[EC2] Factor out Mesos spark-ec2 branch

We reference a specific branch in two places. This patch makes it one place.

Author: Nicholas Chammas <nicholas.chammas@gmail.com>

Closes #3008 from nchammas/mesos-spark-ec2-branch and squashes the following commits:

10a6089 [Nicholas Chammas] factor out mess spark-ec2 branch
[SPARK-4148][PySpark] fix seed distribution and add some tests for rdd.sample

The current way of seed distribution makes the random sequences from partition i and i+1 offset by 1.

~~~
In [14]: import random

In [15]: r1 = random.Random(10)

In [16]: r1.randint(0, 1)
Out[16]: 1

In [17]: r1.random()
Out[17]: 0.4288890546751146

In [18]: r1.random()
Out[18]: 0.5780913011344704

In [19]: r2 = random.Random(10)

In [20]: r2.randint(0, 1)
Out[20]: 1

In [21]: r2.randint(0, 1)
Out[21]: 0

In [22]: r2.random()
Out[22]: 0.5780913011344704
~~~

Note: The new tests are not for this bug fix.

Author: Xiangrui Meng <meng@databricks.com>

Closes #3010 from mengxr/SPARK-4148 and squashes the following commits:

869ae4b [Xiangrui Meng] move tests tests.py
c1bacd9 [Xiangrui Meng] fix seed distribution and add some tests for rdd.sample
[SPARK-4211][Build] Fixes hive.version in Maven profile hive-0.13.1

instead of `hive.version=0.13.1`.
e.g. mvn -Phive -Phive=0.13.1

Note: `hive.version=0.13.1a` is the default property value. However, when explicitly specifying the `hive-0.13.1` maven profile, the wrong one would be selected.
References:  PR #2685, which resolved a package incompatibility issue with Hive-0.13.1 by introducing a special version Hive-0.13.1a

Author: fi <coderfi@gmail.com>

Closes #3072 from coderfi/master and squashes the following commits:

7ca4b1e [fi] Fixes the `hive-0.13.1` maven profile referencing `hive.version=0.13.1` instead of the Spark compatible `hive.version=0.13.1a` Note: `hive.version=0.13.1a` is the default version. However, when explicitly specifying the `hive-0.13.1` maven profile, the wrong one would be selected. e.g. mvn -Phive -Phive=0.13.1 See PR #2685
[SPARK-4207][SQL] Query which has syntax like 'not like' is not working in Spark SQL

Queries which has 'not like' is not working spark sql.

sql("SELECT * FROM records where value not like 'val%'")
 same query works in Spark HiveQL

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

Closes #3075 from ravipesala/SPARK-4207 and squashes the following commits:

35c11e7 [ravipesala] Supported 'not like' syntax in sql
[SPARK-3594] [PySpark] [SQL] take more rows to infer schema or sampling

This patch will try to infer schema for RDD which has empty value (None, [], {}) in the first row. It will try first 100 rows and merge the types into schema, also merge fields of StructType together. If there is still NullType in schema, then it will show an warning, tell user to try with sampling.

If sampling is presented, it will infer schema from all the rows after sampling.

Also, add samplingRatio for jsonFile() and jsonRDD()

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

Closes #2716 from davies/infer and squashes the following commits:

e678f6d [Davies Liu] Merge branch 'master' of github.com:apache/spark into infer
34b5c63 [Davies Liu] Merge branch 'master' of github.com:apache/spark into infer
567dc60 [Davies Liu] update docs
9767b27 [Davies Liu] Merge branch 'master' into infer
e48d7fb [Davies Liu] fix tests
29e94d5 [Davies Liu] let NullType inherit from PrimitiveType
ee5d524 [Davies Liu] Merge branch 'master' of github.com:apache/spark into infer
540d1d5 [Davies Liu] merge fields for StructType
f93fd84 [Davies Liu] add more tests
3603e00 [Davies Liu] take more rows to infer schema, or infer the schema by sampling the RDD
[SPARK-4202][SQL] Simple DSL support for Scala UDF

This feature is based on an offline discussion with mengxr, hopefully can be useful for the new MLlib pipeline API.

For the following test snippet

```scala
case class KeyValue(key: Int, value: String)
val testData = sc.parallelize(1 to 10).map(i => KeyValue(i, i.toString)).toSchemaRDD
def foo(a: Int, b: String) => a.toString + b
```

the newly introduced DSL enables the following syntax

```scala
import org.apache.spark.sql.catalyst.dsl._
testData.select(Star(None), foo.call('key, 'value) as 'result)
```

which is equivalent to

```scala
testData.registerTempTable("testData")
sqlContext.registerFunction("foo", foo)
sql("SELECT *, foo(key, value) AS result FROM testData")
```

Author: Cheng Lian <lian@databricks.com>

Closes #3067 from liancheng/udf-dsl and squashes the following commits:

f132818 [Cheng Lian] Adds DSL support for Scala UDF
[SPARK-4152] [SQL] Avoid data change in CTAS while table already existed

CREATE TABLE t1 (a String);
CREATE TABLE t1 AS SELECT key FROM src; – throw exception
CREATE TABLE if not exists t1 AS SELECT key FROM src; – expect do nothing, currently it will overwrite the t1, which is incorrect.

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

Closes #3013 from chenghao-intel/ctas_unittest and squashes the following commits:

194113e [Cheng Hao] fix bug in CTAS when table already existed
[SQL] More aggressive defaults

 - Turns on compression for in-memory cached data by default
 - Changes the default parquet compression format back to gzip (we have seen more OOMs with production workloads due to the way Snappy allocates memory)
 - Ups the batch size to 10,000 rows
 - Increases the broadcast threshold to 10mb.
 - Uses our parquet implementation instead of the hive one by default.
 - Cache parquet metadata by default.

Author: Michael Armbrust <michael@databricks.com>

Closes #3064 from marmbrus/fasterDefaults and squashes the following commits:

97ee9f8 [Michael Armbrust] parquet codec docs
e641694 [Michael Armbrust] Remote also
a12866a [Michael Armbrust] Cache metadata.
2d73acc [Michael Armbrust] Update docs defaults.
d63d2d5 [Michael Armbrust] document parquet option
da373f9 [Michael Armbrust] More aggressive defaults
SPARK-4178. Hadoop input metrics ignore bytes read in RecordReader insta...

...ntiation

Author: Sandy Ryza <sandy@cloudera.com>

Closes #3045 from sryza/sandy-spark-4178 and squashes the following commits:

8d2e70e [Sandy Ryza] Kostas's review feedback
e5b27c0 [Sandy Ryza] SPARK-4178. Hadoop input metrics ignore bytes read in RecordReader instantiation
[SQL] Convert arguments to Scala UDFs

Author: Michael Armbrust <michael@databricks.com>

Closes #3077 from marmbrus/udfsWithUdts and squashes the following commits:

34b5f27 [Michael Armbrust] style
504adef [Michael Armbrust] Convert arguments to Scala UDFs
[SPARK-4168][WebUI] web statges number should show correctly when stages are more than 1000

The number of completed stages and failed stages showed on webUI will always be less than 1000. This is really misleading when there are already thousands of stages completed or failed. The number should be correct even when only partial stages listed on the webUI (stage info will be removed if the number is too large).

Author: Zhang, Liye <liye.zhang@intel.com>

Closes #3035 from liyezhang556520/webStageNum and squashes the following commits:

d9e29fb [Zhang, Liye] add detailed comments for variables
4ea8fd1 [Zhang, Liye] change variable name accroding to comments
f4c404d [Zhang, Liye] [SPARK-4168][WebUI] web statges number should show correctly when stages are more than 1000
[SPARK-611] Display executor thread dumps in web UI

This patch allows executor thread dumps to be collected on-demand and viewed in the Spark web UI.

The thread dumps are collected using Thread.getAllStackTraces().  To allow remote thread dumps to be triggered from the web UI, I added a new `ExecutorActor` that runs inside of the Executor actor system and responds to RPCs from the driver.  The driver's mechanism for obtaining a reference to this actor is a little bit hacky: it uses the block manager master actor to determine the host/port of the executor actor systems in order to construct ActorRefs to ExecutorActor.  Unfortunately, I couldn't find a much cleaner way to do this without a big refactoring of the executor -> driver communication.

Screenshots:

![image](https://cloud.githubusercontent.com/assets/50748/4781793/7e7a0776-5cbf-11e4-874d-a91cd04620bd.png)

![image](https://cloud.githubusercontent.com/assets/50748/4781794/8bce76aa-5cbf-11e4-8d13-8477748c9f7e.png)

![image](https://cloud.githubusercontent.com/assets/50748/4781797/bd11a8b8-5cbf-11e4-9ad7-a7459467ec8e.png)

Author: Josh Rosen <joshrosen@databricks.com>

Closes #2944 from JoshRosen/jstack-in-web-ui and squashes the following commits:

3c21a5d [Josh Rosen] Address review comments:
880f7f7 [Josh Rosen] Merge remote-tracking branch 'origin/master' into jstack-in-web-ui
f719266 [Josh Rosen] Merge remote-tracking branch 'origin/master' into jstack-in-web-ui
19707b0 [Josh Rosen] Add one comment.
127a130 [Josh Rosen] Update to use SparkContext.DRIVER_IDENTIFIER
b8e69aa [Josh Rosen] Merge remote-tracking branch 'origin/master' into jstack-in-web-ui
3dfc2d4 [Josh Rosen] Add missing file.
bc1e675 [Josh Rosen] Undo some leftover changes from the earlier approach.
f4ac1c1 [Josh Rosen] Switch to on-demand collection of thread dumps
dfec08b [Josh Rosen] Add option to disable thread dumps in UI.
4c87d7f [Josh Rosen] Use separate RPC for sending thread dumps.
2b8bdf3 [Josh Rosen] Enable thread dumps from the driver when running in non-local mode.
cc3e6b3 [Josh Rosen] Fix test code in DAGSchedulerSuite.
87b8b65 [Josh Rosen] Add new listener event for thread dumps.
8c10216 [Josh Rosen] Add missing file.
0f198ac [Josh Rosen] [SPARK-611] Display executor thread dumps in web UI
[FIX][MLLIB] fix seed in BaggedPointSuite

Saw Jenkins test failures due to random seeds.

jkbradley manishamde

Author: Xiangrui Meng <meng@databricks.com>

Closes #3084 from mengxr/fix-baggedpoint-suite and squashes the following commits:

f735a43 [Xiangrui Meng] fix seed in BaggedPointSuite
[SPARK-4192][SQL] Internal API for Python UDT

Following #2919, this PR adds Python UDT (for internal use only) with tests under "pyspark.tests". Before `SQLContext.applySchema`, we check whether we need to convert user-type instances into SQL recognizable data. In the current implementation, a Python UDT must be paired with a Scala UDT for serialization on the JVM side. A following PR will add VectorUDT in MLlib for both Scala and Python.

marmbrus jkbradley davies

Author: Xiangrui Meng <meng@databricks.com>

Closes #3068 from mengxr/SPARK-4192-sql and squashes the following commits:

acff637 [Xiangrui Meng] merge master
dba5ea7 [Xiangrui Meng] only use pyClass for Python UDT output sqlType as well
2c9d7e4 [Xiangrui Meng] move import to global setup; update needsConversion
7c4a6a9 [Xiangrui Meng] address comments
75223db [Xiangrui Meng] minor update
f740379 [Xiangrui Meng] remove UDT from default imports
e98d9d0 [Xiangrui Meng] fix py style
4e84fce [Xiangrui Meng] remove local hive tests and add more tests
39f19e0 [Xiangrui Meng] add tests
b7f666d [Xiangrui Meng] add Python UDT
[SPARK-3573][MLLIB] Make MLlib's Vector compatible with SQL's SchemaRDD

Register MLlib's Vector as a SQL user-defined type (UDT) in both Scala and Python. With this PR, we can easily map a RDD[LabeledPoint] to a SchemaRDD, and then select columns or save to a Parquet file. Examples in Scala/Python are attached. The Scala code was copied from jkbradley.

~~This PR contains the changes from #3068 . I will rebase after #3068 is merged.~~

marmbrus jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #3070 from mengxr/SPARK-3573 and squashes the following commits:

3a0b6e5 [Xiangrui Meng] organize imports
236f0a0 [Xiangrui Meng] register vector as UDT and provide dataset examples
[SPARK-4163][Core] Add a backward compatibility test for FetchFailed

/cc aarondav

Author: zsxwing <zsxwing@gmail.com>

Closes #3086 from zsxwing/SPARK-4163-back-comp and squashes the following commits:

21cb2a8 [zsxwing] Add a backward compatibility test for FetchFailed
[SPARK-4166][Core] Add a backward compatibility test for ExecutorLostFailure

Author: zsxwing <zsxwing@gmail.com>

Closes #3085 from zsxwing/SPARK-4166-back-comp and squashes the following commits:

89329f4 [zsxwing] Add a backward compatibility test for ExecutorLostFailure
[SPARK-3886] [PySpark] simplify serializer, use AutoBatchedSerializer by default.

This PR simplify serializer, always use batched serializer (AutoBatchedSerializer as default), even batch size is 1.

Author: Davies Liu <davies@databricks.com>

This patch had conflicts when merged, resolved by
Committer: Josh Rosen <joshrosen@databricks.com>

Closes #2920 from davies/fix_autobatch and squashes the following commits:

e544ef9 [Davies Liu] revert unrelated change
6880b14 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
1d557fc [Davies Liu] fix tests
8180907 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
76abdce [Davies Liu] clean up
53fa60b [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
d7ac751 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
2cc2497 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
b4292ce [Davies Liu] fix bug in master
d79744c [Davies Liu] recover hive tests
be37ece [Davies Liu] refactor
eb3938d [Davies Liu] refactor serializer in scala
8d77ef2 [Davies Liu] simplify serializer, use AutoBatchedSerializer by default.