## What changes were proposed in this pull request?

Update snappy to 1.1.2.1 to pull in a single fix -- the OOM fix we already worked around.
Supersedes https://github.com/apache/spark/pull/11524

## How was this patch tested?

Jenkins tests.

Author: Sean Owen <sowen@cloudera.com>

Closes #11631 from srowen/SPARK-13663.

Adding support for other numeric types:

* Integer
* Short
* Long
* Float
* Decimal

Author: sethah <seth.hendrickson16@gmail.com>

Closes #9777 from sethah/SPARK-11108.

## What changes were proposed in this pull request?

According to #11627 , this PR replace `DataFrameWriter.stream()` with `startStream()` in comments of `ContinuousQueryListener.java`.

## How was this patch tested?

Manual. (It changes on comments.)

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #11629 from dongjoon-hyun/minor_rename.

## What changes were proposed in this pull request?

This pull request adds a python example for train validation split.

## How was this patch tested?

This was style tested through lint-python, generally tested with ./dev/run-tests, and run in notebook and shell environments. It was viewed in docs locally with jekyll serve.

This contribution is my original work and I license it to Spark under its open source license.

Author: JeremyNixon <jnixon2@gmail.com>

Closes #11547 from JeremyNixon/tvs_example.

[SPARK-7420][STREAMING][TESTS] Enable test: o.a.s.streaming.JobGeneratorSuite "Do not clear received…

## How was this patch tested?

unit test

Author: proflin <proflin.me@gmail.com>

Closes #11626 from lw-lin/SPARK-7420.

## What changes were proposed in this pull request?
The new name makes it more obvious with the verb "start" that we are actually starting some execution.

## How was this patch tested?
This is just a rename. Existing unit tests should cover it.

Author: Reynold Xin <rxin@databricks.com>

Closes #11627 from rxin/SPARK-13794.

## What changes were proposed in this pull request?

https://issues.apache.org/jira/browse/SPARK-13766
This PR makes the file extensions (written by internal datasource) consistent.

**Before**

- TEXT, CSV and JSON
```
[.COMPRESSION_CODEC_NAME]
```

- Parquet
```
[.COMPRESSION_CODEC_NAME].parquet
```

- ORC
```
.orc
```

**After**

- TEXT, CSV and JSON
```
.txt[.COMPRESSION_CODEC_NAME]
.csv[.COMPRESSION_CODEC_NAME]
.json[.COMPRESSION_CODEC_NAME]
```

- Parquet
```
[.COMPRESSION_CODEC_NAME].parquet
```

- ORC
```
[.COMPRESSION_CODEC_NAME].orc
```

When the compression codec is set,
- For Parquet and ORC, each still stays in Parquet and ORC format but just have compressed data internally. So, I think it is okay to name `.parquet` and `.orc` at the end.

- For Text, CSV and JSON, each does not stays in each format but it has different data format according to compression codec. So, each has the names `.json`, `.csv` and `.txt` before the compression extension.

## How was this patch tested?

Unit tests are used and `./dev/run_tests` for coding style tests.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #11604 from HyukjinKwon/SPARK-13766.

This reverts commit 926e9c45.

## What changes were proposed in this pull request?

A very minor change for using `BigDecimal.decimal(f: Float)` instead of `BigDecimal(f: float)`. The latter is deprecated and can result in inconsistencies due to an implicit conversion to `Double`.

## How was this patch tested?

N/A

cc yhuai

Author: Sameer Agarwal <sameer@databricks.com>

Closes #11597 from sameeragarwal/bigdecimal.

## What changes were proposed in this pull request?

Previously the Mesos framework webui URL was being derived only from the Spark UI address leaving no possibility to configure it. This commit makes it configurable. If unset it falls back to the previous behavior.

Motivation:
This change is necessary in order to be able to install Spark on DCOS and to be able to give it a custom service link. The configured `webui_url` is configured to point to a reverse proxy in the DCOS environment.

## How was this patch tested?

Locally, using unit tests and on DCOS testing and stable revision.

Author: Sergiusz Urbaniak <sur@mesosphere.io>

Closes #11369 from s-urbaniak/sur-webui-url.

Minor typo:  docstring for pyspark.sql.functions: hypot has extra characters

N/A

Author: Tristan Reid <treid@netflix.com>

Closes #11616 from tristanreid/master.

## What changes were proposed in this pull request?
Originally the page is sorted by AppID by default.
After tests with users' feedback, we think it might be best to sort by completed time (desc).

## How was this patch tested?
Manually test, with screenshot as follows.
![sorted-by-complete-time-desc](https://cloud.githubusercontent.com/assets/11683054/13647686/d6dea924-e5fa-11e5-8fc5-68e039b74b6f.png)

Author: zhuol <zhuol@yahoo-inc.com>

Closes #11608 from zhuoliu/13775.

## What changes were proposed in this pull request?

When a worker is lost, the executors on this worker are also lost. But Master's ApplicationPage still displays their states as running.

This patch just sets the executor state to `LOST` when a worker is lost.

## How was this patch tested?

manual tests

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #11609 from zsxwing/SPARK-13778.

## What changes were proposed in this pull request?

Fix this use case, which was already fixed in SPARK-10548 in 1.6 but was broken in master due to #9264:

```
(1 to 100).par.foreach { _ => sc.parallelize(1 to 5).map { i => (i, i) }.toDF("a", "b").count() }
```

This threw `IllegalArgumentException` consistently before this patch. For more detail, see the JIRA.

## How was this patch tested?

New test in `SQLExecutionSuite`.

Author: Andrew Or <andrew@databricks.com>

Closes #11586 from andrewor14/fix-concurrent-sql.

## What changes were proposed in this pull request?

This PR is a small follow up on https://github.com/apache/spark/pull/11338 (https://issues.apache.org/jira/browse/SPARK-13092) to use `ExpressionSet` as part of the verification logic in `ConstraintPropagationSuite`.
## How was this patch tested?

No new tests added. Just changes the verification logic in `ConstraintPropagationSuite`.

Author: Sameer Agarwal <sameer@databricks.com>

Closes #11611 from sameeragarwal/expression-set.

This patch adds an API entry point for single decision tree feature importances.

Author: sethah <seth.hendrickson16@gmail.com>

Closes #9912 from sethah/SPARK-11861.

#### What changes were proposed in this pull request?

Remove all the deterministic conditions in a [[Filter]] that are contained in the Child's Constraints.

For example, the first query can be simplified to the second one.

```scala
    val queryWithUselessFilter = tr1
      .where("tr1.a".attr > 10 || "tr1.c".attr < 10)
      .join(tr2.where('d.attr < 100), Inner, Some("tr1.a".attr === "tr2.a".attr))
      .where(
        ("tr1.a".attr > 10 || "tr1.c".attr < 10) &&
        'd.attr < 100 &&
        "tr2.a".attr === "tr1.a".attr)
```
```scala
    val query = tr1
      .where("tr1.a".attr > 10 || "tr1.c".attr < 10)
      .join(tr2.where('d.attr < 100), Inner, Some("tr1.a".attr === "tr2.a".attr))
```
#### How was this patch tested?

Six test cases are added.

Author: gatorsmile <gatorsmile@gmail.com>

Closes #11406 from gatorsmile/FilterRemoval.

## What changes were proposed in this pull request?

It’s possible to have common parts in a query, for example, self join, it will be good to avoid the duplicated part to same CPUs and memory (Broadcast or cache).

Exchange will materialize the underlying RDD by shuffle or collect, it’s a great point to check duplicates and reuse them. Duplicated exchanges means they generate exactly the same result inside a query.

In order to find out the duplicated exchanges, we should be able to compare SparkPlan to check that they have same results or not. We already have that for LogicalPlan, so we should move that into QueryPlan to make it available for SparkPlan.

Once we can find the duplicated exchanges, we should replace all of them with same SparkPlan object (could be wrapped by ReusedExchage for explain), then the plan tree become a DAG. Since all the planner only work with tree, so this rule should be the last one for the entire planning.

After the rule, the plan will looks like:

```
WholeStageCodegen
:  +- Project [id#0L]
:     +- BroadcastHashJoin [id#0L], [id#2L], Inner, BuildRight, None
:        :- Project [id#0L]
:        :  +- BroadcastHashJoin [id#0L], [id#1L], Inner, BuildRight, None
:        :     :- Range 0, 1, 4, 1024, [id#0L]
:        :     +- INPUT
:        +- INPUT
:- BroadcastExchange HashedRelationBroadcastMode(true,List(id#1L),List(id#1L))
:  +- WholeStageCodegen
:     :  +- Range 0, 1, 4, 1024, [id#1L]
+- ReusedExchange [id#2L], BroadcastExchange HashedRelationBroadcastMode(true,List(id#1L),List(id#1L))
```

![bjoin](https://cloud.githubusercontent.com/assets/40902/13414787/209e8c5c-df0a-11e5-8a0f-edff69d89e83.png)

For three ways SortMergeJoin,
```
== Physical Plan ==
WholeStageCodegen
:  +- Project [id#0L]
:     +- SortMergeJoin [id#0L], [id#4L], None
:        :- INPUT
:        +- INPUT
:- WholeStageCodegen
:  :  +- Project [id#0L]
:  :     +- SortMergeJoin [id#0L], [id#3L], None
:  :        :- INPUT
:  :        +- INPUT
:  :- WholeStageCodegen
:  :  :  +- Sort [id#0L ASC], false, 0
:  :  :     +- INPUT
:  :  +- Exchange hashpartitioning(id#0L, 200), None
:  :     +- WholeStageCodegen
:  :        :  +- Range 0, 1, 4, 33554432, [id#0L]
:  +- WholeStageCodegen
:     :  +- Sort [id#3L ASC], false, 0
:     :     +- INPUT
:     +- ReusedExchange [id#3L], Exchange hashpartitioning(id#0L, 200), None
+- WholeStageCodegen
   :  +- Sort [id#4L ASC], false, 0
   :     +- INPUT
   +- ReusedExchange [id#4L], Exchange hashpartitioning(id#0L, 200), None
```
![sjoin](https://cloud.githubusercontent.com/assets/40902/13414790/27aea61c-df0a-11e5-8cbf-fbc985c31d95.png)

If the same ShuffleExchange or BroadcastExchange, execute()/executeBroadcast() will be called by different parents, they should cached the RDD/Broadcast, return the same one for all the parents.

## How was this patch tested?

Added some unit tests for this.  Had done some manual tests on TPCDS query Q59 and Q64, we can see some exchanges are re-used (this requires a change in PhysicalRDD to for sameResult, is be done in #11514 ).

Author: Davies Liu <davies@databricks.com>

Closes #11403 from davies/dedup.

## What changes were proposed in this pull request?
```GeneralizedLinearRegression``` supports ```save/load```.
cc mengxr
## How was this patch tested?
unit test.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #11465 from yanboliang/spark-13615.

## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-13728

https://github.com/apache/spark/pull/11509 makes the output only single ORC file.
It was 10 files but this PR writes only single file. So, this could not skip stripes in ORC by the pushed down filters.
So, this PR simply repartitions data into 10 so that the test could pass.
## How was this patch tested?

unittest and `./dev/run_tests` for code style test.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #11593 from HyukjinKwon/SPARK-13728.

#### What changes were proposed in this pull request?

As shown in another PR: https://github.com/apache/spark/pull/11596, we are using `SELECT 1` as a dummy table, when the table is used for SQL statements in which a table reference is required, but the contents of the table are not important. For example,

```SQL
SELECT value FROM (select 1) dummyTable Lateral View explode(array(1,2,3)) adTable as value
```
Before the PR, the optimized plan contains a useless `Project` after Optimizer executing the `ColumnPruning` rule, as shown below:

```
== Analyzed Logical Plan ==
value: int
Project [value#22]
+- Generate explode(array(1, 2, 3)), true, false, Some(adtable), [value#22]
   +- SubqueryAlias dummyTable
      +- Project [1 AS 1#21]
         +- OneRowRelation$

== Optimized Logical Plan ==
Generate explode([1,2,3]), false, false, Some(adtable), [value#22]
+- Project
   +- OneRowRelation$
```

After the fix, the optimized plan removed the useless `Project`, as shown below:
```
== Optimized Logical Plan ==
Generate explode([1,2,3]), false, false, Some(adtable), [value#22]
+- OneRowRelation$
```

This PR is to remove `Project` when its Child's output is Nil

#### How was this patch tested?

Added a new unit test case into the suite `ColumnPruningSuite.scala`

Author: gatorsmile <gatorsmile@gmail.com>

Closes #11599 from gatorsmile/projectOneRowRelation.

## What changes were proposed in this pull request?

Move `docker` dirs out of top level into `external/`; move `extras/*` into `external/`

## How was this patch tested?

This is tested with Jenkins tests.

Author: Sean Owen <sowen@cloudera.com>

Closes #11523 from srowen/SPARK-13595.

This reverts commit e430614e.

## What changes were proposed in this pull request?

If there are many branches in a CaseWhen expression, the generated code could go above the 64K limit for single java method, will fail to compile. This PR change it to fallback to interpret mode if there are more than 20 branches.

This PR is based on #11243 and #11221, thanks to joehalliwell

Closes #11243
Closes #11221

## How was this patch tested?

Add a test with 50 branches.

Author: Davies Liu <davies@databricks.com>

Closes #11592 from davies/fix_when.

## What changes were proposed in this pull request?
Analysis exception occurs while running the following query.
```
SELECT ints FROM nestedArray LATERAL VIEW explode(a.b) `a` AS `ints`
```
```
Failed to analyze query: org.apache.spark.sql.AnalysisException: cannot resolve '`ints`' given input columns: [a, `ints`]; line 1 pos 7
'Project ['ints]
+- Generate explode(a#0.b), true, false, Some(a), [`ints`#8]
   +- SubqueryAlias nestedarray
      +- LocalRelation [a#0], [[[[1,2,3]]]]
```

## How was this patch tested?

Added new unit tests in SQLQuerySuite and HiveQlSuite

Author: Dilip Biswal <dbiswal@us.ibm.com>

Closes #11538 from dilipbiswal/SPARK-13698.

JIRA : https://issues.apache.org/jira/browse/SPARK-13769

The java doc here (https://github.com/apache/spark/blob/e97fc7f176f8bf501c9b3afd8410014e3b0e1602/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala#L51)
needs to be updated from "The latter two operations are currently supported only for standalone cluster mode." to "The latter two operations are currently supported only for standalone and mesos cluster modes."

Author: Ahmed Kamal <ahmed.kamal@badrit.com>

Closes #11600 from AhmedKamal/SPARK-13769.

## What changes were proposed in this pull request?

In order to make `docs/examples` (and other related code) more simple/readable/user-friendly, this PR replaces existing codes like the followings by using `diamond` operator.

```
-    final ArrayList<Product2<Object, Object>> dataToWrite =
-      new ArrayList<Product2<Object, Object>>();
+    final ArrayList<Product2<Object, Object>> dataToWrite = new ArrayList<>();
```

Java 7 or higher supports **diamond** operator which replaces the type arguments required to invoke the constructor of a generic class with an empty set of type parameters (<>). Currently, Spark Java code use mixed usage of this.

## How was this patch tested?

Manual.
Pass the existing tests.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #11541 from dongjoon-hyun/SPARK-13702.

## What changes were proposed in this pull request?

If a job is being scheduled in one thread which has a dependency on an
RDD currently executing a shuffle in another thread, Spark would throw a
NullPointerException. This patch synchronizes access to `mapStatuses` and
skips null status entries (which are in-progress shuffle tasks).

## How was this patch tested?

Our client code unit test suite, which was reliably reproducing the race
condition with 10 threads, shows that this fixes it. I have not found a minimal
test case to add to Spark, but I will attempt to do so if desired.

The same test case was tripping up on SPARK-4454, which was fixed by
making other DAGScheduler code thread-safe.

shivaram srowen

Author: Andy Sloane <asloane@tetrationanalytics.com>

Closes #11505 from a1k0n/SPARK-13631.

## What changes were proposed in this pull request?

`ScalaReflection.mirror` method should be synchronized when scala version is `2.10` because `universe.runtimeMirror` is not thread safe.

## How was this patch tested?

I added a test to check thread safety of `ScalaRefection.mirror` method in `ScalaReflectionSuite`, which will throw the following Exception in Scala `2.10` without this patch:

```
[info] - thread safety of mirror *** FAILED *** (49 milliseconds)
[info]   java.lang.UnsupportedOperationException: tail of empty list
[info]   at scala.collection.immutable.Nil$.tail(List.scala:339)
[info]   at scala.collection.immutable.Nil$.tail(List.scala:334)
[info]   at scala.reflect.internal.SymbolTable.popPhase(SymbolTable.scala:172)
[info]   at scala.reflect.internal.Symbols$Symbol.unsafeTypeParams(Symbols.scala:1477)
[info]   at scala.reflect.internal.Symbols$TypeSymbol.tpe(Symbols.scala:2777)
[info]   at scala.reflect.internal.Mirrors$RootsBase.init(Mirrors.scala:235)
[info]   at scala.reflect.runtime.JavaMirrors$class.createMirror(JavaMirrors.scala:34)
[info]   at scala.reflect.runtime.JavaMirrors$class.runtimeMirror(JavaMirrors.scala:61)
[info]   at scala.reflect.runtime.JavaUniverse.runtimeMirror(JavaUniverse.scala:12)
[info]   at scala.reflect.runtime.JavaUniverse.runtimeMirror(JavaUniverse.scala:12)
[info]   at org.apache.spark.sql.catalyst.ScalaReflection$.mirror(ScalaReflection.scala:36)
[info]   at org.apache.spark.sql.catalyst.ScalaReflectionSuite$$anonfun$12$$anonfun$apply$mcV$sp$1$$anonfun$apply$1$$anonfun$apply$2.apply(ScalaReflectionSuite.scala:256)
[info]   at org.apache.spark.sql.catalyst.ScalaReflectionSuite$$anonfun$12$$anonfun$apply$mcV$sp$1$$anonfun$apply$1$$anonfun$apply$2.apply(ScalaReflectionSuite.scala:252)
[info]   at scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)
[info]   at scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)
[info]   at scala.concurrent.impl.ExecutionContextImpl$$anon$3.exec(ExecutionContextImpl.scala:107)
[info]   at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
[info]   at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
[info]   at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
[info]   at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
```

Notice that the test will pass when Scala version is `2.11`.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #11487 from ueshin/issues/SPARK-13640.

## What changes were proposed in this pull request?

This issue fixes the following potential bugs and Java coding style detected by Coverity and Checkstyle.

- Implement both null and type checking in equals functions.
- Fix wrong type casting logic in SimpleJavaBean2.equals.
- Add `implement Cloneable` to `UTF8String` and `SortedIterator`.
- Remove dereferencing before null check in `AbstractBytesToBytesMapSuite`.
- Fix coding style: Add '{}' to single `for` statement in mllib examples.
- Remove unused imports in `ColumnarBatch` and `JavaKinesisStreamSuite`.
- Remove unused fields in `ChunkFetchIntegrationSuite`.
- Add `stop()` to prevent resource leak.

Please note that the last two checkstyle errors exist on newly added commits after [SPARK-13583](https://issues.apache.org/jira/browse/SPARK-13583).

## How was this patch tested?

manual via `./dev/lint-java` and Coverity site.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #11530 from dongjoon-hyun/SPARK-13692.

This PR replaces #9925 which had issues with CI. **Please see the original PR for any previous discussions.**

## What changes were proposed in this pull request?
Deprecate the SparkSQL column operator !== and use =!= as an alternative.
Fixes subtle issues related to operator precedence (basically, !== does not have the same priority as its logical negation, ===).

## How was this patch tested?
All currently existing tests.

Author: Jakob Odersky <jodersky@gmail.com>

Closes #11588 from jodersky/SPARK-7286.

## Motivation
CSV data source was contributed by Databricks. It is the inlined version of https://github.com/databricks/spark-csv. The data source name was `com.databricks.spark.csv`. As a result there are many tables created on older versions of spark with that name as the source. For backwards compatibility we should keep the old name.

## Proposed changes
`com.databricks.spark.csv` was added to list of `backwardCompatibilityMap` in `ResolvedDataSource.scala`

## Tests
A unit test was added to `CSVSuite` to parse a csv file using the old name.

Author: Hossein <hossein@databricks.com>

Closes #11589 from falaki/SPARK-13754.

## What changes were proposed in this pull request?

This PR fix the sizeInBytes of HadoopFsRelation.

## How was this patch tested?

Added regression test for that.

Author: Davies Liu <davies@databricks.com>

Closes #11590 from davies/fix_sizeInBytes.

[SPARK-13625][PYSPARK][ML] Added a check to see if an attribute is a property when getting param list

## What changes were proposed in this pull request?

Added a check in pyspark.ml.param.Param.params() to see if an attribute is a property (decorated with `property`) before checking if it is a `Param` instance.  This prevents the property from being invoked to 'get' this attribute, which could possibly cause an error.

## How was this patch tested?

Added a test case with a class has a property that will raise an error when invoked and then call`Param.params` to verify that the property is not invoked, but still able to find another property in the class.  Also ran pyspark-ml test before fix that will trigger an error, and again after the fix to verify that the error was resolved and the method was working properly.

Author: Bryan Cutler <cutlerb@gmail.com>

Closes #11476 from BryanCutler/pyspark-ml-property-attr-SPARK-13625.

When generating Graphviz DOT files in the SQL query visualization we need to escape double-quotes inside node labels. This is a followup to #11309, which fixed a similar graph in Spark Core's DAG visualization.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #11587 from JoshRosen/graphviz-escaping.

## What changes were proposed in this pull request?

If a filter predicate or a join condition consists of `IsNotNull` checks, we should reorder these checks such that these non-nullability checks are evaluated before the rest of the predicates.

For e.g., if a filter predicate is of the form `a > 5 && isNotNull(b)`, we should rewrite this as `isNotNull(b) && a > 5` during physical plan generation.

## How was this patch tested?

new unit tests that verify the physical plan for both filters and joins in `ReorderedPredicateSuite`

Author: Sameer Agarwal <sameer@databricks.com>

Closes #11511 from sameeragarwal/reorder-isnotnull.

Follow-up to #11509, that simply refactors the interface that we use when resolving a pluggable `DataSource`.
 - Multiple functions share the same set of arguments so we make this a case class, called `DataSource`.  Actual resolution is now done by calling a function on this class.
 - Instead of having multiple methods named `apply` (some of which do writing some of which do reading) we now explicitly have `resolveRelation()` and `write(mode, df)`.
 - Get rid of `Array[String]` since this is an internal API and was forcing us to awkwardly call `toArray` in a bunch of places.

Author: Michael Armbrust <michael@databricks.com>

Closes #11572 from marmbrus/dataSourceResolution.

## What changes were proposed in this pull request?

This removes the remaining deprecated Octal escape literals. The followings are the warnings on those two lines.
```
LiteralExpressionSuite.scala:99: Octal escape literals are deprecated, use \u0000 instead.
HiveQlSuite.scala:74: Octal escape literals are deprecated, use \u002c instead.
```

## How was this patch tested?

Manual.
During building, there should be no warning on `Octal escape literals`.
```
mvn -DskipTests clean install
```

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #11584 from dongjoon-hyun/SPARK-13400.

## What changes were proposed in this pull request?

This PR improves the `createDataFrame` method to make it also accept datatype string, then users can convert python RDD to DataFrame easily, for example, `df = rdd.toDF("a: int, b: string")`.
It also supports flat schema so users can convert an RDD of int to DataFrame directly, we will automatically wrap int to row for users.
If schema is given, now we checks if the real data matches the given schema, and throw error if it doesn't.

## How was this patch tested?

new tests in `test.py` and doc test in `types.py`

Author: Wenchen Fan <wenchen@databricks.com>

Closes #11444 from cloud-fan/pyrdd.

## What changes were proposed in this pull request?

This PR adds null check in `_verify_type` according to the nullability information.

## How was this patch tested?

new doc tests

Author: Wenchen Fan <wenchen@databricks.com>

Closes #11574 from cloud-fan/py-null-check.