[SPARK-3426] Fix sort-based shuffle error when spark.shuffle.compress and spark.shuffle.spill.compress settings are different

This PR fixes SPARK-3426, an issue where sort-based shuffle crashes if the
`spark.shuffle.spill.compress` and `spark.shuffle.compress` settings have
different values.

The problem is that sort-based shuffle's read and write paths use different
settings for determining whether to apply compression.  ExternalSorter writes
runs to files using `TempBlockId` ids, which causes
`spark.shuffle.spill.compress` to be used for enabling compression, but these
spilled files end up being shuffled over the network and read as shuffle files
using `ShuffleBlockId` by BlockStoreShuffleFetcher, which causes
`spark.shuffle.compress` to be used for enabling decompression.  As a result,
this leads to errors when these settings disagree.

Based on the discussions in #2247 and #2178, it sounds like we don't want to
remove the `spark.shuffle.spill.compress` setting.  Therefore, I've tried to
come up with a fix where `spark.shuffle.spill.compress` is used to compress
data that's read and written locally and `spark.shuffle.compress` is used to
compress any data that will be fetched / read as shuffle blocks.

To do this, I split `TempBlockId` into two new id types, `TempLocalBlockId` and
`TempShuffleBlockId`, which map to `spark.shuffle.spill.compress` and
`spark.shuffle.compress`, respectively.  ExternalAppendOnlyMap also used temp
blocks for spilling data.  It looks like ExternalSorter was designed to be
a generic sorter but its configuration already happens to be tied to sort-based
shuffle, so I think it's fine if we use `spark.shuffle.compress` to compress
its spills; we can move the compression configuration to the constructor in
a later commit if we find that ExternalSorter is being used in other contexts
where we want different configuration options to control compression.  To
summarize:

**Before:**

|       | ExternalAppendOnlyMap        | ExternalSorter               |
|-------|------------------------------|------------------------------|
| Read  | spark.shuffle.spill.compress | spark.shuffle.compress       |
| Write | spark.shuffle.spill.compress | spark.shuffle.spill.compress |

**After:**

|       | ExternalAppendOnlyMap        | ExternalSorter         |
|-------|------------------------------|------------------------|
| Read  | spark.shuffle.spill.compress | spark.shuffle.compress |
| Write | spark.shuffle.spill.compress | spark.shuffle.compress |

Thanks to andrewor14 for debugging this with me!

Author: Josh Rosen <joshrosen@databricks.com>

Closes #2890 from JoshRosen/SPARK-3426 and squashes the following commits:

1921cf6 [Josh Rosen] Minor edit for clarity.
c8dd8f2 [Josh Rosen] Add comment explaining use of createTempShuffleBlock().
2c687b9 [Josh Rosen] Fix SPARK-3426.
91e7e40 [Josh Rosen] Combine tests into single test of all combinations
76ca65e [Josh Rosen] Add regression test for SPARK-3426.
[SPARK-3877][YARN] Throw an exception when application is not successful so that the exit code wil be set to 1

When an yarn application fails (yarn-cluster mode), the exit code of spark-submit is still 0. It's hard for people to write some automatic scripts to run spark jobs in yarn because the failure can not be detected in these scripts.

This PR added a status checking after `monitorApplication`. If an application is not successful, `run()` will throw an `SparkException`, so that Client.scala will exit with code 1. Therefore, people can use the exit code of `spark-submit` to write some automatic scripts.

Author: zsxwing <zsxwing@gmail.com>

Closes #2732 from zsxwing/SPARK-3877 and squashes the following commits:

1f89fa5 [zsxwing] Fix the unit test
a0498e1 [zsxwing] Update the docs and the error message
e1cb9ef [zsxwing] Fix the hacky way of calling Client
ff16fec [zsxwing] Remove System.exit in Client.scala and add a test
6a2c103 [zsxwing] [SPARK-3877] Throw an exception when application is not successful so that the exit code wil be set to 1
[SPARK-3812] [BUILD] Adapt maven build to publish effective pom.

I have tried maven help plugin first but that published all projects in top level pom. So I was left with no choice but to roll my own trivial plugin. This patch basically installs an effective pom after maven install is finished.

The problem it fixes is described as follows:
If you install using maven
` mvn install -DskipTests -Dhadoop.version=2.2.0 -Phadoop-2.2 `
Then without this patch the published pom(s) will have hadoop version as 1.0.4. This can be a problem at some point.

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

Closes #2673 from ScrapCodes/build-changes-effective-pom and squashes the following commits:

aa7b91d [Prashant Sharma] used an unused dep.
0300dac [Prashant Sharma] improved comment messages..
28f891e [Prashant Sharma] Added a useless dependency, so that we can shade it. And realized fake shading works for us.
553d96b [Prashant Sharma] Shaded some unused class of an unused dep, to generate effective pom(s)
[BUILD] Fixed resolver for scalastyle plugin and upgrade sbt version.

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

Closes #2877 from ScrapCodes/scalastyle-fix and squashes the following commits:

a17b9fe [Prashant Sharma] [BUILD] Fixed resolver for scalastyle plugin.
[SPARK-4055][MLlib] Inconsistent spelling 'MLlib' and 'MLLib'

Thare are some inconsistent spellings 'MLlib' and 'MLLib' in some documents and source codes.

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

Closes #2903 from sarutak/SPARK-4055 and squashes the following commits:

b031640 [Kousuke Saruta] Fixed inconsistent spelling "MLlib and MLLib"
[SPARK-4006] In long running contexts, we encountered the situation of double registe...

...r without a remove in between. The cause for that is unknown, and assumed a temp network issue.

However, since the second register is with a BlockManagerId on a different port, blockManagerInfo.contains() returns false, while blockManagerIdByExecutor returns Some. This inconsistency is caught in a conditional statement that does System.exit(1), which is a huge robustness issue for us.

The fix - simply remove the old id from both maps during register when this happens. We are mimicking the behavior of expireDeadHosts(), by doing local cleanup of the maps before trying to add new ones.

Also - added some logging for register and unregister.

This is just like https://github.com/apache/spark/pull/2854 except it's on master

Author: Tal Sliwowicz <tal.s@taboola.com>

Closes #2886 from tsliwowicz/master-block-mgr-removal and squashes the following commits:

094d508 [Tal Sliwowicz] some more white space change undone
41a2217 [Tal Sliwowicz] some more whitspaces change undone
7bcfc3d [Tal Sliwowicz] whitspaces fix
df9d98f [Tal Sliwowicz] Code review comments fixed
f48bce9 [Tal Sliwowicz] In long running contexts, we encountered the situation of double register without a remove in between. The cause for that is unknown, and assumed a temp network issue.
specify unidocGenjavadocVersion of 0.8

Fixes an issue with being too strict generating javadoc causing errors.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #2893 from holdenk/SPARK-3359-sbtunidoc-java8 and squashes the following commits:

9379a70 [Holden Karau] specify unidocGenjavadocVersion of 0.8
Revert "[SPARK-3812] [BUILD] Adapt maven build to publish effective pom."

This reverts commit c5882c663e054adcd3ecd9f11e91a1929dbc14a3.

I am reverting this becuase it appears to cause the maven tests
to hang.
[SPARK-4019] [SPARK-3740] Fix MapStatus compression bug that could lead to empty results or Snappy errors

This commit fixes a bug in MapStatus that could cause jobs to wrongly return
empty results if those jobs contained stages with more than 2000 partitions
where most of those partitions were empty.

For jobs with > 2000 partitions, MapStatus uses HighlyCompressedMapStatus,
which only stores the average size of blocks.  If the average block size is
zero, then this will cause all blocks to be reported as empty, causing
BlockFetcherIterator to mistakenly skip them.

For example, this would return an empty result:

    sc.makeRDD(0 until 10, 1000).repartition(2001).collect()

This can also lead to deserialization errors (e.g. Snappy decoding errors)
for jobs with > 2000 partitions where the average block size is non-zero but
there is at least one empty block.  In this case, the BlockFetcher attempts to
fetch empty blocks and fails when trying to deserialize them.

The root problem here is that MapStatus has a (previously undocumented)
correctness property that was violated by HighlyCompressedMapStatus:

    If a block is non-empty, then getSizeForBlock must be non-zero.

I fixed this by modifying HighlyCompressedMapStatus to store the average size
of _non-empty_ blocks and to use a compressed bitmap to track which blocks are
empty.

I also removed a test which was broken as originally written: it attempted
to check that HighlyCompressedMapStatus's size estimation error was < 10%,
but this was broken because HighlyCompressedMapStatus is only used for map
statuses with > 2000 partitions, but the test only created 50.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #2866 from JoshRosen/spark-4019 and squashes the following commits:

fc8b490 [Josh Rosen] Roll back hashset change, which didn't improve performance.
5faa0a4 [Josh Rosen] Incorporate review feedback
c8b8cae [Josh Rosen] Two performance fixes:
3b892dd [Josh Rosen] Address Reynold's review comments
ba2e71c [Josh Rosen] Add missing newline
609407d [Josh Rosen] Use Roaring Bitmap to track non-empty blocks.
c23897a [Josh Rosen] Use sets when comparing collect() results
91276a3 [Josh Rosen] [SPARK-4019] Fix MapStatus compression bug that could lead to empty results.
[SPARK-3993] [PySpark] fix bug while reuse worker after take()

After take(), maybe there are some garbage left in the socket, then next task assigned to this worker will hang because of corrupted data.

We should make sure the socket is clean before reuse it, write END_OF_STREAM at the end, and check it after read out all result from python.

Author: Davies Liu <davies.liu@gmail.com>
Author: Davies Liu <davies@databricks.com>

Closes #2838 from davies/fix_reuse and squashes the following commits:

8872914 [Davies Liu] fix tests
660875b [Davies Liu] fix bug while reuse worker after take()
[SPARK-4000][BUILD] Sends archived unit tests logs to Jenkins master

This PR sends archived unit tests logs to the build history directory in Jenkins master, so that we can serve it via HTTP later to help debugging Jenkins build failures.

pwendell JoshRosen Please help review, thanks!

Author: Cheng Lian <lian@databricks.com>

Closes #2845 from liancheng/log-archive and squashes the following commits:

ac8d9d4 [Cheng Lian] Includes build number in messages posted to GitHub
68c7010 [Cheng Lian] Logs backup should be implemented in dev/run-tests-jenkins
4b912f7 [Cheng Lian] Sends archived unit tests logs to Jenkins master
SPARK-3812 Build changes to publish effective pom.

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

Closes #2921 from ScrapCodes/build-changes-effective-pom and squashes the following commits:

8841491 [Prashant Sharma] Fixed broken maven build.
aa7b91d [Prashant Sharma] used an unused dep.
0300dac [Prashant Sharma] improved comment messages..
28f891e [Prashant Sharma] Added a useless dependency, so that we can shade it. And realized fake shading works for us.
553d96b [Prashant Sharma] Shaded some unused class of an unused dep, to generate effective pom(s)
[SPARK-2652] [PySpark] donot use KyroSerializer as default serializer

KyroSerializer can not serialize customized class without registered explicitly, use it as default serializer in PySpark will introduce some regression in MLlib.

cc mengxr

Author: Davies Liu <davies@databricks.com>

Closes #2916 from davies/revert and squashes the following commits:

43eb6d3 [Davies Liu] donot use KyroSerializer as default serializer
[SPARK-3900][YARN] ApplicationMaster's shutdown hook fails and IllegalStateException is thrown.

ApplicationMaster registers a shutdown hook and it calls ApplicationMaster#cleanupStagingDir.

cleanupStagingDir invokes FileSystem.get(yarnConf) and it invokes FileSystem.getInternal. FileSystem.getInternal also registers shutdown hook.
In FileSystem of hadoop 0.23, the shutdown hook registration does not consider whether shutdown is in progress or not (In 2.2, it's considered).

    // 0.23
    if (map.isEmpty() ) {
      ShutdownHookManager.get().addShutdownHook(clientFinalizer, SHUTDOWN_HOOK_PRIORITY);
    }

    // 2.2
    if (map.isEmpty()
                && !ShutdownHookManager.get().isShutdownInProgress()) {
       ShutdownHookManager.get().addShutdownHook(clientFinalizer, SHUTDOWN_HOOK_PRIORITY);
    }

Thus, in 0.23, another shutdown hook can be registered when ApplicationMaster's shutdown hook run.

This issue cause IllegalStateException as follows.

    java.lang.IllegalStateException: Shutdown in progress, cannot add a shutdownHook
            at org.apache.hadoop.util.ShutdownHookManager.addShutdownHook(ShutdownHookManager.java:152)
            at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2306)
            at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2278)
            at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:316)
            at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:162)
            at org.apache.spark.deploy.yarn.ApplicationMaster.org$apache$spark$deploy$yarn$ApplicationMaster$$cleanupStagingDir(ApplicationMaster.scala:307)
            at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$3.run(ApplicationMaster.scala:118)
            at org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:54)

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

Closes #2924 from sarutak/SPARK-3900-2 and squashes the following commits:

9112817 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3900-2
97018fa [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3900
2c2850e [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3900
ee52db2 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3900
a7d6c9b [Kousuke Saruta] Merge branch 'SPARK-3900' of github.com:sarutak/spark into SPARK-3900
1cdf03c [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3900
a5f6443 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3900
57b397d [Kousuke Saruta] Fixed IllegalStateException caused by shutdown hook registration in another shutdown hook
[SPARK-4051] [SQL] [PySpark] Convert Row into dictionary

Added a method to Row to turn row into dict:

```
>>> row = Row(a=1)
>>> row.asDict()
{'a': 1}
```

Author: Davies Liu <davies@databricks.com>

Closes #2896 from davies/dict and squashes the following commits:

8d97366 [Davies Liu] convert Row into dict
[SPARK-4050][SQL] Fix caching of temporary tables with projections.

Previously cached data was found by `sameResult` plan matching on optimized plans.  This technique however fails to locate the cached data when a temporary table with a projection is queried with a further reduced projection.  The failure is due to the fact that optimization will collapse the projections, producing a plan that no longer produces the sameResult as the cached data (though the cached data still subsumes the desired data).  For example consider the following previously failing test case.

```scala
sql("CACHE TABLE tempTable AS SELECT key FROM testData")
assertCached(sql("SELECT COUNT(*) FROM tempTable"))
```

In this PR I change the matching to occur after analysis instead of optimization, so that in the case of temporary tables, the plans will always match.  I think this should work generally, however, this error does raise questions about the need to do more thorough subsumption checking when locating cached data.

Another question is what sort of semantics we want to provide when uncaching data from temporary tables.  For example consider the following sequence of commands:

```scala
testData.select('key).registerTempTable("tempTable1")
testData.select('key).registerTempTable("tempTable2")
cacheTable("tempTable1")

// This obviously works.
assertCached(sql("SELECT COUNT(*) FROM tempTable1"))

// It seems good that this works ...
assertCached(sql("SELECT COUNT(*) FROM tempTable2"))

// ... but is this valid?
uncacheTable("tempTable2")

// Should this still be cached?
assertCached(sql("SELECT COUNT(*) FROM tempTable1"), 0)
```

Author: Michael Armbrust <michael@databricks.com>

Closes #2912 from marmbrus/cachingBug and squashes the following commits:

9c822d4 [Michael Armbrust] remove commented out code
5c72fb7 [Michael Armbrust] Add a test case / question about uncaching semantics.
63a23e4 [Michael Armbrust] Perform caching on analyzed instead of optimized plan.
03f1cfe [Michael Armbrust] Clean-up / add tests to SameResult suite.
[SPARK-2706][SQL] Enable Spark to support Hive 0.13

Given that a lot of users are trying to use hive 0.13 in spark, and the incompatibility between hive-0.12 and hive-0.13 on the API level I want to propose following approach, which has no or minimum impact on existing hive-0.12 support, but be able to jumpstart the development of hive-0.13 and future version support.

Approach: Introduce “hive-version” property,  and manipulate pom.xml files to support different hive version at compiling time through shim layer, e.g., hive-0.12.0 and hive-0.13.1. More specifically,

1. For each different hive version, there is a very light layer of shim code to handle API differences, sitting in sql/hive/hive-version, e.g., sql/hive/v0.12.0 or sql/hive/v0.13.1

2. Add a new profile hive-default active by default, which picks up all existing configuration and hive-0.12.0 shim (v0.12.0)  if no hive.version is specified.

3. If user specifies different version (currently only 0.13.1 by -Dhive.version = 0.13.1), hive-versions profile will be activated, which pick up hive-version specific shim layer and configuration, mainly the hive jars and hive-version shim, e.g., v0.13.1.

4. With this approach, nothing is changed with current hive-0.12 support.

No change by default: sbt/sbt -Phive
For example: sbt/sbt -Phive -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 assembly

To enable hive-0.13: sbt/sbt -Dhive.version=0.13.1
For example: sbt/sbt -Dhive.version=0.13.1 -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 assembly

Note that in hive-0.13, hive-thriftserver is not enabled, which should be fixed by other Jira, and we don’t need -Phive with -Dhive.version in building (probably we should use -Phive -Dhive.version=xxx instead after thrift server is also supported in hive-0.13.1).

Author: Zhan Zhang <zhazhan@gmail.com>
Author: zhzhan <zhazhan@gmail.com>
Author: Patrick Wendell <pwendell@gmail.com>

Closes #2241 from zhzhan/spark-2706 and squashes the following commits:

3ece905 [Zhan Zhang] minor fix
410b668 [Zhan Zhang] solve review comments
cbb4691 [Zhan Zhang] change run-test for new options
0d4d2ed [Zhan Zhang] rebase
497b0f4 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
8fad1cf [Zhan Zhang] change the pom file and make hive-0.13.1 as the default
ab028d1 [Zhan Zhang] rebase
4a2e36d [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
4cb1b93 [zhzhan] Merge pull request #1 from pwendell/pr-2241
b0478c0 [Patrick Wendell] Changes to simplify the build of SPARK-2706
2b50502 [Zhan Zhang] rebase
a72c0d4 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
cb22863 [Zhan Zhang] correct the typo
20f6cf7 [Zhan Zhang] solve compatability issue
f7912a9 [Zhan Zhang] rebase and solve review feedback
301eb4a [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
10c3565 [Zhan Zhang] address review comments
6bc9204 [Zhan Zhang] rebase and remove temparory repo
d3aa3f2 [Zhan Zhang] Merge branch 'master' into spark-2706
cedcc6f [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
3ced0d7 [Zhan Zhang] rebase
d9b981d [Zhan Zhang] rebase and fix error due to rollback
adf4924 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
3dd50e8 [Zhan Zhang] solve conflicts and remove unnecessary implicts
d10bf00 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
dc7bdb3 [Zhan Zhang] solve conflicts
7e0cc36 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
d7c3e1e [Zhan Zhang] Merge branch 'master' into spark-2706
68deb11 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
d48bd18 [Zhan Zhang] address review comments
3ee3b2b [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
57ea52e [Zhan Zhang] Merge branch 'master' into spark-2706
2b0d513 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
9412d24 [Zhan Zhang] address review comments
f4af934 [Zhan Zhang] rebase
1ccd7cc [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
128b60b [Zhan Zhang] ignore 0.12.0 test cases for the time being
af9feb9 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
5f5619f [Zhan Zhang] restructure the directory and different hive version support
05d3683 [Zhan Zhang] solve conflicts
e4c1982 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
94b4fdc [Zhan Zhang] Spark-2706: hive-0.13.1 support on spark
87ebf3b [Zhan Zhang] Merge branch 'master' into spark-2706
921e914 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
f896b2a [Zhan Zhang] Merge branch 'master' into spark-2706
789ea21 [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
cb53a2c [Zhan Zhang] Merge branch 'master' of https://github.com/apache/spark
f6a8a40 [Zhan Zhang] revert
ba14f28 [Zhan Zhang] test
dbedff3 [Zhan Zhang] Merge remote-tracking branch 'upstream/master'
70964fe [Zhan Zhang] revert
fe0f379 [Zhan Zhang] Merge branch 'master' of https://github.com/zhzhan/spark
70ffd93 [Zhan Zhang] revert
42585ec [Zhan Zhang] test
7d5fce2 [Zhan Zhang] test
[SPARK-4026][Streaming] Write ahead log management

As part of the effort to avoid data loss on Spark Streaming driver failure, we want to implement a write ahead log that can write received data to HDFS. This allows the received data to be persist across driver failures. So when the streaming driver is restarted, it can find and reprocess all the data that were received but not processed.

This was primarily implemented by @harishreedharan. This is still WIP, as he is going to improve the unitests by using HDFS mini cluster.

Author: Hari Shreedharan <hshreedharan@apache.org>
Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #2882 from tdas/driver-ha-wal and squashes the following commits:

e4bee20 [Tathagata Das] Removed synchronized, Path.getFileSystem is threadsafe
55514e2 [Tathagata Das] Minor changes based on PR comments.
d29fddd [Tathagata Das] Merge pull request #20 from harishreedharan/driver-ha-wal
a317a4d [Hari Shreedharan] Directory deletion should not fail tests
9514dc8 [Tathagata Das] Added unit tests to test reading of corrupted data and other minor edits
3881706 [Tathagata Das] Merge pull request #19 from harishreedharan/driver-ha-wal
4705fff [Hari Shreedharan] Sort listed files by name. Use local files for WAL tests.
eb356ca [Tathagata Das] Merge pull request #18 from harishreedharan/driver-ha-wal
82ce56e [Hari Shreedharan] Fix file ordering issue in WALManager tests
5ff90ee [Hari Shreedharan] Fix tests to not ignore ordering and also assert all data is present
ef8db09 [Tathagata Das] Merge pull request #17 from harishreedharan/driver-ha-wal
7e40e56 [Hari Shreedharan] Restore old build directory after tests
587b876 [Hari Shreedharan] Fix broken test. Call getFileSystem only from synchronized method.
b4be0c1 [Hari Shreedharan] Remove unused method
edcbee1 [Hari Shreedharan] Tests reading and writing data using writers now use Minicluster.
5c70d1f [Hari Shreedharan] Remove underlying stream from the WALWriter.
4ab602a [Tathagata Das] Refactored write ahead stuff from streaming.storage to streaming.util
b06be2b [Tathagata Das] Adding missing license.
5182ffb [Hari Shreedharan] Added documentation
172358d [Tathagata Das] Pulled WriteAheadLog-related stuff from tdas/spark/tree/driver-ha-working
Merge branch 'master' of https://github.com/apache/spark
[SPARK-2713] Executors of same application in same host should only download files & jars once

If Spark lunched multiple executors in one host for one application, every executor would download it dependent files and jars (if not using local: url) independently. It maybe result in huge latency. In my case, it result in 20 seconds latency to download dependent jars(size about 17M) when I lunched 32 executors in every host(total 4 hosts).

This patch will cache downloaded files and jars for executors to reduce network throughput and download latency. In my case, the latency was reduced from 20 seconds to less than 1 second.

Author: Li Zhihui <zhihui.li@intel.com>
Author: li-zhihui <zhihui.li@intel.com>

Closes #1616 from li-zhihui/cachefiles and squashes the following commits:

36940df [Li Zhihui] Close cache for local mode
935fed6 [Li Zhihui] Clean code.
f9330d4 [Li Zhihui] Clean code again
7050d46 [Li Zhihui] Clean code
074a422 [Li Zhihui] Fix: deal with spark.files.overwrite
03ed3a8 [li-zhihui] rename cache file name as XXXXXXXXX_cache
2766055 [li-zhihui] Use url.hashCode + timestamp as cachedFileName
76a7b66 [Li Zhihui] Clean code & use applcation work directory as cache directory
3510eb0 [Li Zhihui] Keep fetchFile private
2ffd742 [Li Zhihui] add comment for FileLock
e0ebd48 [Li Zhihui] Try and finally lock.release
7fb7c0b [Li Zhihui] Release lock before copy files
6b997bf [Li Zhihui] Executors of same application in same host should only download files & jars once