[SPARK-4110] Wrong comments about default settings in spark-daemon.sh

In spark-daemon.sh, thare are following comments.

    #   SPARK_CONF_DIR  Alternate conf dir. Default is ${SPARK_PREFIX}/conf.
    #   SPARK_LOG_DIR   Where log files are stored.  PWD by default.

But, I think the default value for SPARK_CONF_DIR is `${SPARK_HOME}/conf` and for SPARK_LOG_DIR is `${SPARK_HOME}/logs`.

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

Closes #2972 from sarutak/SPARK-4110 and squashes the following commits:

5a171a2 [Kousuke Saruta] Fixed wrong comments
[SPARK-4096][YARN]let ApplicationMaster accept executor memory argument in same format as JVM memory strings

Here `ApplicationMaster` accept executor memory argument only in number format, we should let it accept JVM style memory strings as well.

Author: WangTaoTheTonic <barneystinson@aliyun.com>

Closes #2955 from WangTaoTheTonic/modifyDesc and squashes the following commits:

ab98c70 [WangTaoTheTonic] append parameter passed in
3779767 [WangTaoTheTonic] Update executor memory description in the help message
[SPARK-3657] yarn alpha YarnRMClientImpl throws NPE appMasterRequest.setTrackingUrl starting spark-shell

tgravescs reported this issue.

Following is quoted from tgravescs' report.

YarnRMClientImpl.registerApplicationMaster can throw null pointer exception when setting the trackingurl if its empty:

    appMasterRequest.setTrackingUrl(new URI(uiAddress).getAuthority())

I hit this just start spark-shell without the tracking url set.

14/09/23 16:18:34 INFO yarn.YarnRMClientImpl: Connecting to ResourceManager at kryptonitered-jt1.red.ygrid.yahoo.com/98.139.154.99:8030
Exception in thread "main" java.lang.NullPointerException
        at org.apache.hadoop.yarn.proto.YarnServiceProtos$RegisterApplicationMasterRequestProto$Builder.setTrackingUrl(YarnServiceProtos.java:710)
        at org.apache.hadoop.yarn.api.protocolrecords.impl.pb.RegisterApplicationMasterRequestPBImpl.setTrackingUrl(RegisterApplicationMasterRequestPBImpl.java:132)
        at org.apache.spark.deploy.yarn.YarnRMClientImpl.registerApplicationMaster(YarnRMClientImpl.scala:102)
        at org.apache.spark.deploy.yarn.YarnRMClientImpl.register(YarnRMClientImpl.scala:55)
        at org.apache.spark.deploy.yarn.YarnRMClientImpl.register(YarnRMClientImpl.scala:38)
        at org.apache.spark.deploy.yarn.ApplicationMaster.registerAM(ApplicationMaster.scala:168)
        at org.apache.spark.deploy.yarn.ApplicationMaster.runExecutorLauncher(ApplicationMaster.scala:206)
        at org.apache.spark.deploy.yarn.ApplicationMaster.run(ApplicationMaster.scala:120)

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

Closes #2981 from sarutak/SPARK-3657-2 and squashes the following commits:

e2fd6bc [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3657
70b8882 [Kousuke Saruta] Fixed NPE thrown
[SPARK-4089][Doc][Minor] The version number of Spark in _config.yaml is wrong.

The version number of Spark in docs/_config.yaml for master branch should be 1.2.0 for now.

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

Closes #2943 from sarutak/SPARK-4089 and squashes the following commits:

aba7fb4 [Kousuke Saruta] Fixed the version number of Spark in _config.yaml
[SPARK-4065] Add check for IPython on Windows

This issue employs logic similar to the bash launcher (pyspark) to check
if IPTYHON=1, and if so launch ipython with options in IPYTHON_OPTS.
This fix assumes that ipython is available in the system Path, and can
be invoked with a plain "ipython" command.

Author: Michael Griffiths <msjgriffiths@gmail.com>

Closes #2910 from msjgriffiths/pyspark-windows and squashes the following commits:

ef34678 [Michael Griffiths] Change build message to comply with [SPARK-3775]
361e3d8 [Michael Griffiths] [SPARK-4065] Add check for IPython on Windows
9ce72d1 [Michael Griffiths] [SPARK-4065] Add check for IPython on Windows
[SPARK-4058] [PySpark] Log file name is hard coded even though there is a variable '$LOG_FILE '

In a script 'python/run-tests', log file name is represented by a variable 'LOG_FILE' and it is used in run-tests. But, there are some hard-coded log file name in the script.

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

Closes #2905 from sarutak/SPARK-4058 and squashes the following commits:

7710490 [Kousuke Saruta] Fixed python/run-tests not to use hard-coded log file name
[SPARK-3814][SQL] Support for Bitwise AND(&), OR(|) ,XOR(^), NOT(~) in Spark HQL and SQL

Currently there is no support of Bitwise & , | in Spark HiveQl and Spark SQL as well. So this PR support the same.
I am closing https://github.com/apache/spark/pull/2926 as it has conflicts to merge. And also added support for Bitwise AND(&), OR(|) ,XOR(^), NOT(~) And I handled all review comments in that PR

Author: ravipesala <ravindra.pesala@huawei.com>

Closes #2961 from ravipesala/SPARK-3814-NEW4 and squashes the following commits:

a391c7a [ravipesala] Rebase with master
[SPARK-3988][SQL] add public API for date type

Add json and python api for date type.
By using Pickle, `java.sql.Date` was serialized as calendar, and recognized in python as `datetime.datetime`.

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #2901 from adrian-wang/spark3988 and squashes the following commits:

c51a24d [Daoyuan Wang] convert datetime to date
5670626 [Daoyuan Wang] minor line combine
f760d8e [Daoyuan Wang] fix indent
444f100 [Daoyuan Wang] fix a typo
1d74448 [Daoyuan Wang] fix scala style
8d7dd22 [Daoyuan Wang] add json and python api for date type
[Spark 3922] Refactor spark-core to use Utils.UTF_8

A global UTF8 constant is very helpful to handle encoding problems when converting between String and bytes. There are several solutions here:

1. Add `val UTF_8 = Charset.forName("UTF-8")` to Utils.scala
2. java.nio.charset.StandardCharsets.UTF_8 (require JDK7)
3. io.netty.util.CharsetUtil.UTF_8
4. com.google.common.base.Charsets.UTF_8
5. org.apache.commons.lang.CharEncoding.UTF_8
6. org.apache.commons.lang3.CharEncoding.UTF_8

IMO, I prefer option 1) because people can find it easily.

This is a PR for option 1) and only fixes Spark Core.

Author: zsxwing <zsxwing@gmail.com>

Closes #2781 from zsxwing/SPARK-3922 and squashes the following commits:

f974edd [zsxwing] Merge branch 'master' into SPARK-3922
2d27423 [zsxwing] Refactor spark-core to use Refactor spark-core to use Utils.UTF_8
[SPARK-3343] [SQL] Add serde support for CTAS

Currently, `CTAS` (Create Table As Select) doesn't support specifying the `SerDe` in HQL. This PR will pass down the `ASTNode` into the physical operator `execution.CreateTableAsSelect`, which will extract the `CreateTableDesc` object via Hive `SemanticAnalyzer`. In the meantime, I also update the `HiveMetastoreCatalog.createTable` to optionally support the `CreateTableDesc` for table creation.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #2570 from chenghao-intel/ctas_serde and squashes the following commits:

e011ef5 [Cheng Hao] shim for both 0.12 & 0.13.1
cfb3662 [Cheng Hao] revert to hive 0.12
c8a547d [Cheng Hao] Support SerDe properties within CTAS
[SPARK-4084] Reuse sort key in Sorter

Sorter uses generic-typed key for sorting. When data is large, it creates lots of key objects, which is not efficient. We should reuse the key in Sorter for memory efficiency. This change is part of the petabyte sort implementation from rxin .

The `Sorter` class was written in Java and marked package private. So it is only available to `org.apache.spark.util.collection`. I renamed it to `TimSort` and add a simple wrapper of it, still called `Sorter`, in Scala, which is `private[spark]`.

The benchmark code is updated, which now resets the array before each run. Here is the result on sorting primitive Int arrays of size 25 million using Sorter:

~~~
[info] - Sorter benchmark for key-value pairs !!! IGNORED !!!
Java Arrays.sort() on non-primitive int array: Took 13237 ms
Java Arrays.sort() on non-primitive int array: Took 13320 ms
Java Arrays.sort() on non-primitive int array: Took 15718 ms
Java Arrays.sort() on non-primitive int array: Took 13283 ms
Java Arrays.sort() on non-primitive int array: Took 13267 ms
Java Arrays.sort() on non-primitive int array: Took 15122 ms
Java Arrays.sort() on non-primitive int array: Took 15495 ms
Java Arrays.sort() on non-primitive int array: Took 14877 ms
Java Arrays.sort() on non-primitive int array: Took 16429 ms
Java Arrays.sort() on non-primitive int array: Took 14250 ms
Java Arrays.sort() on non-primitive int array: (13878 ms first try, 14499 ms average)
Java Arrays.sort() on primitive int array: Took 2683 ms
Java Arrays.sort() on primitive int array: Took 2683 ms
Java Arrays.sort() on primitive int array: Took 2701 ms
Java Arrays.sort() on primitive int array: Took 2746 ms
Java Arrays.sort() on primitive int array: Took 2685 ms
Java Arrays.sort() on primitive int array: Took 2735 ms
Java Arrays.sort() on primitive int array: Took 2669 ms
Java Arrays.sort() on primitive int array: Took 2693 ms
Java Arrays.sort() on primitive int array: Took 2680 ms
Java Arrays.sort() on primitive int array: Took 2642 ms
Java Arrays.sort() on primitive int array: (2948 ms first try, 2691 ms average)
Sorter without key reuse on primitive int array: Took 10732 ms
Sorter without key reuse on primitive int array: Took 12482 ms
Sorter without key reuse on primitive int array: Took 10718 ms
Sorter without key reuse on primitive int array: Took 12650 ms
Sorter without key reuse on primitive int array: Took 10747 ms
Sorter without key reuse on primitive int array: Took 10783 ms
Sorter without key reuse on primitive int array: Took 12721 ms
Sorter without key reuse on primitive int array: Took 10604 ms
Sorter without key reuse on primitive int array: Took 10622 ms
Sorter without key reuse on primitive int array: Took 11843 ms
Sorter without key reuse on primitive int array: (11089 ms first try, 11390 ms average)
Sorter with key reuse on primitive int array: Took 5141 ms
Sorter with key reuse on primitive int array: Took 5298 ms
Sorter with key reuse on primitive int array: Took 5066 ms
Sorter with key reuse on primitive int array: Took 5164 ms
Sorter with key reuse on primitive int array: Took 5203 ms
Sorter with key reuse on primitive int array: Took 5274 ms
Sorter with key reuse on primitive int array: Took 5186 ms
Sorter with key reuse on primitive int array: Took 5159 ms
Sorter with key reuse on primitive int array: Took 5164 ms
Sorter with key reuse on primitive int array: Took 5078 ms
Sorter with key reuse on primitive int array: (5311 ms first try, 5173 ms average)
~~~

So with key reuse, it is faster and less likely to trigger GC.

Author: Xiangrui Meng <meng@databricks.com>
Author: Reynold Xin <rxin@apache.org>

Closes #2937 from mengxr/SPARK-4084 and squashes the following commits:

d73c3d0 [Xiangrui Meng] address comments
0b7b682 [Xiangrui Meng] fix mima
a72f53c [Xiangrui Meng] update timeIt
38ba50c [Xiangrui Meng] update timeIt
720f731 [Xiangrui Meng] add doc about JIT specialization
78f2879 [Xiangrui Meng] update tests
7de2efd [Xiangrui Meng] update the Sorter benchmark code to be correct
8626356 [Xiangrui Meng] add prepare to timeIt and update testsin SorterSuite
5f0d530 [Xiangrui Meng] update method modifiers of SortDataFormat
6ffbe66 [Xiangrui Meng] rename Sorter to TimSort and add a Scala wrapper that is private[spark]
b00db4d [Xiangrui Meng] doc and tests
cf94e8a [Xiangrui Meng] renaming
464ddce [Reynold Xin] cherry-pick rxin's commit
[SPARK-4008] Fix "kryo with fold" in KryoSerializerSuite

`zeroValue` will be serialized by `spark.closure.serializer` but `spark.closure.serializer` only supports the default Java serializer. So it must not be `ClassWithoutNoArgConstructor`, which can not be serialized by the Java serializer.

This PR changed `zeroValue` to null and updated the test to make it work correctly.

Author: zsxwing <zsxwing@gmail.com>

Closes #2856 from zsxwing/SPARK-4008 and squashes the following commits:

51da655 [zsxwing] [SPARK-4008] Fix "kryo with fold" in KryoSerializerSuite
[SPARK-3904] [SQL] add constant objectinspector support for udfs

In HQL, we convert all of the data type into normal `ObjectInspector`s for UDFs, most of cases it works, however, some of the UDF actually requires its children `ObjectInspector` to be the `ConstantObjectInspector`, which will cause exception.
e.g.
select named_struct("x", "str") from src limit 1;

I updated the method `wrap` by adding the one more parameter `ObjectInspector`(to describe what it expects to wrap to, for example: java.lang.Integer or IntWritable).

As well as the `unwrap` method by providing the input `ObjectInspector`.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #2762 from chenghao-intel/udf_coi and squashes the following commits:

bcacfd7 [Cheng Hao] Shim for both Hive 0.12 & 0.13.1
2416e5d [Cheng Hao] revert to hive 0.12
5793c01 [Cheng Hao] add space before while
4e56e1b [Cheng Hao] style issue
683d3fd [Cheng Hao] Add golden files
fe591e4 [Cheng Hao] update HiveGenericUdf for set the ObjectInspector while constructing the DeferredObject
f6740fe [Cheng Hao] Support Constant ObjectInspector for Map & List
8814c3a [Cheng Hao] Passing ContantObjectInspector(when necessary) for UDF initializing
[SPARK-4133] [SQL] [PySpark] type conversionfor python udf

Call Python UDF on ArrayType/MapType/PrimitiveType, the returnType can also be ArrayType/MapType/PrimitiveType.

For StructType, it will act as tuple (without attributes). If returnType is StructType, it also should be tuple.

Author: Davies Liu <davies@databricks.com>

Closes #2973 from davies/udf_array and squashes the following commits:

306956e [Davies Liu] Merge branch 'master' of github.com:apache/spark into udf_array
2c00e43 [Davies Liu] fix merge
11395fa [Davies Liu] Merge branch 'master' of github.com:apache/spark into udf_array
9df50a2 [Davies Liu] address comments
79afb4e [Davies Liu] type conversionfor python udf
[FIX] disable benchmark code

I forgot to disable the benchmark code in #2937, which increased the Jenkins build time by couple minutes.

aarondav

Author: Xiangrui Meng <meng@databricks.com>

Closes #2990 from mengxr/disable-benchmark and squashes the following commits:

c58f070 [Xiangrui Meng] disable benchmark code
[SPARK-4129][MLlib] Performance tuning in MultivariateOnlineSummarizer

In MultivariateOnlineSummarizer, breeze's activeIterator is used
to loop through the nonZero elements in the vector. However,
activeIterator doesn't perform well due to lots of overhead.
In this PR, native while loop is used for both DenseVector and SparseVector.

The benchmark result with 20 executors using mnist8m dataset:
Before:
DenseVector: 48.2 seconds
SparseVector: 16.3 seconds

After:
DenseVector: 17.8 seconds
SparseVector: 11.2 seconds

Since MultivariateOnlineSummarizer is used in several places,
the overall performance gain in mllib library will be significant with this PR.

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #2992 from dbtsai/SPARK-4129 and squashes the following commits:

b99db6c [DB Tsai] fixed java.lang.ArrayIndexOutOfBoundsException
2b5e882 [DB Tsai] small refactoring
ebe3e74 [DB Tsai] First commit
[SPARK-3453] Netty-based BlockTransferService, extracted from Spark core

This PR encapsulates #2330, which is itself a continuation of #2240. The first goal of this PR is to provide an alternate, simpler implementation of the ConnectionManager which is based on Netty.

In addition to this goal, however, we want to resolve [SPARK-3796](https://issues.apache.org/jira/browse/SPARK-3796), which calls for a standalone shuffle service which can be integrated into the YARN NodeManager, Standalone Worker, or on its own. This PR makes the first step in this direction by ensuring that the actual Netty service is as small as possible and extracted from Spark core. Given this, we should be able to construct this standalone jar which can be included in other JVMs without incurring significant dependency or runtime issues. The actual work to ensure that such a standalone shuffle service would work in Spark will be left for a future PR, however.

In order to minimize dependencies and allow for the service to be long-running (possibly much longer-running than Spark, and possibly having to support multiple version of Spark simultaneously), the entire service has been ported to Java, where we have full control over the binary compatibility of the components and do not depend on the Scala runtime or version.

These issues: have been addressed by folding in #2330:

SPARK-3453: Refactor Netty module to use BlockTransferService interface
SPARK-3018: Release all buffers upon task completion/failure
SPARK-3002: Create a connection pool and reuse clients across different threads
SPARK-3017: Integration tests and unit tests for connection failures
SPARK-3049: Make sure client doesn't block when server/connection has error(s)
SPARK-3502: SO_RCVBUF and SO_SNDBUF should be bootstrap childOption, not option
SPARK-3503: Disable thread local cache in PooledByteBufAllocator

TODO before mergeable:
- [x] Implement uploadBlock()
- [x] Unit tests for RPC side of code
- [x] Performance testing (see comments [here](https://github.com/apache/spark/pull/2753#issuecomment-59475022))
- [x] Turn OFF by default (currently on for unit testing)

Author: Reynold Xin <rxin@apache.org>
Author: Aaron Davidson <aaron@databricks.com>
Author: cocoatomo <cocoatomo77@gmail.com>
Author: Patrick Wendell <pwendell@gmail.com>
Author: Prashant Sharma <prashant.s@imaginea.com>
Author: Davies Liu <davies.liu@gmail.com>
Author: Anand Avati <avati@redhat.com>

Closes #2753 from aarondav/netty and squashes the following commits:

cadfd28 [Aaron Davidson] Turn netty off by default
d7be11b [Aaron Davidson] Turn netty on by default
4a204b8 [Aaron Davidson] Fail block fetches if client connection fails
2b0d1c0 [Aaron Davidson] 100ch
0c5bca2 [Aaron Davidson] Merge branch 'master' of https://github.com/apache/spark into netty
14e37f7 [Aaron Davidson] Address Reynold's comments
8dfcceb [Aaron Davidson] Merge branch 'master' of https://github.com/apache/spark into netty
322dfc1 [Aaron Davidson] Address Reynold's comments, including major rename
e5675a4 [Aaron Davidson] Fail outstanding RPCs as well
ccd4959 [Aaron Davidson] Don't throw exception if client immediately fails
9da0bc1 [Aaron Davidson] Add RPC unit tests
d236dfd [Aaron Davidson] Remove no-op serializer :)
7b7a26c [Aaron Davidson] Fix Nio compile issue
dd420fd [Aaron Davidson] Merge branch 'master' of https://github.com/apache/spark into netty-test
939f276 [Aaron Davidson] Attempt to make comm. bidirectional
aa58f67 [cocoatomo] [SPARK-3909][PySpark][Doc] A corrupted format in Sphinx documents and building warnings
8dc1ded [cocoatomo] [SPARK-3867][PySpark] ./python/run-tests failed when it run with Python 2.6 and unittest2 is not installed
5b5dbe6 [Prashant Sharma] [SPARK-2924] Required by scala 2.11, only one fun/ctor amongst overriden alternatives, can have default argument(s).
2c5d9dc [Patrick Wendell] HOTFIX: Fix build issue with Akka 2.3.4 upgrade.
020691e [Davies Liu] [SPARK-3886] [PySpark] use AutoBatchedSerializer by default
ae4083a [Anand Avati] [SPARK-2805] Upgrade Akka to 2.3.4
29c6dcf [Aaron Davidson] [SPARK-3453] Netty-based BlockTransferService, extracted from Spark core
f7e7568 [Reynold Xin] Fixed spark.shuffle.io.receiveBuffer setting.
5d98ce3 [Reynold Xin] Flip buffer.
f6c220d [Reynold Xin] Merge with latest master.
407e59a [Reynold Xin] Fix style violation.
a0518c7 [Reynold Xin] Implemented block uploads.
4b18db2 [Reynold Xin] Copy the buffer in fetchBlockSync.
bec4ea2 [Reynold Xin] Removed OIO and added num threads settings.
1bdd7ee [Reynold Xin] Fixed tests.
d68f328 [Reynold Xin] Logging close() in case close() fails.
f63fb4c [Reynold Xin] Add more debug message.
6afc435 [Reynold Xin] Added logging.
c066309 [Reynold Xin] Implement java.io.Closeable interface.
519d64d [Reynold Xin] Mark private package visibility and MimaExcludes.
f0a16e9 [Reynold Xin] Fixed test hanging.
14323a5 [Reynold Xin] Removed BlockManager.getLocalShuffleFromDisk.
b2f3281 [Reynold Xin] Added connection pooling.
d23ed7b [Reynold Xin] Incorporated feedback from Norman: - use same pool for boss and worker - remove ioratio - disable caching of byte buf allocator - childoption sendbuf/receivebuf - fire exception through pipeline
9e0cb87 [Reynold Xin] Fixed BlockClientHandlerSuite
5cd33d7 [Reynold Xin] Fixed style violation.
cb589ec [Reynold Xin] Added more test cases covering cleanup when fault happens in ShuffleBlockFetcherIteratorSuite
1be4e8e [Reynold Xin] Shorten NioManagedBuffer and NettyManagedBuffer class names.
108c9ed [Reynold Xin] Forgot to add TestSerializer to the commit list.
b5c8d1f [Reynold Xin] Fixed ShuffleBlockFetcherIteratorSuite.
064747b [Reynold Xin] Reference count buffers and clean them up properly.
2b44cf1 [Reynold Xin] Added more documentation.
1760d32 [Reynold Xin] Use Epoll.isAvailable in BlockServer as well.
165eab1 [Reynold Xin] [SPARK-3453] Refactor Netty module to use BlockTransferService.
[SPARK-4003] [SQL] add 3 types for java SQL context

In JavaSqlContext, we need to let java program use big decimal, timestamp, date types.

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #2850 from adrian-wang/javacontext and squashes the following commits:

4c4292c [Daoyuan Wang] change underlying type of JavaSchemaRDD as scala
bb0508f [Daoyuan Wang] add test cases
3c58b0d [Daoyuan Wang] add 3 types for java SQL context
[SPARK-3822] Executor scaling mechanism for Yarn

This is part of a broader effort to enable dynamic scaling of executors ([SPARK-3174](https://issues.apache.org/jira/browse/SPARK-3174)). This is intended to work alongside SPARK-3795 (#2746), SPARK-3796 and SPARK-3797, but is functionally independently of these other issues.

The logic is built on top of PraveenSeluka's changes at #2798. This is different from the changes there in a few major ways: (1) the mechanism is implemented within the existing scheduler backend framework rather than in new `Actor` classes. This also introduces a parent abstract class `YarnSchedulerBackend` to encapsulate common logic to communicate with the Yarn `ApplicationMaster`. (2) The interface of requesting executors exposed to the `SparkContext` is the same, but the communication between the scheduler backend and the AM uses total number executors desired instead of an incremental number. This is discussed in #2746 and explained in the comments in the code.

I have tested this significantly on a stable Yarn cluster.

------------
A remaining task for this issue is to tone down the error messages emitted when an executor is removed.
Currently, `SparkContext` and its components react as if the executor has failed, resulting in many scary error messages and eventual timeouts. While it's not strictly necessary to fix this as of the first-cut implementation of this mechanism, it would be good to add logic to distinguish this case. I prefer to address this in a separate PR. I have filed a separate JIRA for this task at SPARK-4134.

Author: Andrew Or <andrew@databricks.com>
Author: Andrew Or <andrewor14@gmail.com>

Closes #2840 from andrewor14/yarn-scaling-mechanism and squashes the following commits:

485863e [Andrew Or] Minor log message changes
4920be8 [Andrew Or] Clarify that public API is only for Yarn mode for now
1c57804 [Andrew Or] Reword a few comments + other review comments
6321140 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-scaling-mechanism
02836c0 [Andrew Or] Limit scope of synchronization
4e2ed7f [Andrew Or] Fix bug: keep track of removed executors properly
73ade46 [Andrew Or] Wording changes (minor)
2a7a6da [Andrew Or] Add `sc.killExecutor` as a shorthand (minor)
665f229 [Andrew Or] Mima excludes
79aa2df [Andrew Or] Simplify the request interface by asking for a total
04f625b [Andrew Or] Fix race condition that causes over-allocation of executors
f4783f8 [Andrew Or] Change the semantics of requesting executors
005a124 [Andrew Or] Fix tests
4628b16 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-scaling-mechanism
db4a679 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-scaling-mechanism
572f5c5 [Andrew Or] Unused import (minor)
f30261c [Andrew Or] Kill multiple executors rather than one at a time
de260d9 [Andrew Or] Simplify by skipping useless null check
9c52542 [Andrew Or] Simplify by skipping the TaskSchedulerImpl
97dd1a8 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-scaling-mechanism
d987b3e [Andrew Or] Move addWebUIFilters to Yarn scheduler backend
7b76d0a [Andrew Or] Expose mechanism in SparkContext as developer API
47466cd [Andrew Or] Refactor common Yarn scheduler backend logic
c4dfaac [Andrew Or] Avoid thrashing when removing executors
53e8145 [Andrew Or] Start yarn actor early to listen for AM registration message
bbee669 [Andrew Or] Add mechanism in yarn client mode
[SPARK-4097] Fix the race condition of 'thread'

There is a chance that `thread` is null when calling `thread.interrupt()`.

```Scala
  override def cancel(): Unit = this.synchronized {
    _cancelled = true
    if (thread != null) {
      thread.interrupt()
    }
  }
```
Should put `thread = null` into a `synchronized` block to fix the race condition.

Author: zsxwing <zsxwing@gmail.com>

Closes #2957 from zsxwing/SPARK-4097 and squashes the following commits:

edf0aee [zsxwing] Add comments to explain the lock
c5cfeca [zsxwing] Fix the race condition of 'thread'