[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