Turns out we are allocating an allocator pool for every TransportClient (which means that the number increases with the number of nodes in the cluster), when really we should just reuse one for all clients.

This patch, as expected, greatly decreases off-heap memory allocation, and appears to make allocation only proportional to the number of cores.

Author: Aaron Davidson <aaron@databricks.com>

Closes #3465 from aarondav/fewer-pools and squashes the following commits:

36c49da [Aaron Davidson] [SPARK-4516] Avoid allocating unnecessarily Netty PooledByteBufAllocators

In practice, only 2-4 cores should be required to transfer roughly 10 Gb/s, and each core that we use will have an initial overhead of roughly 32 MB of off-heap memory, which comes at a premium.

Thus, this value should still retain maximum throughput and reduce wasted off-heap memory allocation. It can be overridden by setting the number of serverThreads and clientThreads manually in Spark's configuration.

Author: Aaron Davidson <aaron@databricks.com>

Closes #3469 from aarondav/fewer-pools2 and squashes the following commits:

087c59f [Aaron Davidson] [SPARK-4516] Cap default number of Netty threads at 8

User could construct an MF model directly. I added a note about the performance.

Author: Xiangrui Meng <meng@databricks.com>

Closes #3459 from mengxr/SPARK-4604 and squashes the following commits:

f64bcd3 [Xiangrui Meng] organize imports
ed08214 [Xiangrui Meng] check preconditions and unit tests
a624c12 [Xiangrui Meng] make MatrixFactorizationModel public

Currently, the LogLoss used by GradientBoostedTrees has 2 issues:
* the gradient (and therefore loss) does not match that used by Friedman (1999)
* the error computation uses 0/1 accuracy, not log loss

This PR updates LogLoss.
It also adds some doc for boosting and forests.

I tested it on sample data and made sure the log loss is monotonically decreasing with each boosting iteration.

CC: mengxr manishamde codedeft

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #3439 from jkbradley/gbt-loss-fix and squashes the following commits:

cfec17e [Joseph K. Bradley] removed forgotten temp comments
a27eb6d [Joseph K. Bradley] corrections to last log loss commit
ed5da2c [Joseph K. Bradley] updated LogLoss (boosting) for numerical stability
5e52bff [Joseph K. Bradley] * Removed the 1/2 from SquaredError.  This also required updating the test suite since it effectively doubles the gradient and loss. * Added doc for developers within RandomForest. * Small cleanup in test suite (generating data only once)
e57897a [Joseph K. Bradley] Fixed LogLoss for GradientBoostedTrees, and updated doc for losses, forests, and boosting

This PR reverts changes related to tag-based cluster membership. As discussed in SPARK-3332, we didn't figure out a safe strategy to use tags to determine cluster membership, because tagging is not atomic. The following changes are reverted:

SPARK-2333: 94053a7b
SPARK-3213: 7faf755a
SPARK-3608: 78d4220f.

I tested launch, login, and destroy. It is easy to check the diff by comparing it to Josh's patch for branch-1.1:

https://github.com/apache/spark/pull/2225/files

JoshRosen I sent the PR to master. It might be easier for us to keep master and branch-1.2 the same at this time. We can always re-apply the patch once we figure out a stable solution.

Author: Xiangrui Meng <meng@databricks.com>

Closes #3453 from mengxr/SPARK-4509 and squashes the following commits:

f0b708b [Xiangrui Meng] revert 94053a7b
4298ea5 [Xiangrui Meng] revert 7faf755a
35963a1 [Xiangrui Meng] Revert "SPARK-3608 Break if the instance tag naming succeeds"

Fix [SPARK-4471](https://issues.apache.org/jira/browse/SPARK-4471): blockManagerIdFromJson function throws exception while BlockManagerId be null in MetadataFetchFailedException

Author: hushan[胡珊] <hushan@xiaomi.com>

Closes #3340 from suyanNone/fix-blockmanagerId-jnothing-2 and squashes the following commits:

159f9a3 [hushan[胡珊]] Refine test code for blockmanager is null
4380d73 [hushan[胡珊]] remove useless blank line
3ccf651 [hushan[胡珊]] Fix SPARK-4471: blockManagerIdFromJson function throws exception while metadata fetch failed

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

**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-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.

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-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

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-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

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'

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.

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

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

```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

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

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

This reverts commit b043c274.

I accidentally committed this using my own authorship credential. However,
I should have given authoriship to the original author: Jongyoul Lee.

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

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.

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

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()

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-4518][SPARK-4519][Streaming] Refactored file stream to prevent files from being processed multiple times

Because of a corner case, a file already selected for batch t can get considered again for batch t+2. This refactoring fixes it by remembering all the files selected in the last 1 minute, so that this corner case does not arise. Also uses spark context's hadoop configuration to access the file system API for listing directories.

pwendell Please take look. I still have not run long-running integration tests, so I cannot say for sure whether this has indeed solved the issue. You could do a first pass on this in the meantime.

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

Closes #3419 from tdas/filestream-fix2 and squashes the following commits:

c19dd8a [Tathagata Das] Addressed PR comments.
513b608 [Tathagata Das] Updated docs.
d364faf [Tathagata Das] Added the current time condition back
5526222 [Tathagata Das] Removed unnecessary imports.
38bb736 [Tathagata Das] Fix long line.
203bbc7 [Tathagata Das] Un-ignore tests.
eaef4e1 [Tathagata Das] Fixed SPARK-4519
9dbd40a [Tathagata Das] Refactored FileInputDStream to remember last few batches.

This PR adds two new pages to the Spark Web UI:

- A jobs overview page, which shows details on running / completed / failed jobs.
- A job details page, which displays information on an individual job's stages.

The jobs overview page is now the default UI homepage; the old homepage is still accessible at `/stages`.

### Screenshots

#### New UI homepage

![image](https://cloud.githubusercontent.com/assets/50748/5119035/fd0a69e6-701f-11e4-89cb-db7e9705714f.png)

#### Job details page

(This is effectively a per-job version of the stages page that can be extended later with other things, such as DAG visualizations)

![image](https://cloud.githubusercontent.com/assets/50748/5134910/50b340d4-70c7-11e4-88e1-6b73237ea7c8.png)

### Key changes in this PR

- Rename `JobProgressPage` to `AllStagesPage`
- Expose `StageInfo` objects in the ``SparkListenerJobStart` event; add backwards-compatibility tests to JsonProtocol.
- Add additional data structures to `JobProgressListener` to map from stages to jobs.
- Add several fields to `JobUIData`.

I also added ~150 lines of Selenium tests as I uncovered UI issues while developing this patch.

### Limitations

If a job contains stages that aren't run, then its overall job progress bar may be an underestimate of the total job progress; in other words, a completed job may appear to have a progress bar that's not at 100%.

If stages or tasks fail, then the progress bar will not go backwards to reflect the true amount of remaining work.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #3009 from JoshRosen/job-page and squashes the following commits:

eb05e90 [Josh Rosen] Disable kill button in completed stages tables.
f00c851 [Josh Rosen] Fix JsonProtocol compatibility
b89c258 [Josh Rosen] More JSON protocol backwards-compatibility fixes.
ff804cd [Josh Rosen] Don't write "Stage Ids" field in JobStartEvent JSON.
6f17f3f [Josh Rosen] Only store StageInfos in SparkListenerJobStart event.
2bbf41a [Josh Rosen] Update job progress bar to reflect skipped tasks/stages.
61c265a [Josh Rosen] Add “skipped stages” table; only display non-empty tables.
1f45d44 [Josh Rosen] Incorporate a bunch of minor review feedback.
0b77e3e [Josh Rosen] More bug fixes for phantom stages.
034aa8d [Josh Rosen] Use `.max()` to find result stage for job.
eebdc2c [Josh Rosen] Don’t display pending stages for completed jobs.
67080ba [Josh Rosen] Ensure that "phantom stages" don't cause memory leaks.
7d10b97 [Josh Rosen] Merge remote-tracking branch 'apache/master' into job-page
d69c775 [Josh Rosen] Fix table sorting on all jobs page.
5eb39dc [Josh Rosen] Add pending stages table to job page.
f2a15da [Josh Rosen] Add status field to job details page.
171b53c [Josh Rosen] Move `startTime` to the start of SparkContext.
e2f2c43 [Josh Rosen] Fix sorting of stages in job details page.
8955f4c [Josh Rosen] Display information for pending stages on jobs page.
8ab6c28 [Josh Rosen] Compute numTasks from job start stage infos.
5884f91 [Josh Rosen] Add StageInfos to SparkListenerJobStart event.
79793cd [Josh Rosen] Track indices of completed stage to avoid overcounting when failures occur.
d62ea7b [Josh Rosen] Add failing Selenium test for stage overcounting issue.
1145c60 [Josh Rosen] Display text instead of progress bar for stages.
3d0a007 [Josh Rosen] Merge remote-tracking branch 'origin/master' into job-page
8a2351b [Josh Rosen] Add help tooltip to Spark Jobs page.
b7bf30e [Josh Rosen] Add stages progress bar; fix bug where active stages show as completed.
4846ce4 [Josh Rosen] Hide "(Job Group") if no jobs were submitted in job groups.
4d58e55 [Josh Rosen] Change label to "Tasks (for all stages)"
85e9c85 [Josh Rosen] Extract startTime into separate variable.
1cf4987 [Josh Rosen] Fix broken kill links; add Selenium test to avoid future regressions.
56701fa [Josh Rosen] Move last stage name / description logic out of markup.
a475ea1 [Josh Rosen] Add progress bars to jobs page.
45343b8 [Josh Rosen] More comments
4b206fb [Josh Rosen] Merge remote-tracking branch 'origin/master' into job-page
bfce2b9 [Josh Rosen] Address review comments, except for progress bar.
4487dcb [Josh Rosen] [SPARK-4145] Web UI job pages
2568a6c [Josh Rosen] Rename JobProgressPage to AllStagesPage:

When we use ORDER BY clause, at first, attributes referenced by projection are resolved (1).
And then, attributes referenced at ORDER BY clause are resolved (2).
 But when resolving attributes referenced at ORDER BY clause, the resolution result generated in (1) is discarded so for example, following query fails.

    SELECT c1 + c2 FROM mytable ORDER BY c1;

The query above fails because when resolving the attribute reference 'c1', the resolution result of 'c2' is discarded.

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #3363 from sarutak/SPARK-4487 and squashes the following commits:

fd314f3 [Kousuke Saruta] Fixed attribute resolution logic in Analyzer
6e60c20 [Kousuke Saruta] Fixed conflicts
cb5b7e9 [Kousuke Saruta] Added test case for SPARK-4487
282d529 [Kousuke Saruta] Fixed attributes reference resolution error
b6123e6 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into concat-feature
317b7fb [Kousuke Saruta] WIP

It require us to run ```HiveFromSpark``` in specified dir because ```HiveFromSpark``` use relative path, this leads to ```run-example``` error(http://apache-spark-developers-list.1001551.n3.nabble.com/src-main-resources-kv1-txt-not-found-in-example-of-HiveFromSpark-td9100.html).

Author: scwf <wangfei1@huawei.com>

Closes #3415 from scwf/HiveFromSpark and squashes the following commits:

ed3d6c9 [scwf] revert no need change
b00e20c [scwf] fix path usring spark_home
dbd321b [scwf] fix path in hivefromspark

This file is for Hive 0.13.1 I think.

Author: Daniel Darabos <darabos.daniel@gmail.com>

Closes #3432 from darabos/patch-2 and squashes the following commits:

4fd22ed [Daniel Darabos] Fix comment. This file is for Hive 0.13.1.

This PR is a workaround for SPARK-4479. Two changes are introduced: when merge sort is bypassed in `ExternalSorter`,

1. also bypass RDD elements buffering as buffering is the reason that `MutableRow` backed row objects must be copied, and
2. avoids defensive copies in `Exchange` operator

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Author: Cheng Lian <lian@databricks.com>

Closes #3422 from liancheng/avoids-defensive-copies and squashes the following commits:

591f2e9 [Cheng Lian] Passes all shuffle suites
0c3c91e [Cheng Lian] Fixes shuffle write metrics when merge sort is bypassed
ed5df3c [Cheng Lian] Fixes styling changes
f75089b [Cheng Lian] Avoids unnecessary defensive copies when sort based shuffle is on

Author: Sandy Ryza <sandy@cloudera.com>

Closes #3322 from sryza/sandy-spark-4457 and squashes the following commits:

5e72b77 [Sandy Ryza] Feedback
0cf05c1 [Sandy Ryza] Caveat
be8084b [Sandy Ryza] SPARK-4457. Document how to build for Hadoop versions greater than 2.4

... - there is no way around this for deserializing actorRef(s).

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #3402 from ScrapCodes/SPARK-4377/troubleDeserializing and squashes the following commits:

77233fd [Prashant Sharma] Style fixes
9b35c6e [Prashant Sharma] Scalastyle fixes
29880da [Prashant Sharma] [SPARK-4377] Fixed serialization issue by switching to akka provided serializer - there is no way around this for deserializing actorRef(s).

Previously, we were using Breeze's activeIterator to access the non-zero elements
in dense/sparse vector. Due to the overhead, we switched back to native `while loop`
in #SPARK-4129.

However, #SPARK-4129 requires de-reference the dv.values/sv.values in
each access to the value, which is very expensive. Also, in MultivariateOnlineSummarizer,
we're using Breeze's dense vector to store the partial stats, and this is very expensive compared
with using primitive scala array.

In this PR, efficient foreachActive is implemented to unify the code path for dense and sparse
vector operation which makes codebase easier to maintain. Breeze dense vector is replaced
by primitive array to reduce the overhead further.

Benchmarking with mnist8m dataset on single JVM
with first 200 samples loaded in memory, and repeating 5000 times.

Before change:
Sparse Vector - 30.02
Dense Vector - 38.27

With this PR:
Sparse Vector - 6.29
Dense Vector - 11.72

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #3288 from dbtsai/activeIterator and squashes the following commits:

844b0e6 [DB Tsai] formating
03dd693 [DB Tsai] futher performance tunning.
1907ae1 [DB Tsai] address feedback
98448bb [DB Tsai] Made the override final, and had a local copy of variables which made the accessing a single step operation.
c0cbd5a [DB Tsai] fix a bug
6441f92 [DB Tsai] Finished SPARK-4431

The Pyrolite is pretty slow (comparing to the adhoc serializer in 1.1), it cause much performance regression in 1.2, because we cache the serialized Python object in JVM, deserialize them into Java object in each step.

This PR change to cache the deserialized JavaRDD instead of PythonRDD to avoid the deserialization of Pyrolite. It should have similar memory usage as before, but much faster.

Author: Davies Liu <davies@databricks.com>

Closes #3397 from davies/cache and squashes the following commits:

7f6e6ce [Davies Liu] Update -> Updater
4b52edd [Davies Liu] using named argument
63b984e [Davies Liu] fix
7da0332 [Davies Liu] add unpersist()
dff33e1 [Davies Liu] address comments
c2bdfc2 [Davies Liu] refactor
d572f00 [Davies Liu] Merge branch 'master' into cache
f1063e1 [Davies Liu] cache serialized java object

Because the Hive profile is no longer defined in the root pom,
we need to check specifically in the sql/hive pom when we
perform the check in make-distribtion.sh.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #3398 from pwendell/make-distribution and squashes the following commits:

8a58279 [Patrick Wendell] Fix bug in detection of Hive in Spark 1.2

This PR moved `implicit`s to `package object` and `companion object` to enable the Scala compiler search them automatically without explicit importing.

It should not break any API. A test project for backforward compatibility is [here](https://github.com/zsxwing/SPARK-4397-Backforward-Compatibility). It proves the codes compiled with Spark 1.1.0 can run with this PR.

To summarize, the changes are:

* Deprecated the old implicit conversion functions: this preserves binary compatibility for code compiled against earlier versions of Spark.
* Removed "implicit" from them so they are just normal functions: this made sure the compiler doesn't get confused and warn about multiple implicits in scope.
* Created new implicit functions in package rdd object, which is part of the scope that scalac will search when looking for implicit conversions on various RDD objects.

The disadvantage is there are duplicated codes in SparkContext for backforward compatibility.

Author: zsxwing <zsxwing@gmail.com>

Closes #3262 from zsxwing/SPARK-4397 and squashes the following commits:

fc30314 [zsxwing] Update the comments
9c27aff [zsxwing] Move implicit functions to object RDD and forward old functions to new implicit ones directly
2b5f5a4 [zsxwing] Comments for the deprecated functions
52353de [zsxwing] Remove private[spark] from object WritableConverter
34641d4 [zsxwing] Move ImplicitSuite to org.apache.sparktest
7266218 [zsxwing] Add comments to warn the duplicate codes in SparkContext
185c12f [zsxwing] Remove simpleWritableConverter from SparkContext
3bdcae2 [zsxwing] Move WritableConverter implicits to object WritableConverter
9b73188 [zsxwing] Fix the code style issue
3ac4f07 [zsxwing] Add license header
1eda9e4 [zsxwing] Reorganize 'implicit's to improve the API convenience

... successfully

It's weird that printing "Spark context available as sc" when creating SparkContext unsuccessfully.

Author: zsxwing <zsxwing@gmail.com>

Closes #3341 from zsxwing/SPARK-4472 and squashes the following commits:

4850093 [zsxwing] Print "Spark context available as sc." only when SparkContext is created successfully