[SPARK-2580] [PySpark] keep silent in worker if JVM close the socket

During rdd.take(n), JVM will close the socket if it had got enough data, the Python worker should keep silent in this case.

In the same time, the worker should not print the trackback into stderr if it send the traceback to JVM successfully.

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

Closes #1625 from davies/error and squashes the following commits:

4fbcc6d [Davies Liu] disable log4j during testing when exception is expected.
cc14202 [Davies Liu] keep silent in worker if JVM close the socket
[SPARK-791] [PySpark] fix pickle itemgetter with cloudpickle

fix the problem with pickle operator.itemgetter with multiple index.

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

Closes #1627 from davies/itemgetter and squashes the following commits:

aabd7fa [Davies Liu] fix pickle itemgetter with cloudpickle
[SPARK-2726] and [SPARK-2727] Remove SortOrder and do in-place sort.

The pull request includes two changes:

1. Removes SortOrder introduced by SPARK-2125. The key ordering already includes the SortOrder information since an Ordering can be reverse. This is similar to Java's Comparator interface. Rarely does an API accept both a Comparator as well as a SortOrder.

2. Replaces the sortWith call in HashShuffleReader with an in-place quick sort.

Author: Reynold Xin <rxin@apache.org>

Closes #1631 from rxin/sortOrder and squashes the following commits:

c9d37e1 [Reynold Xin] [SPARK-2726] and [SPARK-2727] Remove SortOrder and do in-place sort.
[SPARK-2174][MLLIB] treeReduce and treeAggregate

In `reduce` and `aggregate`, the driver node spends linear time on the number of partitions. It becomes a bottleneck when there are many partitions and the data from each partition is big.

SPARK-1485 (#506) tracks the progress of implementing AllReduce on Spark. I did several implementations including butterfly, reduce + broadcast, and treeReduce + broadcast. treeReduce + BT broadcast seems to be right way to go for Spark. Using binary tree may introduce some overhead in communication, because the driver still need to coordinate on data shuffling. In my experiments, n -> sqrt(n) -> 1 gives the best performance in general, which is why I set "depth = 2" in MLlib algorithms. But it certainly needs more testing.

I left `treeReduce` and `treeAggregate` public for easy testing. Some numbers from a test on 32-node m3.2xlarge cluster.

code:

~~~
import breeze.linalg._
import org.apache.log4j._

Logger.getRootLogger.setLevel(Level.OFF)

for (n <- Seq(1, 10, 100, 1000, 10000, 100000, 1000000)) {
  val vv = sc.parallelize(0 until 1024, 1024).map(i => DenseVector.zeros[Double](n))
  var start = System.nanoTime(); vv.treeReduce(_ + _, 2); println((System.nanoTime() - start) / 1e9)
  start = System.nanoTime(); vv.reduce(_ + _); println((System.nanoTime() - start) / 1e9)
}
~~~

out:

| n | treeReduce(,2) | reduce |
|---|---------------------|-----------|
| 10 | 0.215538731 | 0.204206899 |
| 100 | 0.278405907 | 0.205732582 |
| 1000 | 0.208972182 | 0.214298272 |
| 10000 | 0.194792071 | 0.349353687 |
| 100000 | 0.347683285 | 6.086671892 |
| 1000000 | 2.589350682 | 66.572906702 |

CC: @pwendell

This is clearly more scalable than the default implementation. My question is whether we should use this implementation in `reduce` and `aggregate` or put them as separate methods. The concern is that users may use `reduce` and `aggregate` as collect, where having multiple stages doesn't reduce the data size. However, in this case, `collect` is more appropriate.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1110 from mengxr/tree and squashes the following commits:

c6cd267 [Xiangrui Meng] make depth default to 2
b04b96a [Xiangrui Meng] address comments
9bcc5d3 [Xiangrui Meng] add depth for readability
7495681 [Xiangrui Meng] fix compile error
142a857 [Xiangrui Meng] merge master
d58a087 [Xiangrui Meng] move treeReduce and treeAggregate to mllib
8a2a59c [Xiangrui Meng] Merge branch 'master' into tree
be6a88a [Xiangrui Meng] use treeAggregate in mllib
0f94490 [Xiangrui Meng] add docs
eb71c33 [Xiangrui Meng] add treeReduce
fe42a5e [Xiangrui Meng] add treeAggregate
Minor indentation and comment typo fixes.

Author: Aaron Staple <astaple@gmail.com>

Closes #1630 from staple/minor and squashes the following commits:

6f295a2 [Aaron Staple] Fix typos in comment about ExprId.
8566467 [Aaron Staple] Fix off by one column indentation in SqlParser.
[STREAMING] SPARK-1729. Make Flume pull data from source, rather than the current pu...

...sh model

Currently Spark uses Flume's internal Avro Protocol to ingest data from Flume. If the executor running the
receiver fails, it currently has to be restarted on the same node to be able to receive data.

This commit adds a new Sink which can be deployed to a Flume agent. This sink can be polled by a new
DStream that is also included in this commit. This model ensures that data can be pulled into Spark from
Flume even if the receiver is restarted on a new node. This also allows the receiver to receive data on
multiple threads for better performance.

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

Closes #807 from harishreedharan/master and squashes the following commits:

e7f70a3 [Hari Shreedharan] Merge remote-tracking branch 'asf-git/master'
96cfb6f [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
e48d785 [Hari Shreedharan] Documenting flume-sink being ignored for Mima checks.
5f212ce [Hari Shreedharan] Ignore Spark Sink from mima.
981bf62 [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
7a1bc6e [Hari Shreedharan] Fix SparkBuild.scala
a082eb3 [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
1f47364 [Hari Shreedharan] Minor fixes.
73d6f6d [Hari Shreedharan] Cleaned up tests a bit. Added some docs in multiple places.
65b76b4 [Hari Shreedharan] Fixing the unit test.
e59cc20 [Hari Shreedharan] Use SparkFlumeEvent instead of the new type. Also, Flume Polling Receiver now uses the store(ArrayBuffer) method.
f3c99d1 [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
3572180 [Hari Shreedharan] Adding a license header, making Jenkins happy.
799509f [Hari Shreedharan] Fix a compile issue.
3c5194c [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
d248d22 [harishreedharan] Merge pull request #1 from tdas/flume-polling
10b6214 [Tathagata Das] Changed public API, changed sink package, and added java unit test to make sure Java API is callable from Java.
1edc806 [Hari Shreedharan] SPARK-1729. Update logging in Spark Sink.
8c00289 [Hari Shreedharan] More debug messages
393bd94 [Hari Shreedharan] SPARK-1729. Use LinkedBlockingQueue instead of ArrayBuffer to keep track of connections.
120e2a1 [Hari Shreedharan] SPARK-1729. Some test changes and changes to utils classes.
9fd0da7 [Hari Shreedharan] SPARK-1729. Use foreach instead of map for all Options.
8136aa6 [Hari Shreedharan] Adding TransactionProcessor to map on returning batch of data
86aa274 [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
205034d [Hari Shreedharan] Merging master in
4b0c7fc [Hari Shreedharan] FLUME-1729. New Flume-Spark integration.
bda01fc [Hari Shreedharan] FLUME-1729. Flume-Spark integration.
0d69604 [Hari Shreedharan] FLUME-1729. Better Flume-Spark integration.
3c23c18 [Hari Shreedharan] SPARK-1729. New Spark-Flume integration.
70bcc2a [Hari Shreedharan] SPARK-1729. New Flume-Spark integration.
d6fa3aa [Hari Shreedharan] SPARK-1729. New Flume-Spark integration.
e7da512 [Hari Shreedharan] SPARK-1729. Fixing import order
9741683 [Hari Shreedharan] SPARK-1729. Fixes based on review.
c604a3c [Hari Shreedharan] SPARK-1729. Optimize imports.
0f10788 [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
87775aa [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
8df37e4 [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
03d6c1c [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
08176ad [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
d24d9d4 [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
6d6776a [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
[SQL]change some test lists

1. there's no `hook_context.q` but a `hook_context_cs.q` in query folder
2. there's no `compute_stats_table.q` in query folder
3. there's no `having1.q` in query folder
4. `udf_E` and `udf_PI` appear twice in white list

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

Closes #1634 from adrian-wang/testcases and squashes the following commits:

d7482ce [Daoyuan] change some test lists
[SPARK-2730][SQL] When retrieving a value from a Map, GetItem evaluates key twice

JIRA: https://issues.apache.org/jira/browse/SPARK-2730

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

Closes #1637 from yhuai/SPARK-2730 and squashes the following commits:

1a9f24e [Yin Huai] Remove unnecessary key evaluation.
[SPARK-2674] [SQL] [PySpark] support datetime type for SchemaRDD

Datetime and time in Python will be converted into java.util.Calendar after serialization, it will be converted into java.sql.Timestamp during inferSchema().

In javaToPython(), Timestamp will be converted into Calendar, then be converted into datetime in Python after pickling.

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

Closes #1601 from davies/date and squashes the following commits:

f0599b0 [Davies Liu] remove tests for sets and tuple in sql, fix list of list
c9d607a [Davies Liu] convert datetype for runtime
709d40d [Davies Liu] remove brackets
96db384 [Davies Liu] support datetime type for SchemaRDD
[SPARK-2082] stratified sampling in PairRDDFunctions that guarantees exact sample size

Implemented stratified sampling that guarantees exact sample size using ScaRSR with two passes over the RDD for sampling without replacement and three passes for sampling with replacement.

Author: Doris Xin <doris.s.xin@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>

Closes #1025 from dorx/stratified and squashes the following commits:

245439e [Doris Xin] moved minSamplingRate to getUpperBound
eaf5771 [Doris Xin] bug fixes.
17a381b [Doris Xin] fixed a merge issue and a failed unit
ea7d27f [Doris Xin] merge master
b223529 [Xiangrui Meng] use approx bounds for poisson fix poisson mean for waitlisting add unit tests for Java
b3013a4 [Xiangrui Meng] move math3 back to test scope
eecee5f [Doris Xin] Merge branch 'master' into stratified
f4c21f3 [Doris Xin] Reviewer comments
a10e68d [Doris Xin] style fix
a2bf756 [Doris Xin] Merge branch 'master' into stratified
680b677 [Doris Xin] use mapPartitionWithIndex instead
9884a9f [Doris Xin] style fix
bbfb8c9 [Doris Xin] Merge branch 'master' into stratified
ee9d260 [Doris Xin] addressed reviewer comments
6b5b10b [Doris Xin] Merge branch 'master' into stratified
254e03c [Doris Xin] minor fixes and Java API.
4ad516b [Doris Xin] remove unused imports from PairRDDFunctions
bd9dc6e [Doris Xin] unit bug and style violation fixed
1fe1cff [Doris Xin] Changed fractionByKey to a map to enable arg check
944a10c [Doris Xin] [SPARK-2145] Add lower bound on sampling rate
0214a76 [Doris Xin] cleanUp
90d94c0 [Doris Xin] merge master
9e74ab5 [Doris Xin] Separated out most of the logic in sampleByKey
7327611 [Doris Xin] merge master
50581fc [Doris Xin] added a TODO for logging in python
46f6c8c [Doris Xin] fixed the NPE caused by closures being cleaned before being passed into the aggregate function
7e1a481 [Doris Xin] changed the permission on SamplingUtil
1d413ce [Doris Xin] fixed checkstyle issues
9ee94ee [Doris Xin] [SPARK-2082] stratified sampling in PairRDDFunctions that guarantees exact sample size
e3fd6a6 [Doris Xin] Merge branch 'master' into takeSample
7cab53a [Doris Xin] fixed import bug in rdd.py
ffea61a [Doris Xin] SPARK-1939: Refactor takeSample method in RDD
1441977 [Doris Xin] SPARK-1939 Refactor takeSample method in RDD to use ScaSRS
[SPARK-2393][SQL] Cost estimation optimization framework for Catalyst logical plans & sample usage.

The idea is that every Catalyst logical plan gets hold of a Statistics class, the usage of which provides useful estimations on various statistics. See the implementations of `MetastoreRelation`.

This patch also includes several usages of the estimation interface in the planner. For instance, we now use physical table sizes from the estimate interface to convert an equi-join to a broadcast join (when doing so is beneficial, as determined by a size threshold).

Finally, there are a couple minor accompanying changes including:
- Remove the not-in-use `BaseRelation`.
- Make SparkLogicalPlan take a `SQLContext` in the second param list.

Author: Zongheng Yang <zongheng.y@gmail.com>

Closes #1238 from concretevitamin/estimates and squashes the following commits:

329071d [Zongheng Yang] Address review comments; turn config name from string to field in SQLConf.
8663e84 [Zongheng Yang] Use BigInt for stat; for logical leaves, by default throw an exception.
2f2fb89 [Zongheng Yang] Fix statistics for SparkLogicalPlan.
9951305 [Zongheng Yang] Remove childrenStats.
16fc60a [Zongheng Yang] Avoid calling statistics on plans if auto join conversion is disabled.
8bd2816 [Zongheng Yang] Add a note on performance of statistics.
6e594b8 [Zongheng Yang] Get size info from metastore for MetastoreRelation.
01b7a3e [Zongheng Yang] Update scaladoc for a field and move it to @param section.
549061c [Zongheng Yang] Remove numTuples in Statistics for now.
729a8e2 [Zongheng Yang] Update docs to be more explicit.
573e644 [Zongheng Yang] Remove singleton SQLConf and move back `settings` to the trait.
2d99eb5 [Zongheng Yang] {Cleanup, use synchronized in, enrich} StatisticsSuite.
ca5b825 [Zongheng Yang] Inject SQLContext into SparkLogicalPlan, removing SQLConf mixin from it.
43d38a6 [Zongheng Yang] Revert optimization for BroadcastNestedLoopJoin (this fixes tests).
0ef9e5b [Zongheng Yang] Use multiplication instead of sum for default estimates.
4ef0d26 [Zongheng Yang] Make Statistics a case class.
3ba8f3e [Zongheng Yang] Add comment.
e5bcf5b [Zongheng Yang] Fix optimization conditions & update scala docs to explain.
7d9216a [Zongheng Yang] Apply estimation to planning ShuffleHashJoin & BroadcastNestedLoopJoin.
73cde01 [Zongheng Yang] Move SQLConf back. Assign default sizeInBytes to SparkLogicalPlan.
73412be [Zongheng Yang] Move SQLConf to Catalyst & add default val for sizeInBytes.
7a60ab7 [Zongheng Yang] s/Estimates/Statistics, s/cardinality/numTuples.
de3ae13 [Zongheng Yang] Add parquetAfter() properly in test.
dcff9bd [Zongheng Yang] Cleanups.
84301a4 [Zongheng Yang] Refactors.
5bf5586 [Zongheng Yang] Typo.
56a8e6e [Zongheng Yang] Prototype impl of estimations for Catalyst logical plans.
MAINTENANCE: Automated closing of pull requests.

This commit exists to close the following pull requests on Github:

Closes #740 (close requested by 'rxin')
Closes #647 (close requested by 'rxin')
Closes #1383 (close requested by 'rxin')
Closes #1485 (close requested by 'pwendell')
Closes #693 (close requested by 'rxin')
Closes #478 (close requested by 'JoshRosen')
[SPARK-2716][SQL] Don't check resolved for having filters.

For queries like `... HAVING COUNT(*) > 9` the expression is always resolved since it contains no attributes.  This was causing us to avoid doing the Having clause aggregation rewrite.

Author: Michael Armbrust <michael@databricks.com>

Closes #1640 from marmbrus/havingNoRef and squashes the following commits:

92d3901 [Michael Armbrust] Don't check resolved for having filters.
[SPARK-2631][SQL] Use SQLConf to configure in-memory columnar caching

Author: Michael Armbrust <michael@databricks.com>

Closes #1638 from marmbrus/cachedConfig and squashes the following commits:

2362082 [Michael Armbrust] Use SQLConf to configure in-memory columnar caching
[SPARK-2305] [PySpark] Update Py4J to version 0.8.2.1

Author: Josh Rosen <joshrosen@apache.org>

Closes #1626 from JoshRosen/SPARK-2305 and squashes the following commits:

03fb283 [Josh Rosen] Update Py4J to version 0.8.2.1.
[SPARK-2054][SQL] Code Generation for Expression Evaluation

Adds a new method for evaluating expressions using code that is generated though Scala reflection.  This functionality is configured by the SQLConf option `spark.sql.codegen` and is currently turned off by default.

Evaluation can be done in several specialized ways:
 - *Projection* - Given an input row, produce a new row from a set of expressions that define each column in terms of the input row.  This can either produce a new Row object or perform the projection in-place on an existing Row (MutableProjection).
 - *Ordering* - Compares two rows based on a list of `SortOrder` expressions
 - *Condition* - Returns `true` or `false` given an input row.

For each of the above operations there is both a Generated and Interpreted version.  When generation for a given expression type is undefined, the code generator falls back on calling the `eval` function of the expression class.  Even without custom code, there is still a potential speed up, as loops are unrolled and code can still be inlined by JIT.

This PR also contains a new type of Aggregation operator, `GeneratedAggregate`, that performs aggregation by using generated `Projection` code.  Currently the required expression rewriting only works for simple aggregations like `SUM` and `COUNT`.  This functionality will be extended in a future PR.

This PR also performs several clean ups that simplified the implementation:
 - The notion of `Binding` all expressions in a tree automatically before query execution has been removed.  Instead it is the responsibly of an operator to provide the input schema when creating one of the specialized evaluators defined above.  In cases when the standard eval method is going to be called, binding can still be done manually using `BindReferences`.  There are a few reasons for this change:  First, there were many operators where it just didn't work before.  For example, operators with more than one child, and operators like aggregation that do significant rewriting of the expression. Second, the semantics of equality with `BoundReferences` are broken.  Specifically, we have had a few bugs where partitioning breaks because of the binding.
 - A copy of the current `SQLContext` is automatically propagated to all `SparkPlan` nodes by the query planner.  Before this was done ad-hoc for the nodes that needed this.  However, this required a lot of boilerplate as one had to always remember to make it `transient` and also had to modify the `otherCopyArgs`.

Author: Michael Armbrust <michael@databricks.com>

Closes #993 from marmbrus/newCodeGen and squashes the following commits:

96ef82c [Michael Armbrust] Merge remote-tracking branch 'apache/master' into newCodeGen
f34122d [Michael Armbrust] Merge remote-tracking branch 'apache/master' into newCodeGen
67b1c48 [Michael Armbrust] Use conf variable in SQLConf object
4bdc42c [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
41a40c9 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
de22aac [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
fed3634 [Michael Armbrust] Inspectors are not serializable.
ef8d42b [Michael Armbrust] comments
533fdfd [Michael Armbrust] More logging of expression rewriting for GeneratedAggregate.
3cd773e [Michael Armbrust] Allow codegen for Generate.
64b2ee1 [Michael Armbrust] Implement copy
3587460 [Michael Armbrust] Drop unused string builder function.
9cce346 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
1a61293 [Michael Armbrust] Address review comments.
0672e8a [Michael Armbrust] Address comments.
1ec2d6e [Michael Armbrust] Address comments
033abc6 [Michael Armbrust] off by default
4771fab [Michael Armbrust] Docs, more test coverage.
d30fee2 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
d2ad5c5 [Michael Armbrust] Refactor putting SQLContext into SparkPlan. Fix ordering, other test cases.
be2cd6b [Michael Armbrust] WIP: Remove old method for reference binding, more work on configuration.
bc88ecd [Michael Armbrust] Style
6cc97ca [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
4220f1e [Michael Armbrust] Better config, docs, etc.
ca6cc6b [Michael Armbrust] WIP
9d67d85 [Michael Armbrust] Fix hive planner
fc522d5 [Michael Armbrust] Hook generated aggregation in to the planner.
e742640 [Michael Armbrust] Remove unneeded changes and code.
675e679 [Michael Armbrust] Upgrade paradise.
0093376 [Michael Armbrust] Comment / indenting cleanup.
d81f998 [Michael Armbrust] include schema for binding.
0e889e8 [Michael Armbrust] Use typeOf instead tq
f623ffd [Michael Armbrust] Quiet logging from test suite.
efad14f [Michael Armbrust] Remove some half finished functions.
92e74a4 [Michael Armbrust] add overrides
a2b5408 [Michael Armbrust] WIP: Code generation with scala reflection.
[SPARK-2568] RangePartitioner should run only one job if data is balanced

As of Spark 1.0, RangePartitioner goes through data twice: once to compute the count and once to do sampling. As a result, to do sortByKey, Spark goes through data 3 times (once to count, once to sample, and once to sort).

`RangePartitioner` should go through data only once, collecting samples from input partitions as well as counting. If the data is balanced, this should give us a good sketch. If we see big partitions, we re-sample from them in order to collect enough items.

The downside is that we need to collect more from each partition in the first pass. An alternative solution is caching the intermediate result and decide whether to fetch the data after.

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

Closes #1562 from mengxr/range-partitioner and squashes the following commits:

6cc2551 [Xiangrui Meng] change foreach to for
eb39b08 [Xiangrui Meng] Merge branch 'master' into range-partitioner
eb95dd8 [Xiangrui Meng] separate sketching and determining bounds impl
c436d30 [Xiangrui Meng] fix binary metrics unit tests
db58a55 [Xiangrui Meng] add unit tests
a6e35d6 [Xiangrui Meng] minor update
60be09e [Xiangrui Meng] remove importance sampler
9ee9992 [Xiangrui Meng] update range partitioner to run only one job on roughly balanced data
cc12f47 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into range-part
06ac2ec [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into range-part
17bcbf3 [Reynold Xin] Added seed.
badf20d [Reynold Xin] Renamed the method.
6940010 [Reynold Xin] Reservoir sampling implementation.
[SQL] Handle null values in debug()

Author: Michael Armbrust <michael@databricks.com>

Closes #1646 from marmbrus/nullDebug and squashes the following commits:

49050a8 [Michael Armbrust] Handle null values in debug()
[SPARK-2260] Fix standalone-cluster mode, which was broken

The main thing was that spark configs were not propagated to the driver, and so applications that do not specify `master` or `appName` automatically failed. This PR fixes that and a couple of miscellaneous things that are related.

One thing that may or may not be an issue is that the jars must be available on the driver node. In `standalone-cluster` mode, this effectively means these jars must be available on all the worker machines, since the driver is launched on one of them. The semantics here are not the same as `yarn-cluster` mode,  where all the relevant jars are uploaded to a distributed cache automatically and shipped to the containers. This is probably not a concern, but still worth a mention.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1538 from andrewor14/standalone-cluster and squashes the following commits:

8c11a0d [Andrew Or] Clean up imports / comments (minor)
2678d13 [Andrew Or] Handle extraJavaOpts properly
7660547 [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-cluster
6f64a9b [Andrew Or] Revert changes in YARN
2f2908b [Andrew Or] Fix tests
ed01491 [Andrew Or] Don't go overboard with escaping
8e105e1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-cluster
b890949 [Andrew Or] Abstract usages of converting spark opts to java opts
79f63a3 [Andrew Or] Move sparkProps into javaOpts
78752f8 [Andrew Or] Fix tests
5a9c6c7 [Andrew Or] Fix line too long
c141a00 [Andrew Or] Don't display "unknown app" on driver log pages
d7e2728 [Andrew Or] Avoid deprecation warning in standalone Client
6ceb14f [Andrew Or] Allow relevant configs to propagate to standalone Driver
7f854bc [Andrew Or] Fix test
855256e [Andrew Or] Fix standalone-cluster mode
fd9da51 [Andrew Or] Formatting changes (minor)
[SPARK-2179][SQL] Public API for DataTypes and Schema

The current PR contains the following changes:
* Expose `DataType`s in the sql package (internal details are private to sql).
* Users can create Rows.
* Introduce `applySchema` to create a `SchemaRDD` by applying a `schema: StructType` to an `RDD[Row]`.
* Add a function `simpleString` to every `DataType`. Also, the schema represented by a `StructType` can be visualized by `printSchema`.
* `ScalaReflection.typeOfObject` provides a way to infer the Catalyst data type based on an object. Also, we can compose `typeOfObject` with some custom logics to form a new function to infer the data type (for different use cases).
* `JsonRDD` has been refactored to use changes introduced by this PR.
* Add a field `containsNull` to `ArrayType`. So, we can explicitly mark if an `ArrayType` can contain null values. The default value of `containsNull` is `false`.

New APIs are introduced in the sql package object and SQLContext. You can find the scaladoc at
[sql package object](http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.sql.package) and [SQLContext](http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.sql.SQLContext).

An example of using `applySchema` is shown below.
```scala
import org.apache.spark.sql._
val sqlContext = new org.apache.spark.sql.SQLContext(sc)

val schema =
  StructType(
    StructField("name", StringType, false) ::
    StructField("age", IntegerType, true) :: Nil)

val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split(",")).map(p => Row(p(0), p(1).trim.toInt))
val peopleSchemaRDD = sqlContext. applySchema(people, schema)
peopleSchemaRDD.printSchema
// root
// |-- name: string (nullable = false)
// |-- age: integer (nullable = true)

peopleSchemaRDD.registerAsTable("people")
sqlContext.sql("select name from people").collect.foreach(println)
```

I will add new contents to the SQL programming guide later.

JIRA: https://issues.apache.org/jira/browse/SPARK-2179

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

Closes #1346 from yhuai/dataTypeAndSchema and squashes the following commits:

1d45977 [Yin Huai] Clean up.
a6e08b4 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
c712fbf [Yin Huai] Converts types of values based on defined schema.
4ceeb66 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
e5f8df5 [Yin Huai] Scaladoc.
122d1e7 [Yin Huai] Address comments.
03bfd95 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
2476ed0 [Yin Huai] Minor updates.
ab71f21 [Yin Huai] Format.
fc2bed1 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
bd40a33 [Yin Huai] Address comments.
991f860 [Yin Huai] Move "asJavaDataType" and "asScalaDataType" to DataTypeConversions.scala.
1cb35fe [Yin Huai] Add "valueContainsNull" to MapType.
3edb3ae [Yin Huai] Python doc.
692c0b9 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
1d93395 [Yin Huai] Python APIs.
246da96 [Yin Huai] Add java data type APIs to javadoc index.
1db9531 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
d48fc7b [Yin Huai] Minor updates.
33c4fec [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
b9f3071 [Yin Huai] Java API for applySchema.
1c9f33c [Yin Huai] Java APIs for DataTypes and Row.
624765c [Yin Huai] Tests for applySchema.
aa92e84 [Yin Huai] Update data type tests.
8da1a17 [Yin Huai] Add Row.fromSeq.
9c99bc0 [Yin Huai] Several minor updates.
1d9c13a [Yin Huai] Update applySchema API.
85e9b51 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
e495e4e [Yin Huai] More comments.
42d47a3 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
c3f4a02 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
2e58dbd [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
b8b7db4 [Yin Huai] 1. Move sql package object and package-info to sql-core. 2. Minor updates on APIs. 3. Update scala doc.
68525a2 [Yin Huai] Update JSON unit test.
3209108 [Yin Huai] Add unit tests.
dcaf22f [Yin Huai] Add a field containsNull to ArrayType to indicate if an array can contain null values or not. If an ArrayType is constructed by "ArrayType(elementType)" (the existing constructor), the value of containsNull is false.
9168b83 [Yin Huai] Update comments.
fc649d7 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
eca7d04 [Yin Huai] Add two apply methods which will be used to extract StructField(s) from a StructType.
949d6bb [Yin Huai] When creating a SchemaRDD for a JSON dataset, users can apply an existing schema.
7a6a7e5 [Yin Huai] Fix bug introduced by the change made on SQLContext.inferSchema.
43a45e1 [Yin Huai] Remove sql.util.package introduced in a previous commit.
0266761 [Yin Huai] Format
03eec4c [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
90460ac [Yin Huai] Infer the Catalyst data type from an object and cast a data value to the expected type.
3fa0df5 [Yin Huai] Provide easier ways to construct a StructType.
16be3e5 [Yin Huai] This commit contains three changes: * Expose `DataType`s in the sql package (internal details are private to sql). * Introduce `createSchemaRDD` to create a `SchemaRDD` from an `RDD` with a provided schema (represented by a `StructType`) and a provided function to construct `Row`, * Add a function `simpleString` to every `DataType`. Also, the schema represented by a `StructType` can be visualized by `printSchema`.