## What changes were proposed in this pull request?

This PR allows to use `SerializationStream` and `DeserializationStream` in try-with-resources.

## How was this patch tested?

`core` unit tests.

Author: Sergei Lebedev <s.lebedev@criteo.com>

Closes #17598 from superbobry/compression-stream-closeable.

## What changes were proposed in this pull request?

`o.a.s.streaming.StreamingContextSuite.SPARK-18560 Receiver data should be deserialized properly` is flaky is because there is a potential dead-lock in StandaloneSchedulerBackend which causes `await` timeout. Here is the related stack trace:
```
"Thread-31" #211 daemon prio=5 os_prio=31 tid=0x00007fedd4808000 nid=0x16403 waiting on condition [0x00007000239b7000]
   java.lang.Thread.State: TIMED_WAITING (parking)
	at sun.misc.Unsafe.park(Native Method)
	- parking to wait for  <0x000000079b49ca10> (a scala.concurrent.impl.Promise$CompletionLatch)
	at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)
	at java.util.concurrent.locks.AbstractQueuedSynchronizer.doAcquireSharedNanos(AbstractQueuedSynchronizer.java:1037)
	at java.util.concurrent.locks.AbstractQueuedSynchronizer.tryAcquireSharedNanos(AbstractQueuedSynchronizer.java:1328)
	at scala.concurrent.impl.Promise$DefaultPromise.tryAwait(Promise.scala:208)
	at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:218)
	at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
	at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:201)
	at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
	at org.apache.spark.rpc.RpcEndpointRef.askSync(RpcEndpointRef.scala:92)
	at org.apache.spark.rpc.RpcEndpointRef.askSync(RpcEndpointRef.scala:76)
	at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.stop(CoarseGrainedSchedulerBackend.scala:402)
	at org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend.org$apache$spark$scheduler$cluster$StandaloneSchedulerBackend$$stop(StandaloneSchedulerBackend.scala:213)
	- locked <0x00000007066fca38> (a org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend)
	at org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend.stop(StandaloneSchedulerBackend.scala:116)
	- locked <0x00000007066fca38> (a org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend)
	at org.apache.spark.scheduler.TaskSchedulerImpl.stop(TaskSchedulerImpl.scala:517)
	at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:1657)
	at org.apache.spark.SparkContext$$anonfun$stop$8.apply$mcV$sp(SparkContext.scala:1921)
	at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1302)
	at org.apache.spark.SparkContext.stop(SparkContext.scala:1920)
	at org.apache.spark.streaming.StreamingContext.stop(StreamingContext.scala:708)
	at org.apache.spark.streaming.StreamingContextSuite$$anonfun$43$$anonfun$apply$mcV$sp$66$$anon$3.run(StreamingContextSuite.scala:827)

"dispatcher-event-loop-3" #18 daemon prio=5 os_prio=31 tid=0x00007fedd603a000 nid=0x6203 waiting for monitor entry [0x0000700003be4000]
   java.lang.Thread.State: BLOCKED (on object monitor)
	at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint.org$apache$spark$scheduler$cluster$CoarseGrainedSchedulerBackend$DriverEndpoint$$makeOffers(CoarseGrainedSchedulerBackend.scala:253)
	- waiting to lock <0x00000007066fca38> (a org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend)
	at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint$$anonfun$receive$1.applyOrElse(CoarseGrainedSchedulerBackend.scala:124)
	at org.apache.spark.rpc.netty.Inbox$$anonfun$process$1.apply$mcV$sp(Inbox.scala:117)
	at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:205)
	at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:101)
	at org.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:213)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	at java.lang.Thread.run(Thread.java:745)
```

This PR removes `synchronized` and changes `stopping` to AtomicBoolean to ensure idempotent to fix the dead-lock.

## How was this patch tested?

Jenkins

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #17610 from zsxwing/SPARK-20131.

## What changes were proposed in this pull request?

`TopologyAwareBlockReplicationPolicyBehavior.Peers in 2 racks` is failing occasionally: https://spark-tests.appspot.com/test-details?suite_name=org.apache.spark.storage.TopologyAwareBlockReplicationPolicyBehavior&test_name=Peers+in+2+racks.

This is because, when we generate 10 block manager id to test, they may all belong to the same rack, as the rack is randomly picked. This PR fixes this problem by forcing each rack to be picked at least once.

## How was this patch tested?

N/A

Author: Wenchen Fan <wenchen@databricks.com>

Closes #17624 from cloud-fan/test.

## What changes were proposed in this pull request?

This PR proposes to run Spark unidoc to test Javadoc 8 build as Javadoc 8 is easily re-breakable.

There are several problems with it:

- It introduces little extra bit of time to run the tests. In my case, it took 1.5 mins more (`Elapsed :[94.8746569157]`). How it was tested is described in "How was this patch tested?".

- > One problem that I noticed was that Unidoc appeared to be processing test sources: if we can find a way to exclude those from being processed in the first place then that might significantly speed things up.

  (see  joshrosen's [comment](https://issues.apache.org/jira/browse/SPARK-18692?focusedCommentId=15947627&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15947627))

To complete this automated build, It also suggests to fix existing Javadoc breaks / ones introduced by test codes as described above.

There fixes are similar instances that previously fixed. Please refer https://github.com/apache/spark/pull/15999 and https://github.com/apache/spark/pull/16013

Note that this only fixes **errors** not **warnings**. Please see my observation https://github.com/apache/spark/pull/17389#issuecomment-288438704 for spurious errors by warnings.

## How was this patch tested?

Manually via `jekyll build` for building tests. Also, tested via running `./dev/run-tests`.

This was tested via manually adding `time.time()` as below:

```diff
     profiles_and_goals = build_profiles + sbt_goals

     print("[info] Building Spark unidoc (w/Hive 1.2.1) using SBT with these arguments: ",
           " ".join(profiles_and_goals))

+    import time
+    st = time.time()
     exec_sbt(profiles_and_goals)
+    print("Elapsed :[%s]" % str(time.time() - st))
```

produces

```
...
========================================================================
Building Unidoc API Documentation
========================================================================
...
[info] Main Java API documentation successful.
...
Elapsed :[94.8746569157]
...

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #17477 from HyukjinKwon/SPARK-18692.

[SPARK-20156][CORE][SQL][STREAMING][MLLIB] Java String toLowerCase "Turkish locale bug" causes Spark problems

## What changes were proposed in this pull request?

Add Locale.ROOT to internal calls to String `toLowerCase`, `toUpperCase`, to avoid inadvertent locale-sensitive variation in behavior (aka the "Turkish locale problem").

The change looks large but it is just adding `Locale.ROOT` (the locale with no country or language specified) to every call to these methods.

## How was this patch tested?

Existing tests.

Author: Sean Owen <sowen@cloudera.com>

Closes #17527 from srowen/SPARK-20156.

## What changes were proposed in this pull request?

Synchronize access to openStreams map.

## How was this patch tested?

Existing tests.

Author: Bogdan Raducanu <bogdan@databricks.com>

Closes #17592 from bogdanrdc/SPARK-20243.

## What changes were proposed in this pull request?
This error message doesn't get properly formatted because of a missing `s`.  Currently the error looks like:

```
Caused by: java.lang.IllegalArgumentException: requirement failed: indices should be one-based and in ascending order; found current=$current, previous=$previous; line="$line"
```
(note the literal `$current` instead of the interpolated value)

Please review http://spark.apache.org/contributing.html before opening a pull request.

Author: Vijay Ramesh <vramesh@demandbase.com>

Closes #17572 from vijaykramesh/master.

## What changes were proposed in this pull request?

Avoid `NoSuchElementException` every time `ConfigProvider.get(val, default)` falls back to default. This apparently causes non-trivial overhead in at least one path, and can easily be avoided.

See https://github.com/apache/spark/pull/17329

## How was this patch tested?

Existing tests

Author: Sean Owen <sowen@cloudera.com>

Closes #17567 from srowen/SPARK-19991.

## What changes were proposed in this pull request?

Came across the term "slice" when running some spark scala code. Consequently, a Google search indicated that "slices" and "partitions" refer to the same things; indeed see:

- [This issue](https://issues.apache.org/jira/browse/SPARK-1701)
- [This pull request](https://github.com/apache/spark/pull/2305)
- [This StackOverflow answer](http://stackoverflow.com/questions/23436640/what-is-the-difference-between-an-rdd-partition-and-a-slice) and [this one](http://stackoverflow.com/questions/24269495/what-are-the-differences-between-slices-and-partitions-of-rdds)

Thus this pull request fixes the occurrence of slice I came accross. Nonetheless, [it would appear](https://github.com/apache/spark/search?utf8=%E2%9C%93&q=slice&type=) there are still many references to "slice/slices" - thus I thought I'd raise this Pull Request to address the issue (sorry if this is the wrong place, I'm not too familar with raising apache issues).

## How was this patch tested?

(Not tested locally - only a minor exception message change.)

Please review http://spark.apache.org/contributing.html before opening a pull request.

Author: asmith26 <asmith26@users.noreply.github.com>

Closes #17565 from asmith26/master.

## What changes were proposed in this pull request?

With [SPARK-13992](https://issues.apache.org/jira/browse/SPARK-13992), Spark supports persisting data into off-heap memory, but the usage of on-heap and off-heap memory is not exposed currently, it is not so convenient for user to monitor and profile, so here propose to expose off-heap memory as well as on-heap memory usage in various places:
1. Spark UI's executor page will display both on-heap and off-heap memory usage.
2. REST request returns both on-heap and off-heap memory.
3. Also this can be gotten from MetricsSystem.
4. Last this usage can be obtained programmatically from SparkListener.

Attach the UI changes:

![screen shot 2016-08-12 at 11 20 44 am](https://cloud.githubusercontent.com/assets/850797/17612032/6c2f4480-607f-11e6-82e8-a27fb8cbb4ae.png)

Backward compatibility is also considered for event-log and REST API. Old event log can still be replayed with off-heap usage displayed as 0. For REST API, only adds the new fields, so JSON backward compatibility can still be kept.
## How was this patch tested?

Unit test added and manual verification.

Author: jerryshao <sshao@hortonworks.com>

Closes #14617 from jerryshao/SPARK-17019.

## What changes were proposed in this pull request?

If tasks throw non-interrupted exceptions on kill (e.g. java.nio.channels.ClosedByInterruptException), their death is reported back as TaskFailed instead of TaskKilled. This causes stage failure in some cases.

This is reproducible as follows. Run the following, and then use SparkContext.killTaskAttempt to kill one of the tasks. The entire stage will fail since we threw a RuntimeException instead of InterruptedException.

```
spark.range(100).repartition(100).foreach { i =>
  try {
    Thread.sleep(10000000)
  } catch {
    case t: InterruptedException =>
      throw new RuntimeException(t)
  }
}
```
Based on the code in TaskSetManager, I think this also affects kills of speculative tasks. However, since the number of speculated tasks is few, and usually you need to fail a task a few times before the stage is cancelled, it unlikely this would be noticed in production unless both speculation was enabled and the num allowed task failures was = 1.

We should probably unconditionally return TaskKilled instead of TaskFailed if the task was killed by the driver, regardless of the actual exception thrown.

## How was this patch tested?

Unit test. The test fails before the change in Executor.scala

cc JoshRosen

Author: Eric Liang <ekl@databricks.com>

Closes #17531 from ericl/fix-task-interrupt.

## What changes were proposed in this pull request?
Make sure SESSION_LOCAL_TIMEZONE reflects the change in JVM's default timezone setting. Currently several timezone related tests fail as the change to default timezone is not picked up by SQLConf.

## How was this patch tested?
Added an unit test in ConfigEntrySuite

Author: Dilip Biswal <dbiswal@us.ibm.com>

Closes #17537 from dilipbiswal/timezone_debug.

## What changes were proposed in this pull request?

When a user kills a stage using web UI (in Stages page), StagesTab.handleKillRequest requests SparkContext to cancel the stage without giving a reason. SparkContext has cancelStage(stageId: Int, reason: String) that Spark could use to pass the information for monitoring/debugging purposes.

## How was this patch tested?

manual tests

Please review http://spark.apache.org/contributing.html before opening a pull request.

Author: shaolinliu <liu.shaolin1@zte.com.cn>
Author: lvdongr <lv.dongdong@zte.com.cn>

Closes #17258 from shaolinliu/SPARK-19807.

with spark.ui.reverseProxy=true, full path URLs like /log will point to
the master web endpoint which is serving the worker UI as reverse proxy.
To access a REST endpoint in the worker in reverse proxy mode , the
leading /proxy/"target"/ part of the base URI must be retained.

Added logic to log-view.js to handle this, similar to executorspage.js

Patch was tested manually

Author: Oliver Köth <okoeth@de.ibm.com>

Closes #17370 from okoethibm/master.

[SPARK-9002][CORE] KryoSerializer initialization does not include 'Array[Int]'

## What changes were proposed in this pull request?

Array[Int] has been registered in KryoSerializer.
The following file has been changed
core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala

## How was this patch tested?

First, the issue was reproduced by new unit test.
Then, the issue was fixed to pass the failed test.

Author: Denis Bolshakov <denis.bolshakov@onefactor.com>

Closes #17482 from dbolshak/SPARK-9002.

## What changes were proposed in this pull request?

Remove accumulator updates for internal.metrics.updatedBlockStatuses from SparkListenerTaskEnd entries in the history file. These can cause history files to grow to hundreds of GB because the value of the accumulator contains all tracked blocks.

## How was this patch tested?

Current History UI tests cover use of the history file.

Author: Ryan Blue <blue@apache.org>

Closes #17412 from rdblue/SPARK-20084-remove-block-accumulator-info.

## What changes were proposed in this pull request?
Few changes related to Intellij IDEA inspection.

## How was this patch tested?
Changes were tested by existing unit tests

Author: Denis Bolshakov <denis.bolshakov@onefactor.com>

Closes #17458 from dbolshak/SPARK-20127.

[SPARK-20096][SPARK SUBMIT][MINOR] Expose the right queue name not null if set by --conf or configure file

## What changes were proposed in this pull request?

while submit apps with -v or --verbose， we can print the right queue name, but if we set a queue name with `spark.yarn.queue` by --conf or in the spark-default.conf, we just got `null`  for the queue in Parsed arguments.
```
bin/spark-shell -v --conf spark.yarn.queue=thequeue
Using properties file: /home/hadoop/spark-2.1.0-bin-apache-hdp2.7.3/conf/spark-defaults.conf
....
Adding default property: spark.yarn.queue=default
Parsed arguments:
  master                  yarn
  deployMode              client
  ...
  queue                   null
  ....
  verbose                 true
Spark properties used, including those specified through
 --conf and those from the properties file /home/hadoop/spark-2.1.0-bin-apache-hdp2.7.3/conf/spark-defaults.conf:
  spark.yarn.queue -> thequeue
  ....
```
## How was this patch tested?

ut and local verify

Author: Kent Yao <yaooqinn@hotmail.com>

Closes #17430 from yaooqinn/SPARK-20096.

…adoc

## What changes were proposed in this pull request?

Use recommended values for row boundaries in Window's scaladoc, i.e. `Window.unboundedPreceding`, `Window.unboundedFollowing`, and `Window.currentRow` (that were introduced in 2.1.0).

## How was this patch tested?

Local build

Author: Jacek Laskowski <jacek@japila.pl>

Closes #17417 from jaceklaskowski/window-expression-scaladoc.

## What changes were proposed in this pull request?

Implementations of strategies for resilient block replication for different resource managers that replicate the 3-replica strategy used by HDFS, where the first replica is on an executor, the second replica within the same rack as the executor and a third replica on a different rack.
The implementation involves providing two pluggable classes, one running in the driver that provides topology information for every host at cluster start and the second prioritizing a list of peer BlockManagerIds.

The prioritization itself can be thought of an optimization problem to find a minimal set of peers that satisfy certain objectives and replicating to these peers first. The objectives can be used to express richer constraints over and above HDFS like 3-replica strategy.
## How was this patch tested?

This patch was tested with unit tests for storage, along with new unit tests to verify prioritization behaviour.

Author: Shubham Chopra <schopra31@bloomberg.net>

Closes #13932 from shubhamchopra/PrioritizerStrategy.

## What changes were proposed in this pull request?

The internal FileCommitProtocol interface returns all task commit messages in bulk to the implementation when a job finishes. However, it is sometimes useful to access those messages before the job completes, so that the driver gets incremental progress updates before the job finishes.

This adds an `onTaskCommit` listener to the internal api.

## How was this patch tested?

Unit tests.

cc rxin

Author: Eric Liang <ekl@databricks.com>

Closes #17475 from ericl/file-commit-api-ext.

## What changes were proposed in this pull request?

Currently we use system classloader to find HBase jars, if it is specified by `--jars`, then it will be failed with ClassNotFound issue. So here changing to use child classloader.

Also putting added jars and main jar into classpath of submitted application in yarn cluster mode, otherwise HBase jars specified with `--jars` will never be honored in cluster mode, and fetching tokens in client side will always be failed.

## How was this patch tested?

Unit test and local verification.

Author: jerryshao <sshao@hortonworks.com>

Closes #17388 from jerryshao/SPARK-20059.

This change modifies the way block data is encrypted to make the more
common cases faster, while penalizing an edge case. As a side effect
of the change, all data that goes through the block manager is now
encrypted only when needed, including the previous path (broadcast
variables) where that did not happen.

The way the change works is by not encrypting data that is stored in
memory; so if a serialized block is in memory, it will only be encrypted
once it is evicted to disk.

The penalty comes when transferring that encrypted data from disk. If the
data ends up in memory again, it is as efficient as before; but if the
evicted block needs to be transferred directly to a remote executor, then
there's now a performance penalty, since the code now uses a custom
FileRegion implementation to decrypt the data before transferring.

This also means that block data transferred between executors now is
not encrypted (and thus relies on the network library encryption support
for secrecy). Shuffle blocks are still transferred in encrypted form,
since they're handled in a slightly different way by the code. This also
keeps compatibility with existing external shuffle services, which transfer
encrypted shuffle blocks, and avoids having to make the external service
aware of encryption at all.

The serialization and deserialization APIs in the SerializerManager now
do not do encryption automatically; callers need to explicitly wrap their
streams with an appropriate crypto stream before using those.

As a result of these changes, some of the workarounds added in SPARK-19520
are removed here.

Testing: a new trait ("EncryptionFunSuite") was added that provides an easy
way to run a test twice, with encryption on and off; broadcast, block manager
and caching tests were modified to use this new trait so that the existing
tests exercise both encrypted and non-encrypted paths. I also ran some
applications with encryption turned on to verify that they still work,
including streaming tests that failed without the fix for SPARK-19520.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #17295 from vanzin/SPARK-19556.

## What changes were proposed in this pull request?

We must set the taskset to zombie before the DAGScheduler handles the taskEnded event. It's possible the taskEnded event will cause the DAGScheduler to launch a new stage attempt (this happens when map output data was lost), and if this happens before the taskSet has been set to zombie, it will appear that we have conflicting task sets.

Author: liujianhui <liujianhui@didichuxing>

Closes #17208 from liujianhuiouc/spark-19868.

## What changes were proposed in this pull request?
Executors cache a list of their peers that is refreshed by default every minute. The cached stale references were randomly being used for replication. Since those executors were removed from the master, they did not occur in the block locations as reported by the master. This was fixed by
1. Refreshing peer cache in the block manager before trying to pro-actively replicate. This way the probability of replicating to a failed executor is eliminated.
2. Explicitly stopping the block manager in the tests. This shuts down the RPC endpoint use by the block manager. This way, even if a block manager tries to replicate using a stale reference, the replication logic should take care of refreshing the list of peers after failure.

## How was this patch tested?
Tested manually

Author: Shubham Chopra <schopra31@bloomberg.net>
Author: Kay Ousterhout <kayousterhout@gmail.com>
Author: Shubham Chopra <shubhamchopra@users.noreply.github.com>

Closes #17325 from shubhamchopra/SPARK-19803.

## What changes were proposed in this pull request?
Instead of creating new `JavaSparkContext` we use `SparkContext.getOrCreate`.

## How was this patch tested?
Existing tests

Author: Hossein <hossein@databricks.com>

Closes #17423 from falaki/SPARK-20088.

## What changes were proposed in this pull request?
Adding additional information to existing logging messages:
  - YarnAllocator: log the executor ID together with the container id when a container for an executor is launched.
  - NettyRpcEnv: log the receiver address when there is a timeout waiting for an answer to a remote call.
  - ExecutorAllocationManager: fix a typo in the logging message for the list of executors to be removed.

## How was this patch tested?
Build spark and submit the word count example to a YARN cluster using cluster mode

Author: Juan Rodriguez Hortala <hortala@amazon.com>

Closes #17411 from juanrh/logging-improvements.

## What changes were proposed in this pull request?
Commit https://github.com/apache/spark/commit/91fa80fe8a2480d64c430bd10f97b3d44c007bcc broke the build for scala 2.10. The commit uses `Regex.regex` field which is not available in Scala 2.10. This PR fixes this.

## How was this patch tested?
Existing tests.

Author: Herman van Hovell <hvanhovell@databricks.com>

Closes #17420 from hvanhovell/SPARK-20070-2.0.

## What changes were proposed in this pull request?
The explain output of `DataSourceScanExec` can contain sensitive information (like Amazon keys). Such information should not end up in logs, or be exposed to non privileged users.

This PR addresses this by adding a redaction facility for the `DataSourceScanExec.treeString`. A user can enable this by setting a regex in the `spark.redaction.string.regex` configuration.

## How was this patch tested?
Added a unit test to check the output of DataSourceScanExec.

Author: Herman van Hovell <hvanhovell@databricks.com>

Closes #17397 from hvanhovell/SPARK-20070.

This commit adds a killTaskAttempt method to SparkContext, to allow users to
kill tasks so that they can be re-scheduled elsewhere.

This also refactors the task kill path to allow specifying a reason for the task kill. The reason is propagated opaquely through events, and will show up in the UI automatically as `(N killed: $reason)` and `TaskKilled: $reason`. Without this change, there is no way to provide the user feedback through the UI.

Currently used reasons are "stage cancelled", "another attempt succeeded", and "killed via SparkContext.killTask". The user can also specify a custom reason through `SparkContext.killTask`.

cc rxin

In the stage overview UI the reasons are summarized:
![1](https://cloud.githubusercontent.com/assets/14922/23929209/a83b2862-08e1-11e7-8b3e-ae1967bbe2e5.png)

Within the stage UI you can see individual task kill reasons:
![2](https://cloud.githubusercontent.com/assets/14922/23929200/9a798692-08e1-11e7-8697-72b27ad8a287.png)

Existing tests, tried killing some stages in the UI and verified the messages are as expected.

Author: Eric Liang <ekl@databricks.com>
Author: Eric Liang <ekl@google.com>

Closes #17166 from ericl/kill-reason.

## What changes were proposed in this pull request?
1. Use a MedianHeap to record durations of successful tasks.  When check speculatable tasks, we can get the median duration with O(1) time complexity.

2. `checkSpeculatableTasks` will synchronize `TaskSchedulerImpl`. If `checkSpeculatableTasks` doesn't finish with 100ms, then the possibility exists for that thread to release and then immediately re-acquire the lock. Change `scheduleAtFixedRate` to be `scheduleWithFixedDelay` when call method of `checkSpeculatableTasks`.
## How was this patch tested?
Added MedianHeapSuite.

Author: jinxing <jinxing6042@126.com>

Closes #16867 from jinxing64/SPARK-16929.

## What changes were proposed in this pull request?
Some `Schedulable` Entities(`Pool` and `TaskSetManager`) variables need refactoring for _immutability_ and _access modifiers_ levels as follows:
- From `var` to `val` (if there is no requirement): This is important to support immutability as much as possible.
  - Sample => `Pool`: `weight`, `minShare`, `priority`, `name` and `taskSetSchedulingAlgorithm`.
- Access modifiers: Specially, `var`s access needs to be restricted from other parts of codebase to prevent potential side effects.
  - `TaskSetManager`: `tasksSuccessful`, `totalResultSize`, `calculatedTasks` etc...

This PR is related with #15604 and has been created seperatedly to keep patch content as isolated and to help the reviewers.

## How was this patch tested?
Added new UTs and existing UT coverage.

Author: erenavsarogullari <erenavsarogullari@gmail.com>

Closes #16905 from erenavsarogullari/SPARK-19567.

## What changes were proposed in this pull request?

Several javadoc8 breaks have been introduced. This PR proposes fix those instances so that we can build Scala/Java API docs.

```
[error] .../spark/sql/core/target/java/org/apache/spark/sql/streaming/GroupState.java:6: error: reference not found
[error]  * <code>flatMapGroupsWithState</code> operations on {link KeyValueGroupedDataset}.
[error]                                                             ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/streaming/GroupState.java:10: error: reference not found
[error]  * Both, <code>mapGroupsWithState</code> and <code>flatMapGroupsWithState</code> in {link KeyValueGroupedDataset}
[error]                                                                                            ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/streaming/GroupState.java:51: error: reference not found
[error]  *    {link GroupStateTimeout.ProcessingTimeTimeout}) or event time (i.e.
[error]              ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/streaming/GroupState.java:52: error: reference not found
[error]  *    {link GroupStateTimeout.EventTimeTimeout}).
[error]              ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/streaming/GroupState.java:158: error: reference not found
[error]  *           Spark SQL types (see {link Encoder} for more details).
[error]                                          ^
[error] .../spark/mllib/target/java/org/apache/spark/ml/fpm/FPGrowthParams.java:26: error: bad use of '>'
[error]    * Number of partitions (>=1) used by parallel FP-growth. By default the param is not set, and
[error]                            ^
[error] .../spark/sql/core/src/main/java/org/apache/spark/api/java/function/FlatMapGroupsWithStateFunction.java:30: error: reference not found
[error]  * {link org.apache.spark.sql.KeyValueGroupedDataset#flatMapGroupsWithState(
[error]           ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyValueGroupedDataset.java:211: error: reference not found
[error]    * See {link GroupState} for more details.
[error]                 ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyValueGroupedDataset.java:232: error: reference not found
[error]    * See {link GroupState} for more details.
[error]                 ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyValueGroupedDataset.java:254: error: reference not found
[error]    * See {link GroupState} for more details.
[error]                 ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyValueGroupedDataset.java:277: error: reference not found
[error]    * See {link GroupState} for more details.
[error]                 ^
[error] .../spark/core/target/java/org/apache/spark/TaskContextImpl.java:10: error: reference not found
[error]  * {link TaskMetrics} &amp; {link MetricsSystem} objects are not thread safe.
[error]           ^
[error] .../spark/core/target/java/org/apache/spark/TaskContextImpl.java:10: error: reference not found
[error]  * {link TaskMetrics} &amp; {link MetricsSystem} objects are not thread safe.
[error]                                     ^
[info] 13 errors
```

```
jekyll 3.3.1 | Error:  Unidoc generation failed
```

## How was this patch tested?

Manually via `jekyll build`

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #17389 from HyukjinKwon/minor-javadoc8-fix.

## What changes were proposed in this pull request?
SparkR ```spark.getSparkFiles``` fails when it was called on executors, see details at [SPARK-19925](https://issues.apache.org/jira/browse/SPARK-19925).

## How was this patch tested?
Add unit tests, and verify this fix at standalone and yarn cluster.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #17274 from yanboliang/spark-19925.

## What changes were proposed in this pull request?

"java.lang.Exception: Could not compute split, block $blockId not found" doesn't have the rdd id info, the "BlockManager: Removing RDD $id" has only the RDD id, so it couldn't find that the Exception's reason is the Removing; so it's better block not found Exception add RDD id info

## How was this patch tested?

Existing tests

Author: jianran.tfh <jianran.tfh@taobao.com>
Author: jianran <tanfanhua1984@163.com>

Closes #17334 from jianran/SPARK-19998.

(Jira: https://issues.apache.org/jira/browse/SPARK-17204)

## What changes were proposed in this pull request?

There are a couple of bugs in the `BlockManager` with respect to support for replicated off-heap storage. First, the locally-stored off-heap byte buffer is disposed of when it is replicated. It should not be. Second, the replica byte buffers are stored as heap byte buffers instead of direct byte buffers even when the storage level memory mode is off-heap. This PR addresses both of these problems.

## How was this patch tested?

`BlockManagerReplicationSuite` was enhanced to fill in the coverage gaps. It now fails if either of the bugs in this PR exist.

Author: Michael Allman <michael@videoamp.com>

Closes #16499 from mallman/spark-17204-replicated_off_heap_storage.

## What changes were proposed in this pull request?

Passes R `tempdir()` (this is the R session temp dir, shared with other temp files/dirs) to JVM, set System.Property for derby home dir to move derby.log

## How was this patch tested?

Manually, unit tests

With this, these are relocated to under /tmp
```
# ls /tmp/RtmpG2M0cB/
derby.log
```
And they are removed automatically when the R session is ended.

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #16330 from felixcheung/rderby.

[SPARK-16599][CORE] java.util.NoSuchElementException: None.get at at org.apache.spark.storage.BlockInfoManager.releaseAllLocksForTask

## What changes were proposed in this pull request?

Avoid None.get exception in (rare?) case that no readLocks exist
Note that while this would resolve the immediate cause of the exception, it's not clear it is the root problem.

## How was this patch tested?

Existing tests

Author: Sean Owen <sowen@cloudera.com>

Closes #17290 from srowen/SPARK-16599.

The previously hardcoded max 4 retries per stage is not suitable for all cluster configurations. Since spark retries a stage at the sign of the first fetch failure, you can easily end up with many stage retries to discover all the failures. In particular, two scenarios this value should change are (1) if there are more than 4 executors per node; in that case, it may take 4 retries to discover the problem with each executor on the node and (2) during cluster maintenance on large clusters, where multiple machines are serviced at once, but you also cannot afford total cluster downtime. By making this value configurable, cluster managers can tune this value to something more appropriate to their cluster configuration.

Unit tests

Author: Sital Kedia <skedia@fb.com>

Closes #17307 from sitalkedia/SPARK-13369.

[SPARK-19946][TESTING] DebugFilesystem.assertNoOpenStreams should report the open streams to help debugging

## What changes were proposed in this pull request?

DebugFilesystem.assertNoOpenStreams throws an exception with a cause exception that actually shows the code line which leaked the stream.

## How was this patch tested?
New test in SparkContextSuite to check there is a cause exception.

Author: Bogdan Raducanu <bogdan@databricks.com>

Closes #17292 from bogdanrdc/SPARK-19946.