[SPARK-3706][PySpark] Cannot run IPython REPL with IPYTHON set to "1" and PYSPARK_PYTHON unset

### Problem

The section "Using the shell" in Spark Programming Guide (https://spark.apache.org/docs/latest/programming-guide.html#using-the-shell) says that we can run pyspark REPL through IPython.
But a folloing command does not run IPython but a default Python executable.

```
$ IPYTHON=1 ./bin/pyspark
Python 2.7.8 (default, Jul  2 2014, 10:14:46)
...
```

the spark/bin/pyspark script on the commit b235e013638685758885842dc3268e9800af3678 decides which executable and options it use folloing way.

1. if PYSPARK_PYTHON unset
   * → defaulting to "python"
2. if IPYTHON_OPTS set
   * → set IPYTHON "1"
3. some python scripts passed to ./bin/pyspak → run it with ./bin/spark-submit
   * out of this issues scope
4. if IPYTHON set as "1"
   * → execute $PYSPARK_PYTHON (default: ipython) with arguments $IPYTHON_OPTS
   * otherwise execute $PYSPARK_PYTHON

Therefore, when PYSPARK_PYTHON is unset, python is executed though IPYTHON is "1".
In other word, when PYSPARK_PYTHON is unset, IPYTHON_OPS and IPYTHON has no effect on decide which command to use.

PYSPARK_PYTHON | IPYTHON_OPTS | IPYTHON | resulting command | expected command
---- | ---- | ----- | ----- | -----
(unset → defaults to python) | (unset) | (unset) | python | (same)
(unset → defaults to python) | (unset) | 1 | python | ipython
(unset → defaults to python) | an_option | (unset → set to 1) | python an_option | ipython an_option
(unset → defaults to python) | an_option | 1 | python an_option | ipython an_option
ipython | (unset) | (unset) | ipython | (same)
ipython | (unset) | 1 | ipython | (same)
ipython | an_option | (unset → set to 1) | ipython an_option | (same)
ipython | an_option | 1 | ipython an_option | (same)

### Suggestion

The pyspark script should determine firstly whether a user wants to run IPython or other executables.

1. if IPYTHON_OPTS set
   * set IPYTHON "1"
2.  if IPYTHON has a value "1"
   * PYSPARK_PYTHON defaults to "ipython" if not set
3. PYSPARK_PYTHON defaults to "python" if not set

See the pull request for more detailed modification.

Author: cocoatomo <cocoatomo77@gmail.com>

Closes #2554 from cocoatomo/issues/cannot-run-ipython-without-options and squashes the following commits:

d2a9b06 [cocoatomo] [SPARK-3706][PySpark] Use PYTHONUNBUFFERED environment variable instead of -u option
264114c [cocoatomo] [SPARK-3706][PySpark] Remove the sentence about deprecated environment variables
42e02d5 [cocoatomo] [SPARK-3706][PySpark] Replace environment variables used to customize execution of PySpark REPL
10d56fb [cocoatomo] [SPARK-3706][PySpark] Cannot run IPython REPL with IPYTHON set to "1" and PYSPARK_PYTHON unset
[SQL][Docs] Update the output of printSchema and fix a typo in SQL programming guide.

We have changed the output format of `printSchema`. This PR will update our SQL programming guide to show the updated format. Also, it fixes a typo (the value type of `StructType` in Java API).

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #2630 from yhuai/sqlDoc and squashes the following commits:

267d63e [Yin Huai] Update the output of printSchema and fix a typo.
Modify default YARN memory_overhead-- from an additive constant to a multiplier

Redone against the recent master branch (https://github.com/apache/spark/pull/1391)

Author: Nishkam Ravi <nravi@cloudera.com>
Author: nravi <nravi@c1704.halxg.cloudera.com>
Author: nishkamravi2 <nishkamravi@gmail.com>

Closes #2485 from nishkamravi2/master_nravi and squashes the following commits:

636a9ff [nishkamravi2] Update YarnAllocator.scala
8f76c8b [Nishkam Ravi] Doc change for yarn memory overhead
35daa64 [Nishkam Ravi] Slight change in the doc for yarn memory overhead
5ac2ec1 [Nishkam Ravi] Remove out
dac1047 [Nishkam Ravi] Additional documentation for yarn memory overhead issue
42c2c3d [Nishkam Ravi] Additional changes for yarn memory overhead issue
362da5e [Nishkam Ravi] Additional changes for yarn memory overhead
c726bd9 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi
f00fa31 [Nishkam Ravi] Improving logging for AM memoryOverhead
1cf2d1e [nishkamravi2] Update YarnAllocator.scala
ebcde10 [Nishkam Ravi] Modify default YARN memory_overhead-- from an additive constant to a multiplier (redone to resolve merge conflicts)
2e69f11 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi
efd688a [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark
2b630f9 [nravi] Accept memory input as "30g", "512M" instead of an int value, to be consistent with rest of Spark
3bf8fad [nravi] Merge branch 'master' of https://github.com/apache/spark
5423a03 [nravi] Merge branch 'master' of https://github.com/apache/spark
eb663ca [nravi] Merge branch 'master' of https://github.com/apache/spark
df2aeb1 [nravi] Improved fix for ConcurrentModificationIssue (Spark-1097, Hadoop-10456)
6b840f0 [nravi] Undo the fix for SPARK-1758 (the problem is fixed)
5108700 [nravi] Fix in Spark for the Concurrent thread modification issue (SPARK-1097, HADOOP-10456)
681b36f [nravi] Fix for SPARK-1758: failing test org.apache.spark.JavaAPISuite.wholeTextFiles
[SPARK-3766][Doc]Snappy is also the default compress codec for broadcast variables

Author: scwf <wangfei1@huawei.com>

Closes #2632 from scwf/compress-doc and squashes the following commits:

7983a1a [scwf] snappy is the default compression codec for broadcast
[SPARK-3495] Block replication fails continuously when the replication target node is dead AND [SPARK-3496] Block replication by mistake chooses driver as target

If a block manager (say, A) wants to replicate a block and the node chosen for replication (say, B) is dead, then the attempt to send the block to B fails. However, this continues to fail indefinitely. Even if the driver learns about the demise of the B, A continues to try replicating to B and failing miserably.

The reason behind this bug is that A initially fetches a list of peers from the driver (when B was active), but never updates it after B is dead. This affects Spark Streaming as its receiver uses block replication.

The solution in this patch adds the following.
- Changed BlockManagerMaster to return all the peers of a block manager, rather than the requested number. It also filters out driver BlockManager.
- Refactored BlockManager's replication code to handle peer caching correctly.
    + The peer for replication is randomly selected. This is different from past behavior where for a node A, a node B was deterministically chosen for the lifetime of the application.
    + If replication fails to one node, the peers are refetched.
    + The peer cached has a TTL of 1 second to enable discovery of new peers and using them for replication.
- Refactored use of \<driver\> in BlockManager into a new method `BlockManagerId.isDriver`
- Added replication unit tests (replication was not tested till now, duh!)

This should not make a difference in performance of Spark workloads where replication is not used.

@andrewor14 @JoshRosen

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

Closes #2366 from tdas/replication-fix and squashes the following commits:

9690f57 [Tathagata Das] Moved replication tests to a new BlockManagerReplicationSuite.
0661773 [Tathagata Das] Minor changes based on PR comments.
a55a65c [Tathagata Das] Added a unit test to test replication behavior.
012afa3 [Tathagata Das] Bug fix
89f91a0 [Tathagata Das] Minor change.
68e2c72 [Tathagata Das] Made replication peer selection logic more efficient.
08afaa9 [Tathagata Das] Made peer selection for replication deterministic to block id
3821ab9 [Tathagata Das] Fixes based on PR comments.
08e5646 [Tathagata Das] More minor changes.
d402506 [Tathagata Das] Fixed imports.
4a20531 [Tathagata Das] Filtered driver block manager from peer list, and also consolidated the use of <driver> in BlockManager.
7598f91 [Tathagata Das] Minor changes.
03de02d [Tathagata Das] Change replication logic to correctly refetch peers from master on failure and on new worker addition.
d081bf6 [Tathagata Das] Fixed bug in get peers and unit tests to test get-peers and replication under executor churn.
9f0ac9f [Tathagata Das] Modified replication tests to fail on replication bug.
af0c1da [Tathagata Das] Added replication unit tests to BlockManagerSuite
[SPARK-3632] ConnectionManager can run out of receive threads with authentication on

If you turn authentication on and you are using a lot of executors. There is a chance that all the of the threads in the handleMessageExecutor could be waiting to send a message because they are blocked waiting on authentication to happen. This can cause a temporary deadlock until the connection times out.

To fix it, I got rid of the wait/notify and use a single outbox but only send security messages from it until authentication has completed.

Author: Thomas Graves <tgraves@apache.org>

Closes #2484 from tgravescs/cm_threads_auth and squashes the following commits:

a0a961d [Thomas Graves] give it a type
b6bc80b [Thomas Graves] Rework comments
d6d4175 [Thomas Graves] update from comments
081b765 [Thomas Graves] cleanup
4d7f8f5 [Thomas Graves] Change to not use wait/notify while waiting for authentication
[SPARK-3755][Core] avoid trying privileged port when request a non-privileged port

pwendell, ```tryPort``` is not compatible with old code in last PR, this is to fix it.
And after discuss with srowen renamed the title to "avoid trying privileged port when request a non-privileged port". Plz refer to the discuss for detail.

Author: scwf <wangfei1@huawei.com>

Closes #2623 from scwf/1-1024 and squashes the following commits:

10a4437 [scwf] add comment
de3fd17 [scwf] do not try privileged port when request a non-privileged port
42cb0fa [scwf] make tryPort compatible with old code
cb8cc76 [scwf] do not use port 1 - 1024
[DEPLOY] SPARK-3759: Return the exit code of the driver process

SparkSubmitDriverBootstrapper.scala now returns the exit code of the driver process, instead of always returning 0.

Author: Eric Eijkelenboom <ee@userreport.com>

Closes #2628 from ericeijkelenboom/master and squashes the following commits:

cc4a571 [Eric Eijkelenboom] Return the exit code of the driver process
[SQL] Initilize session state before creating CommandProcessor

With the old ordering it was possible for commands in the HiveDriver to NPE due to the lack of configuration in the threadlocal session state.

Author: Michael Armbrust <michael@databricks.com>

Closes #2635 from marmbrus/initOrder and squashes the following commits:

9749850 [Michael Armbrust] Initilize session state before creating CommandProcessor
[SPARK-3654][SQL] Implement all extended HiveQL statements/commands with a separate parser combinator

Created separate parser for hql. It preparses the commands like cache,uncache,add jar etc.. and then parses with HiveQl

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

Closes #2590 from ravipesala/SPARK-3654 and squashes the following commits:

bbca7dd [ravipesala] Fixed code as per admin comments.
ae9290a [ravipesala] Fixed style issues as per Admin comments
898ed81 [ravipesala] Removed spaces
fb24edf [ravipesala] Updated the code as per admin comments
8947d37 [ravipesala] Removed duplicate code
ba26cd1 [ravipesala] Created seperate parser for hql.It pre parses the commands like cache,uncache,add jar etc.. and then parses with HiveQl
[SPARK-3366][MLLIB]Compute best splits distributively in decision tree

Currently, all best splits are computed on the driver, which makes the driver a bottleneck for both communication and computation. This PR fix this problem by computed best splits on executors.
Instead of send all aggregate stats to the driver node, we can send aggregate stats for a node to a particular executor, using `reduceByKey` operation, then we can compute best split for this node there.

Implementation details:

Each node now has a nodeStatsAggregator, which save aggregate stats for all features and bins.
First use mapPartition to compute node aggregate stats for all nodes in each partition.
Then transform node aggregate stats to (nodeIndex, nodeStatsAggregator) pairs and use to `reduceByKey` operation to combine nodeStatsAggregator for the same node.
After all stats have been combined, best splits can be computed for each node based on the node aggregate stats. Best split result is collected to driver to construct the decision tree.

CC: mengxr manishamde jkbradley, please help me review this, thanks.

Author: qiping.lqp <qiping.lqp@alibaba-inc.com>
Author: chouqin <liqiping1991@gmail.com>

Closes #2595 from chouqin/dt-dist-agg and squashes the following commits:

db0d24a [chouqin] fix a minor bug and adjust code
a0d9de3 [chouqin] adjust code based on comments
9f201a6 [chouqin] fix bug: statsSize -> allStatsSize
a8a7ed0 [chouqin] Merge branch 'master' of https://github.com/apache/spark into dt-dist-agg
f13b346 [chouqin] adjust randomforest comments
c32636e [chouqin] adjust code based on comments
ac6a505 [chouqin] adjust code based on comments
7bbb787 [chouqin] add comments
bdd2a63 [qiping.lqp] fix test suite
a75df27 [qiping.lqp] fix test suite
b5b0bc2 [qiping.lqp] fix style
e76414f [qiping.lqp] fix testsuite
748bd45 [qiping.lqp] fix type-mismatch bug
24eacd8 [qiping.lqp] fix type-mismatch bug
5f63d6c [qiping.lqp] add multiclassification using One-Vs-All strategy
4f56496 [qiping.lqp] fix bug
f00fc22 [qiping.lqp] fix bug
532993a [qiping.lqp] Compute best splits distributively in decision tree