SPARK-729:  Closures not always serialized at capture time

[SPARK-729](https://spark-project.atlassian.net/browse/SPARK-729) concerns when free variables in closure arguments to transformations are captured.  Currently, it is possible for closures to get the environment in which they are serialized (not the environment in which they are created).  There are a few possible approaches to solving this problem and this PR will discuss some of them.  The approach I took has the advantage of being simple, obviously correct, and minimally-invasive, but it preserves something that has been bothering me about Spark's closure handling, so I'd like to discuss an alternative and get some feedback on whether or not it is worth pursuing.

## What I did

The basic approach I took depends on the work I did for #143, and so this PR is based atop that.  Specifically: #143 modifies `ClosureCleaner.clean` to preemptively determine whether or not closures are serializable immediately upon closure cleaning (rather than waiting for an job involving that closure to be scheduled).  Thus non-serializable closure exceptions will be triggered by the line defining the closure rather than triggered where the closure is used.

Since the easiest way to determine whether or not a closure is serializable is to attempt to serialize it, the code in #143 is creating a serialized closure as part of `ClosureCleaner.clean`.  `clean` currently modifies its argument, but the method in `SparkContext` that wraps it to return a value (a reference to the modified-in-place argument).  This branch modifies `ClosureCleaner.clean` so that it returns a value:  if it is cleaning a serializable closure, it returns the result of deserializing its serialized argument; therefore it is returning a closure with an environment captured at cleaning time.  `SparkContext.clean` then returns the result of `ClosureCleaner.clean`, rather than a reference to its modified-in-place argument.

I've added tests for this behavior (777a1bc).  The pull request as it stands, given the changes in #143, is nearly trivial.  There is some overhead from deserializing the closure, but it is minimal and the benefit of obvious operational correctness (vs. a more sophisticated but harder-to-validate transformation in `ClosureCleaner`) seems pretty important.  I think this is a fine way to solve this problem, but it's not perfect.

## What we might want to do

The thing that has been bothering me about Spark's handling of closures is that it seems like we should be able to statically ensure that cleaning and serialization happen exactly once for a given closure.  If we serialize a closure in order to determine whether or not it is serializable, we should be able to hang on to the generated byte buffer and use it instead of re-serializing the closure later.  By replacing closures with instances of a sum type that encodes whether or not a closure has been cleaned or serialized, we could handle clean, to-be-cleaned, and serialized closures separately with case matches.  Here's a somewhat-concrete sketch (taken from my git stash) of what this might look like:

```scala
package org.apache.spark.util

import java.nio.ByteBuffer
import scala.reflect.ClassManifest

sealed abstract class ClosureBox[T] { def func: T }
final case class RawClosure[T](func: T) extends ClosureBox[T] {}
final case class CleanedClosure[T](func: T) extends ClosureBox[T] {}
final case class SerializedClosure[T](func: T, bytebuf: ByteBuffer) extends ClosureBox[T] {}

object ClosureBoxImplicits {
  implicit def closureBoxFromFunc[T <: AnyRef](fun: T) = new RawClosure[T](fun)
}
```

With these types declared, we'd be able to change `ClosureCleaner.clean` to take a `ClosureBox[T=>U]` (possibly generated by implicit conversion) and return a `ClosureBox[T=>U]` (either a `CleanedClosure[T=>U]` or a `SerializedClosure[T=>U]`, depending on whether or not serializability-checking was enabled) instead of a `T=>U`.  A case match could thus short-circuit cleaning or serializing closures that had already been cleaned or serialized (both in `ClosureCleaner` and in the closure serializer).  Cleaned-and-serialized closures would be represented by a boxed tuple of the original closure and a serialized copy (complete with an environment quiesced at transformation time).  Additional implicit conversions could convert from `ClosureBox` instances to the underlying function type where appropriate.  Tracking this sort of state in the type system seems like the right thing to do to me.

### Why we might not want to do that

_It's pretty invasive._  Every function type used by every `RDD` subclass would have to change to reflect that they expected a `ClosureBox[T=>U]` instead of a `T=>U`.  This obscures what's going on and is not a little ugly.  Although I really like the idea of using the type system to enforce the clean-or-serialize once discipline, it might not be worth adding another layer of types (even if we could hide some of the extra boilerplate with judicious application of implicit conversions).

_It statically guarantees a property whose absence is unlikely to cause any serious problems as it stands._  It appears that all closures are currently dynamically cleaned once and it's not obvious that repeated closure-cleaning is likely to be a problem in the future.  Furthermore, serializing closures is relatively cheap, so doing it once to check for serialization and once again to actually ship them across the wire doesn't seem like a big deal.

Taken together, these seem like a high price to pay for statically guaranteeing that closures are operated upon only once.

## Other possibilities

I felt like the serialize-and-deserialize approach was best due to its obvious simplicity.  But it would be possible to do a more sophisticated transformation within `ClosureCleaner.clean`.  It might also be possible for `clean` to modify its argument in a way so that whether or not a given closure had been cleaned would be apparent upon inspection; this would buy us some of the operational benefits of the `ClosureBox` approach but not the static cleanliness.

I'm interested in any feedback or discussion on whether or not the problems with the type-based approach indeed outweigh the advantage, as well as of approaches to this issue and to closure handling in general.

Author: William Benton <willb@redhat.com>

Closes #189 from willb/spark-729 and squashes the following commits:

f4cafa0 [William Benton] Stylistic changes and cleanups
b3d9c86 [William Benton] Fixed style issues in tests
9b56ce0 [William Benton] Added array-element capture test
97e9d91 [William Benton] Split closure-serializability failure tests
12ef6e3 [William Benton] Skip proactive closure capture for runJob
8ee3ee7 [William Benton] Predictable closure environment capture
12c63a7 [William Benton] Added tests for variable capture in closures
d6e8dd6 [William Benton] Don't check serializability of DStream transforms.
4ecf841 [William Benton] Make proactive serializability checking optional.
d8df3db [William Benton] Adds proactive closure-serializablilty checking
21b4b06 [William Benton] Test cases for SPARK-897.
d5947b3 [William Benton] Ensure assertions in Graph.apply are asserted.
SPARK-1446: Spark examples should not do a System.exit

Spark examples should exit nice using SparkContext.stop() method, rather than System.exit
System.exit can cause issues like in SPARK-1407

Author: Sandeep <sandeep@techaddict.me>

Closes #370 from techaddict/1446 and squashes the following commits:

e9234cf [Sandeep] SPARK-1446: Spark examples should not do a System.exit Spark examples should exit nice using SparkContext.stop() method, rather than System.exit System.exit can cause issues like in SPARK-1407
Revert "SPARK-729:  Closures not always serialized at capture time"

This reverts commit 8ca3b2bc90a63b23a03f339e390174cd7a672b40.
Fix SPARK-1413: Parquet messes up stdout and stdin when used in Spark REPL

Author: witgo <witgo@qq.com>

Closes #325 from witgo/SPARK-1413 and squashes the following commits:

e57cd8e [witgo] use scala reflection to access and call the SLF4JBridgeHandler  methods
45c8f40 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413
5e35d87 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413
0d5f819 [witgo] review commit
45e5b70 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413
fa69dcf [witgo] Merge branch 'master' into SPARK-1413
3c98dc4 [witgo] Merge branch 'master' into SPARK-1413
38160cb [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413
ba09bcd [witgo] remove set the parquet log level
a63d574 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413
5231ecd [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413
3feb635 [witgo] parquet logger use parent handler
fa00d5d [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413
8bb6ffd [witgo] enableLogForwarding note fix
edd9630 [witgo]  move to
f447f50 [witgo] merging master
5ad52bd [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413
76670c1 [witgo] review commit
70f3c64 [witgo] Fix SPARK-1413
[SPARK-1276] Add a HistoryServer to render persisted UI

The new feature of event logging, introduced in #42, allows the user to persist the details of his/her Spark application to storage, and later replay these events to reconstruct an after-the-fact SparkUI.
Currently, however, a persisted UI can only be rendered through the standalone Master. This greatly limits the use case of this new feature as many people also run Spark on Yarn / Mesos.

This PR introduces a new entity called the HistoryServer, which, given a log directory, keeps track of all completed applications independently of a Spark Master. Unlike Master, the HistoryServer needs not be running while the application is still running. It is relatively light-weight in that it only maintains static information of applications and performs no scheduling.

To quickly test it out, generate event logs with ```spark.eventLog.enabled=true``` and run ```sbin/start-history-server.sh <log-dir-path>```. Your HistoryServer awaits on port 18080.

Comments and feedback are most welcome.

---

A few other changes introduced in this PR include refactoring the WebUI interface, which is beginning to have a lot of duplicate code now that we have added more functionality to it. Two new SparkListenerEvents have been introduced (SparkListenerApplicationStart/End) to keep track of application name and start/finish times. This PR also clarifies the semantics of the ReplayListenerBus introduced in #42.

A potential TODO in the future (not part of this PR) is to render live applications in addition to just completed applications. This is useful when applications fail, a condition that our current HistoryServer does not handle unless the user manually signals application completion (by creating the APPLICATION_COMPLETION file). Handling live applications becomes significantly more challenging, however, because it is now necessary to render the same SparkUI multiple times. To avoid reading the entire log every time, which is inefficient, we must handle reading the log from where we previously left off, but this becomes fairly complicated because we must deal with the arbitrary behavior of each input stream.

Author: Andrew Or <andrewor14@gmail.com>

Closes #204 from andrewor14/master and squashes the following commits:

7b7234c [Andrew Or] Finished -> Completed
b158d98 [Andrew Or] Address Patrick's comments
69d1b41 [Andrew Or] Do not block on posting SparkListenerApplicationEnd
19d5dd0 [Andrew Or] Merge github.com:apache/spark
f7f5bf0 [Andrew Or] Make history server's web UI port a Spark configuration
2dfb494 [Andrew Or] Decouple checking for application completion from replaying
d02dbaa [Andrew Or] Expose Spark version and include it in event logs
2282300 [Andrew Or] Add documentation for the HistoryServer
567474a [Andrew Or] Merge github.com:apache/spark
6edf052 [Andrew Or] Merge github.com:apache/spark
19e1fb4 [Andrew Or] Address Thomas' comments
248cb3d [Andrew Or] Limit number of live applications + add configurability
a3598de [Andrew Or] Do not close file system with ReplayBus + fix bind address
bc46fc8 [Andrew Or] Merge github.com:apache/spark
e2f4ff9 [Andrew Or] Merge github.com:apache/spark
050419e [Andrew Or] Merge github.com:apache/spark
81b568b [Andrew Or] Fix strange error messages...
0670743 [Andrew Or] Decouple page rendering from loading files from disk
1b2f391 [Andrew Or] Minor changes
a9eae7e [Andrew Or] Merge branch 'master' of github.com:apache/spark
d5154da [Andrew Or] Styling and comments
5dbfbb4 [Andrew Or] Merge branch 'master' of github.com:apache/spark
60bc6d5 [Andrew Or] First complete implementation of HistoryServer (only for finished apps)
7584418 [Andrew Or] Report application start/end times to HistoryServer
8aac163 [Andrew Or] Add basic application table
c086bd5 [Andrew Or] Add HistoryServer and scripts ++ Refactor WebUI interface
SPARK-1428: MLlib should convert non-float64 NumPy arrays to float64 instead of complaining

Author: Sandeep <sandeep@techaddict.me>

Closes #356 from techaddict/1428 and squashes the following commits:

3bdf5f6 [Sandeep] SPARK-1428: MLlib should convert non-float64 NumPy arrays to float64 instead of complaining
Revert "SPARK-1433: Upgrade Mesos dependency to 0.17.0"

This reverts commit 12c077d5aa0b76a808a55db625c9677a52bd43f9.
Update tuning.md

http://stackoverflow.com/questions/9699071/what-is-the-javas-internal-represention-for-string-modified-utf-8-utf-16

Author: Andrew Ash <andrew@andrewash.com>

Closes #384 from ash211/patch-2 and squashes the following commits:

da1b0be [Andrew Ash] Update tuning.md
Remove Unnecessary Whitespace's

stack these together in a commit else they show up chunk by chunk in different commits.

Author: Sandeep <sandeep@techaddict.me>

Closes #380 from techaddict/white_space and squashes the following commits:

b58f294 [Sandeep] Remove Unnecessary Whitespace's
[SQL] Improve column pruning in the optimizer.

Author: Michael Armbrust <michael@databricks.com>

Closes #378 from marmbrus/columnPruning and squashes the following commits:

779da56 [Michael Armbrust] More consistent naming.
1a4e9ea [Michael Armbrust] More comments.
2f4e7b9 [Michael Armbrust] Improve column pruning in the optimizer.
SPARK-1202 - Add a "cancel" button in the UI for stages

Author: Sundeep Narravula <sundeepn@superduel.local>
Author: Sundeep Narravula <sundeepn@dhcpx-204-110.corp.yahoo.com>

Closes #246 from sundeepn/uikilljob and squashes the following commits:

5fdd0e2 [Sundeep Narravula] Fix test string
f6fdff1 [Sundeep Narravula] Format fix; reduced line size to less than 100 chars
d1daeb9 [Sundeep Narravula] Incorporating review comments.
8d97923 [Sundeep Narravula] Ability to kill jobs thru the UI. This behavior can be turned on be settings the following variable: spark.ui.killEnabled=true (default=false) Adding DAGScheduler event StageCancelled and corresponding handlers. Added cancellation reason to handlers.
Set spark.executor.uri from environment variable (needed by Mesos)

The Mesos backend uses this property when setting up a slave process.  It is similarly set in the Scala repl (org.apache.spark.repl.SparkILoop), but I couldn't find any analogous for pyspark.

Author: Ivan Wick <ivanwick+github@gmail.com>

This patch had conflicts when merged, resolved by
Committer: Matei Zaharia <matei@databricks.com>

Closes #311 from ivanwick/master and squashes the following commits:

da0c3e4 [Ivan Wick] Set spark.executor.uri from environment variable (needed by Mesos)
Add Spark v0.9.1 to ec2 launch script and use it as the default

Mainly ported from branch-0.9.

Author: Harvey Feng <hyfeng224@gmail.com>

Closes #385 from harveyfeng/0.9.1-ec2 and squashes the following commits:

769ac2f [Harvey Feng] Add Spark v0.9.1 to ec2 launch script and use it as the default
SPARK-1202: Improvements to task killing in the UI.

1. Adds a separate endpoint for the killing logic that is outside of a page.
2. Narrows the scope of the killingEnabled tracking.
3. Some style improvements.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #386 from pwendell/kill-link and squashes the following commits:

8efe02b [Patrick Wendell] Improvements to task killing in the UI.
SPARK-1417: Spark on Yarn - spark UI link from resourcemanager is broken

Author: Thomas Graves <tgraves@apache.org>

Closes #344 from tgravescs/SPARK-1417 and squashes the following commits:

c450b5f [Thomas Graves] fix test
e1c1d7e [Thomas Graves] add missing $ to appUIAddress
e982ddb [Thomas Graves] use appUIHostPort in appUIAddress
0803ec2 [Thomas Graves] Review comment updates - remove extra newline, simplify assert in test
658a8ec [Thomas Graves] Add a appUIHostPort routine
0614208 [Thomas Graves] Fix test
2a6b1b7 [Thomas Graves] SPARK-1417: Spark on Yarn - spark UI link from resourcemanager is broken
Some clean up in build/docs

(a) Deleted an outdated line from the docs
(b) Removed a work around that is no longer necessary given the mesos version bump.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #382 from pwendell/maven-clean and squashes the following commits:

f0447fa [Patrick Wendell] Minor doc clean-up
[SPARK-1225, 1241] [MLLIB] Add AreaUnderCurve and BinaryClassificationMetrics

This PR implements a generic version of `AreaUnderCurve` using the `RDD.sliding` implementation from https://github.com/apache/spark/pull/136 . It also contains refactoring of https://github.com/apache/spark/pull/160 for binary classification evaluation.

Author: Xiangrui Meng <meng@databricks.com>

Closes #364 from mengxr/auc and squashes the following commits:

a05941d [Xiangrui Meng] replace TP/FP/TN/FN by their full names
3f42e98 [Xiangrui Meng] add (0, 0), (1, 1) to roc, and (0, 1) to pr
fb4b6d2 [Xiangrui Meng] rename Evaluator to Metrics and add more metrics
b1b7dab [Xiangrui Meng] fix code styles
9dc3518 [Xiangrui Meng] add tests for BinaryClassificationEvaluator
ca31da5 [Xiangrui Meng] remove PredictionAndResponse
3d71525 [Xiangrui Meng] move binary evalution classes to evaluation.binary
8f78958 [Xiangrui Meng] add PredictionAndResponse
dda82d5 [Xiangrui Meng] add confusion matrix
aa7e278 [Xiangrui Meng] add initial version of binary classification evaluator
221ebce [Xiangrui Meng] add a new test to sliding
a920865 [Xiangrui Meng] Merge branch 'sliding' into auc
a9b250a [Xiangrui Meng] move sliding to mllib
cab9a52 [Xiangrui Meng] use last for the last element
db6cb30 [Xiangrui Meng] remove unnecessary toSeq
9916202 [Xiangrui Meng] change RDD.sliding return type to RDD[Seq[T]]
284d991 [Xiangrui Meng] change SlidedRDD to SlidingRDD
c1c6c22 [Xiangrui Meng] add AreaUnderCurve
65461b2 [Xiangrui Meng] Merge branch 'sliding' into auc
5ee6001 [Xiangrui Meng] add TODO
d2a600d [Xiangrui Meng] add sliding to rdd
HOTFIX: Ignore python metastore files in RAT checks.

This was causing some errors with pull request tests.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #393 from pwendell/hotfix and squashes the following commits:

6201dd3 [Patrick Wendell] HOTFIX: Ignore python metastore files in RAT checks.
[FIX] make coalesce test deterministic in RDDSuite

Make coalesce test deterministic by setting pre-defined seeds. (Saw random failures in other PRs.)

Author: Xiangrui Meng <meng@databricks.com>

Closes #387 from mengxr/fix-random and squashes the following commits:

59bc16f [Xiangrui Meng] make coalesce test deterministic in RDDSuite
[WIP] [SPARK-1328] Add vector statistics

As with the new vector system in MLlib, we find that it is good to add some new APIs to precess the `RDD[Vector]`. Beside, the former implementation of `computeStat` is not stable which could loss precision, and has the possibility to cause `Nan` in scientific computing, just as said in the [SPARK-1328](https://spark-project.atlassian.net/browse/SPARK-1328).

APIs contain:

* rowMeans(): RDD[Double]
* rowNorm2(): RDD[Double]
* rowSDs(): RDD[Double]
* colMeans(): Vector
* colMeans(size: Int): Vector
* colNorm2(): Vector
* colNorm2(size: Int): Vector
* colSDs(): Vector
* colSDs(size: Int): Vector
* maxOption((Vector, Vector) => Boolean): Option[Vector]
* minOption((Vector, Vector) => Boolean): Option[Vector]
* rowShrink(): RDD[Vector]
* colShrink(): RDD[Vector]

This is working in process now, and some more APIs will add to `LabeledPoint`. Moreover, the implicit declaration will move from `MLUtils` to `MLContext` later.

Author: Xusen Yin <yinxusen@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>

Closes #268 from yinxusen/vector-statistics and squashes the following commits:

d61363f [Xusen Yin] rebase to latest master
16ae684 [Xusen Yin] fix minor error and remove useless method
10cf5d3 [Xusen Yin] refine some return type
b064714 [Xusen Yin] remove computeStat in MLUtils
cbbefdb [Xiangrui Meng] update multivariate statistical summary interface and clean tests
4eaf28a [Xusen Yin] merge VectorRDDStatistics into RowMatrix
48ee053 [Xusen Yin] fix minor error
e624f93 [Xusen Yin] fix scala style error
1fba230 [Xusen Yin] merge while loop together
69e1f37 [Xusen Yin] remove lazy eval, and minor memory footprint
548e9de [Xusen Yin] minor revision
86522c4 [Xusen Yin] add comments on functions
dc77e38 [Xusen Yin] test sparse vector RDD
18cf072 [Xusen Yin] change def to lazy val to make sure that the computations in function be evaluated only once
f7a3ca2 [Xusen Yin] fix the corner case of maxmin
967d041 [Xusen Yin] full revision with Aggregator class
138300c [Xusen Yin] add new Aggregator class
1376ff4 [Xusen Yin] rename variables and adjust code
4a5c38d [Xusen Yin] add scala doc, refine code and comments
036b7a5 [Xusen Yin] fix the bug of Nan occur
f6e8e9a [Xusen Yin] add sparse vectors test
4cfbadf [Xusen Yin] fix bug of min max
4e4fbd1 [Xusen Yin] separate seqop and combop out as independent functions
a6d5a2e [Xusen Yin] rewrite for only computing non-zero elements
3980287 [Xusen Yin] rename variables
62a2c3e [Xusen Yin] use axpy and in-place if possible
9a75ebd [Xusen Yin] add case class to wrap return values
d816ac7 [Xusen Yin] remove useless APIs
c4651bb [Xusen Yin] remove row-wise APIs and refine code
1338ea1 [Xusen Yin] all-in-one version test passed
cc65810 [Xusen Yin] add parallel mean and variance
9af2e95 [Xusen Yin] refine the code style
ad6c82d [Xusen Yin] add shrink test
e09d5d2 [Xusen Yin] add scala docs and refine shrink method
8ef3377 [Xusen Yin] pass all tests
28cf060 [Xusen Yin] fix error of column means
54b19ab [Xusen Yin] add new API to shrink RDD[Vector]
8c6c0e1 [Xusen Yin] add basic statistics