most of the users will using numpy.array.

It also improve the serialization of DenseVector.

Before this change:

trial	| trainingTime | 	testTime
-------|--------|--------
0	| 5.126 | 	1.786
1	|2.698	|1.693

After the change:

trial	| trainingTime |	testTime
-------|--------|--------
0	|4.692	|0.554
1	|2.307	|0.525

This could partially fix the performance regression during test.

Author: Davies Liu <davies@databricks.com>

Closes #3420 from davies/ser2 and squashes the following commits:

0e1e6f3 [Davies Liu] fix tests
426f5db [Davies Liu] impove toArray()
44707ec [Davies Liu] add name for ISO-8859-1
fa7d791 [Davies Liu] address comments
1cfb137 [Davies Liu] handle zero sparse vector
2548ee2 [Davies Liu] fix tests
9e6389d [Davies Liu] bugfix
470f702 [Davies Liu] speed up DenseMatrix
f0d3c40 [Davies Liu] speedup SparseVector
ef6ce70 [Davies Liu] speed up dense vector
[SPARK-4578] fix asDict() with nested Row()

The Row object is created on the fly once the field is accessed, so we should access them by getattr() in asDict(0

Author: Davies Liu <davies@databricks.com>

Closes #3434 from davies/fix_asDict and squashes the following commits:

b20f1e7 [Davies Liu] fix asDict() with nested Row()
[SPARK-4548] []SPARK-4517] improve performance of python broadcast

Re-implement the Python broadcast using file:

1) serialize the python object using cPickle, write into disks.
2) Create a wrapper in JVM (for the dumped file), it read data from during serialization
3) Using TorrentBroadcast or HttpBroadcast to transfer the data (compressed) into executors
4) During deserialization, writing the data into disk.
5) Passing the path into Python worker, read data from disk and unpickle it into python object, until the first access.

It fixes the performance regression introduced in #2659, has similar performance as 1.1, but support object larger than 2G, also improve the memory efficiency (only one compressed copy in driver and executor).

Testing with a 500M broadcast and 4 tasks (excluding the benefit from reused worker in 1.2):

         name |   1.1   | 1.2 with this patch |  improvement
---------|--------|---------|--------
      python-broadcast-w-bytes  |	25.20  |	9.33   |	170.13% |
        python-broadcast-w-set	  |     4.13	   |    4.50  |	-8.35%  |

Testing with 100 tasks (16 CPUs):

         name |   1.1   | 1.2 with this patch |  improvement
---------|--------|---------|--------
     python-broadcast-w-bytes	| 38.16	| 8.40	 | 353.98%
        python-broadcast-w-set	| 23.29	| 9.59 |	142.80%

Author: Davies Liu <davies@databricks.com>

Closes #3417 from davies/pybroadcast and squashes the following commits:

50a58e0 [Davies Liu] address comments
b98de1d [Davies Liu] disable gc while unpickle
e5ee6b9 [Davies Liu] support large string
09303b8 [Davies Liu] read all data into memory
dde02dd [Davies Liu] improve performance of python broadcast
[SPARK-4266] [Web-UI] Reduce stage page load time.

The commit changes the java script used to show/hide additional
metrics in order to reduce page load time. SPARK-4016 significantly
increased page load time for the stage page when stages had a lot
(thousands or tens of thousands) of tasks, due to the additional
Javascript to hide some metrics by default and stripe the tables.
This commit reduces page load time in two ways:

(1) Now, all of the metrics that are hidden by default are
hidden by setting "display: none;" using CSS for the page,
rather than hiding them using javascript after the page loads.
Without this change, for stages with thousands of tasks, there
was a few second delay after page load, where first the additional
metrics were shown, and then after a delay were hidden once the
relevant JS finished running.

(2) CSS is used to stripe all of the tables except for the summary
table. The summary table needs javascript to do the striping because
some rows are hidden, but the javascript striping is slower, which
again resulted in a delay when it was used for the task table (where
for a few seconds after page load, all of the rows in the task table
would be white, while the browser finished running the JS to stripe
the table).

cc pwendell

This change is intended to be backported to 1.2 to avoid a regression in
UI performance when users run large jobs.

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #3328 from kayousterhout/SPARK-4266 and squashes the following commits:

f964091 [Kay Ousterhout] [SPARK-4266] [Web-UI] Reduce stage page load time.
[SPARK-4525] Mesos should decline unused offers

Functionally, this is just a small change on top of #3393 (by jongyoul). The issue being addressed is discussed in the comments there. I have not yet added a test for the bug there. I will add one shortly.

I've also done some minor renaming/clean-up of variables in this class and tests.

Author: Patrick Wendell <pwendell@gmail.com>
Author: Jongyoul Lee <jongyoul@gmail.com>

Closes #3436 from pwendell/mesos-issue and squashes the following commits:

58c35b5 [Patrick Wendell] Adding unit test for this situation
c4f0697 [Patrick Wendell] Additional clean-up and fixes on top of existing fix
f20f1b3 [Jongyoul Lee] [SPARK-4525] MesosSchedulerBackend.resourceOffers cannot decline unused offers from acceptedOffers - Added code for declining unused offers among acceptedOffers - Edited testCase for checking declining unused offers
Revert "[SPARK-4525] Mesos should decline unused offers"

This reverts commit b043c27424d05e3200e7ba99a1a65656b57fa2f0.

I accidentally committed this using my own authorship credential. However,
I should have given authoriship to the original author: Jongyoul Lee.
[SPARK-4525] Mesos should decline unused offers

Functionally, this is just a small change on top of #3393 (by jongyoul). The issue being addressed is discussed in the comments there. I have not yet added a test for the bug there. I will add one shortly.

I've also done some minor renaming/clean-up of variables in this class and tests.

Author: Patrick Wendell <pwendell@gmail.com>
Author: Jongyoul Lee <jongyoul@gmail.com>

Closes #3436 from pwendell/mesos-issue and squashes the following commits:

58c35b5 [Patrick Wendell] Adding unit test for this situation
c4f0697 [Patrick Wendell] Additional clean-up and fixes on top of existing fix
f20f1b3 [Jongyoul Lee] [SPARK-4525] MesosSchedulerBackend.resourceOffers cannot decline unused offers from acceptedOffers - Added code for declining unused offers among acceptedOffers - Edited testCase for checking declining unused offers
[SPARK-4582][MLLIB] get raw vectors for further processing in Word2Vec

This is #3309 for the master branch.

e.g. clustering

Author: tkaessmann <tobias.kaessmanns24.com>

Closes #3309 from tkaessmann/branch-1.2 and squashes the following commits:

e3a3142 [tkaessmann] changes the comment for getVectors
58d3d83 [tkaessmann] removes sign from comment
a5be213 [tkaessmann] fixes getVectors to fit code guidelines
3782fa9 [tkaessmann] get raw vectors for further processing

Author: tkaessmann <tobias.kaessmann@s24.com>

Closes #3437 from mengxr/SPARK-4582 and squashes the following commits:

6c666b4 [tkaessmann] get raw vectors for further processing in Word2Vec
[SQL] Compute timeTaken correctly

```timeTaken``` should not count the time of printing result.

Author: w00228970 <wangfei1@huawei.com>

Closes #3423 from scwf/time-taken-bug and squashes the following commits:

da7e102 [w00228970] compute time taken correctly
[DOC][Build] Wrong cmd for build spark with apache hadoop 2.4.X and hive 12

Author: wangfei <wangfei1@huawei.com>

Closes #3335 from scwf/patch-10 and squashes the following commits:

d343113 [wangfei] add '-Phive'
60d595e [wangfei] [DOC] Wrong cmd for build spark with apache hadoop 2.4.X and Hive 12 support
[SPARK-4596][MLLib] Refactorize Normalizer to make code cleaner

In this refactoring, the performance will be slightly increased due to removing
the overhead from breeze vector. The bottleneck is still in breeze norm
which is implemented by activeIterator.

This inefficiency of breeze norm will be addressed in next PR. At least,
this PR makes the code more consistent in the codebase.

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #3446 from dbtsai/normalizer and squashes the following commits:

e20a2b9 [DB Tsai] first commit
[SPARK-4526][MLLIB]GradientDescent get a wrong gradient value according to the gradient formula.

This is caused by the miniBatchSize parameter.The number of `RDD.sample` returns is not fixed.
cc mengxr

Author: GuoQiang Li <witgo@qq.com>

Closes #3399 from witgo/GradientDescent and squashes the following commits:

13cb228 [GuoQiang Li] review commit
668ab66 [GuoQiang Li] Double to Long
b6aa11a [GuoQiang Li] Check miniBatchSize is greater than 0
0b5c3e3 [GuoQiang Li] Minor fix
12e7424 [GuoQiang Li] GradientDescent get a wrong gradient value according to the gradient formula, which is caused by the miniBatchSize parameter.
[SPARK-4535][Streaming] Fix the error in comments

change `NetworkInputDStream` to `ReceiverInputDStream`
change `ReceiverInputTracker` to `ReceiverTracker`

Author: q00251598 <qiyadong@huawei.com>

Closes #3400 from watermen/fix-comments and squashes the following commits:

75d795c [q00251598] change 'NetworkInputDStream' to 'ReceiverInputDStream' && change 'ReceiverInputTracker' to 'ReceiverTracker'
[SPARK-4381][Streaming]Add warning log when user set spark.master to local in Spark Streaming and there's no job executed

Author: jerryshao <saisai.shao@intel.com>

Closes #3244 from jerryshao/SPARK-4381 and squashes the following commits:

d2486c7 [jerryshao] Improve the warning log
d726e85 [jerryshao] Add local[1] to the filter condition
eca428b [jerryshao] Add warning log
[SPARK-4344][DOCS] adding documentation on spark.yarn.user.classpath.first

The documentation for the two parameters is the same with a pointer from the standalone parameter to the yarn parameter

Author: arahuja <aahuja11@gmail.com>

Closes #3209 from arahuja/yarn-classpath-first-param and squashes the following commits:

51cb9b2 [arahuja] [SPARK-4344][DOCS] adding documentation for YARN on userClassPathFirst
[SPARK-4601][Streaming] Set correct call site for streaming jobs so that it is displayed correctly on the Spark UI

When running the NetworkWordCount, the description of the word count jobs are set as "getCallsite at DStream:xxx" . This should be set to the line number of the streaming application that has the output operation that led to the job being created. This is because the callsite is incorrectly set in the thread launching the jobs. This PR fixes that.

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

Closes #3455 from tdas/streaming-callsite-fix and squashes the following commits:

69fc26f [Tathagata Das] Set correct call site for streaming jobs so that it is displayed correctly on the Spark UI
[SPARK-4581][MLlib] Refactorize StandardScaler to improve the transformation performance

The following optimizations are done to improve the StandardScaler model
transformation performance.

1) Covert Breeze dense vector to primitive vector to reduce the overhead.
2) Since mean can be potentially a sparse vector, we explicitly convert it to dense primitive vector.
3) Have a local reference to `shift` and `factor` array so JVM can locate the value with one operation call.
4) In pattern matching part, we use the mllib SparseVector/DenseVector instead of breeze's vector to
make the codebase cleaner.

Benchmark with mnist8m dataset:

Before,
DenseVector withMean and withStd: 50.97secs
DenseVector withMean and withoutStd: 42.11secs
DenseVector withoutMean and withStd: 8.75secs
SparseVector withoutMean and withStd: 5.437secs

With this PR,
DenseVector withMean and withStd: 5.76secs
DenseVector withMean and withoutStd: 5.28secs
DenseVector withoutMean and withStd: 5.30secs
SparseVector withoutMean and withStd: 1.27secs

Note that without the local reference copy of `factor` and `shift` arrays,
the runtime is almost three time slower.

DenseVector withMean and withStd: 18.15secs
DenseVector withMean and withoutStd: 18.05secs
DenseVector withoutMean and withStd: 18.54secs
SparseVector withoutMean and withStd: 2.01secs

The following code,
```scala
while (i < size) {
   values(i) = (values(i) - shift(i)) * factor(i)
   i += 1
}
```
will generate the bytecode
```
   L13
    LINENUMBER 106 L13
   FRAME FULL [org/apache/spark/mllib/feature/StandardScalerModel org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/DenseVector T [D I I] []
    ILOAD 7
    ILOAD 6
    IF_ICMPGE L14
   L15
    LINENUMBER 107 L15
    ALOAD 5
    ILOAD 7
    ALOAD 5
    ILOAD 7
    DALOAD
    ALOAD 0
    INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.shift ()[D
    ILOAD 7
    DALOAD
    DSUB
    ALOAD 0
    INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.factor ()[D
    ILOAD 7
    DALOAD
    DMUL
    DASTORE
   L16
    LINENUMBER 108 L16
    ILOAD 7
    ICONST_1
    IADD
    ISTORE 7
    GOTO L13
```
, while with the local reference of the `shift` and `factor` arrays, the bytecode will be
```
   L14
    LINENUMBER 107 L14
    ALOAD 0
    INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.factor ()[D
    ASTORE 9
   L15
    LINENUMBER 108 L15
   FRAME FULL [org/apache/spark/mllib/feature/StandardScalerModel org/apache/spark/mllib/linalg/Vector [D org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/DenseVector T [D I I [D] []
    ILOAD 8
    ILOAD 7
    IF_ICMPGE L16
   L17
    LINENUMBER 109 L17
    ALOAD 6
    ILOAD 8
    ALOAD 6
    ILOAD 8
    DALOAD
    ALOAD 2
    ILOAD 8
    DALOAD
    DSUB
    ALOAD 9
    ILOAD 8
    DALOAD
    DMUL
    DASTORE
   L18
    LINENUMBER 110 L18
    ILOAD 8
    ICONST_1
    IADD
    ISTORE 8
    GOTO L15
```

You can see that with local reference, the both of the arrays will be in the stack, so JVM can access the value without calling `INVOKESPECIAL`.

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #3435 from dbtsai/standardscaler and squashes the following commits:

85885a9 [DB Tsai] revert to have lazy in shift array.
daf2b06 [DB Tsai] Address the feedback
cdb5cef [DB Tsai] small change
9c51eef [DB Tsai] style
fc795e4 [DB Tsai] update
5bffd3d [DB Tsai] first commit
[SPARK-4196][SPARK-4602][Streaming] Fix serialization issue in PairDStreamFunctions.saveAsNewAPIHadoopFiles

Solves two JIRAs in one shot
- Makes the ForechDStream created by saveAsNewAPIHadoopFiles serializable for checkpoints
- Makes the default configuration object used saveAsNewAPIHadoopFiles be the Spark's hadoop configuration

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

Closes #3457 from tdas/savefiles-fix and squashes the following commits:

bb4729a [Tathagata Das] Same treatment for saveAsHadoopFiles
b382ea9 [Tathagata Das] Fix serialization issue in PairDStreamFunctions.saveAsNewAPIHadoopFiles.
[SPARK-4592] Avoid duplicate worker registrations in standalone mode

**Summary.** On failover, the Master may receive duplicate registrations from the same worker, causing the worker to exit. This is caused by this commit https://github.com/apache/spark/commit/4afe9a4852ebeb4cc77322a14225cd3dec165f3f, which adds logic for the worker to re-register with the master in case of failures. However, the following race condition may occur:

(1) Master A fails and Worker attempts to reconnect to all masters
(2) Master B takes over and notifies Worker
(3) Worker responds by registering with Master B
(4) Meanwhile, Worker's previous reconnection attempt reaches Master B, causing the same Worker to register with Master B twice

**Fix.** Instead of attempting to register with all known masters, the worker should re-register with only the one that it has been communicating with. This is safe because the fact that a failover has occurred means the old master must have died. Then, when the worker is finally notified of a new master, it gives up on the old one in favor of the new one.

**Caveat.** Even this fix is subject to more obscure race conditions. For instance, if Master B fails and Master A recovers immediately, then Master A may still observe duplicate worker registrations. However, this and other potential race conditions summarized in [SPARK-4592](https://issues.apache.org/jira/browse/SPARK-4592), are much, much less likely than the one described above, which is deterministically reproducible.

Author: Andrew Or <andrew@databricks.com>

Closes #3447 from andrewor14/standalone-failover and squashes the following commits:

0d9716c [Andrew Or] Move re-registration logic to actor for thread-safety
79286dc [Andrew Or] Preserve old behavior for initial retries
83b321c [Andrew Or] Tweak wording
1fce6a9 [Andrew Or] Active master actor could be null in the beginning
b6f269e [Andrew Or] Avoid duplicate worker registrations
[SPARK-4546] Improve HistoryServer first time user experience

The documentation points the user to run the following
```
sbin/start-history-server.sh
```
The first thing this does is throw an exception that complains a log directory is not specified. The exception message itself does not say anything about what to set. Instead we should have a default and a landing page with a better message. The new default log directory is `file:/tmp/spark-events`.

This is what it looks like as of this PR:

![after](https://issues.apache.org/jira/secure/attachment/12682985/after.png)

Author: Andrew Or <andrew@databricks.com>

Closes #3411 from andrewor14/minor-history-improvements and squashes the following commits:

f33d6b3 [Andrew Or] Point user to set config if default log dir does not exist
fc4c17a [Andrew Or] Improve HistoryServer UX