[SQL] SPARK-1372 Support for caching and uncaching tables in a SQLContext.

This doesn't yet support different databases in Hive (though you can probably workaround this by calling `USE <dbname>`).  However, given the time constraints for 1.0 I think its probably worth including this now and extending the functionality in the next release.

Author: Michael Armbrust <michael@databricks.com>

Closes #282 from marmbrus/cacheTables and squashes the following commits:

83785db [Michael Armbrust] Support for caching and uncaching tables in a SQLContext.
[SPARK-1342] Scala 2.10.4

Just a Scala version increment

Author: Mark Hamstra <markhamstra@gmail.com>

Closes #259 from markhamstra/scala-2.10.4 and squashes the following commits:

fbec547 [Mark Hamstra] [SPARK-1342] Bumped Scala version to 2.10.4
[Spark-1134] only call ipython if no arguments are given; remove IPYTHONOPTS from call

see comments on Pull Request https://github.com/apache/spark/pull/38
(i couldn't figure out how to modify an existing pull request, so I'm hoping I can withdraw that one and replace it with this one.)

Author: Diana Carroll <dcarroll@cloudera.com>

Closes #227 from dianacarroll/spark-1134 and squashes the following commits:

ffe47f2 [Diana Carroll] [spark-1134] remove ipythonopts from ipython command
b673bf7 [Diana Carroll] Merge branch 'master' of github.com:apache/spark
0309cf9 [Diana Carroll] SPARK-1134 bug with ipython prevents non-interactive use with spark; only call ipython if no command line arguments were supplied
Revert "[Spark-1134] only call ipython if no arguments are given; remove IPYTHONOPTS from call"

This reverts commit afb5ea62786e3ca055e247176def3e7ecf0d2c9d.
MLI-1 Decision Trees

Joint work with @hirakendu, @etrain, @atalwalkar and @harsha2010.

Key features:
+ Supports binary classification and regression
+ Supports gini, entropy and variance for information gain calculation
+ Supports both continuous and categorical features

The algorithm has gone through several development iterations over the last few months leading to a highly optimized implementation. Optimizations include:

1. Level-wise training to reduce passes over the entire dataset.
2. Bin-wise split calculation to reduce computation overhead.
3. Aggregation over partitions before combining to reduce communication overhead.

Author: Manish Amde <manish9ue@gmail.com>
Author: manishamde <manish9ue@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>

Closes #79 from manishamde/tree and squashes the following commits:

1e8c704 [Manish Amde] remove numBins field in the Strategy class
7d54b4f [manishamde] Merge pull request #4 from mengxr/dtree
f536ae9 [Xiangrui Meng] another pass on code style
e1dd86f [Manish Amde] implementing code style suggestions
62dc723 [Manish Amde] updating javadoc and converting helper methods to package private to allow unit testing
201702f [Manish Amde] making some more methods private
f963ef5 [Manish Amde] making methods private
c487e6a [manishamde] Merge pull request #1 from mengxr/dtree
24500c5 [Xiangrui Meng] minor style updates
4576b64 [Manish Amde] documentation and for to while loop conversion
ff363a7 [Manish Amde] binary search for bins and while loop for categorical feature bins
632818f [Manish Amde] removing threshold for classification predict method
2116360 [Manish Amde] removing dummy bin calculation for categorical variables
6068356 [Manish Amde] ensuring num bins is always greater than max number of categories
62c2562 [Manish Amde] fixing comment indentation
ad1fc21 [Manish Amde] incorporated mengxr's code style suggestions
d1ef4f6 [Manish Amde] more documentation
794ff4d [Manish Amde] minor improvements to docs and style
eb8fcbe [Manish Amde] minor code style updates
cd2c2b4 [Manish Amde] fixing code style based on feedback
63e786b [Manish Amde] added multiple train methods for java compatability
d3023b3 [Manish Amde] adding more docs for nested methods
84f85d6 [Manish Amde] code documentation
9372779 [Manish Amde] code style: max line lenght <= 100
dd0c0d7 [Manish Amde] minor: some docs
0dd7659 [manishamde] basic doc
5841c28 [Manish Amde] unit tests for categorical features
f067d68 [Manish Amde] minor cleanup
c0e522b [Manish Amde] updated predict and split threshold logic
b09dc98 [Manish Amde] minor refactoring
6b7de78 [Manish Amde] minor refactoring and tests
d504eb1 [Manish Amde] more tests for categorical features
dbb7ac1 [Manish Amde] categorical feature support
6df35b9 [Manish Amde] regression predict logic
53108ed [Manish Amde] fixing index for highest bin
e23c2e5 [Manish Amde] added regression support
c8f6d60 [Manish Amde] adding enum for feature type
b0e3e76 [Manish Amde] adding enum for feature type
154aa77 [Manish Amde] enums for configurations
733d6dd [Manish Amde] fixed tests
02c595c [Manish Amde] added command line parsing
98ec8d5 [Manish Amde] tree building and prediction logic
b0eb866 [Manish Amde] added logic to handle leaf nodes
80e8c66 [Manish Amde] working version of multi-level split calculation
4798aae [Manish Amde] added gain stats class
dad0afc [Manish Amde] decison stump functionality working
03f534c [Manish Amde] some more tests
0012a77 [Manish Amde] basic stump working
8bca1e2 [Manish Amde] additional code for creating intermediate RDD
92cedce [Manish Amde] basic building blocks for intermediate RDD calculation. untested.
cd53eae [Manish Amde] skeletal framework
Remove * from test case golden filename.

@rxin mentioned this might cause issues on windows machines.

Author: Michael Armbrust <michael@databricks.com>

Closes #297 from marmbrus/noStars and squashes the following commits:

263122a [Michael Armbrust] Remove * from test case golden filename.
Renamed stageIdToActiveJob to jobIdToActiveJob.

This data structure was misused and, as a result, later renamed to an incorrect name.

This data structure seems to have gotten into this tangled state as a result of @henrydavidge using the stageID instead of the job Id to index into it and later @andrewor14 renaming the data structure to reflect this misunderstanding.

This patch renames it and removes an incorrect indexing into it.  The incorrect indexing into it meant that the code added by @henrydavidge to warn when a task size is too large (added here https://github.com/apache/spark/commit/57579934f0454f258615c10e69ac2adafc5b9835) was not always executed; this commit fixes that.

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #301 from kayousterhout/fixCancellation and squashes the following commits:

bd3d3a4 [Kay Ousterhout] Renamed stageIdToActiveJob to jobIdToActiveJob.
[SPARK-1385] Use existing code for JSON de/serialization of BlockId

`BlockId.scala` offers a way to reconstruct a BlockId from a string through regex matching. `util/JsonProtocol.scala` duplicates this functionality by explicitly matching on the BlockId type.
With this PR, the de/serialization of BlockIds will go through the first (older) code path.

(Most of the line changes in this PR involve changing `==` to `===` in `JsonProtocolSuite.scala`)

Author: Andrew Or <andrewor14@gmail.com>

Closes #289 from andrewor14/blockid-json and squashes the following commits:

409d226 [Andrew Or] Simplify JSON de/serialization for BlockId
Do not re-use objects in the EdgePartition/EdgeTriplet iterators.

This avoids a silent data corruption issue (https://spark-project.atlassian.net/browse/SPARK-1188) and has no performance impact by my measurements. It also simplifies the code. As far as I can tell the object re-use was nothing but premature optimization.

I did actual benchmarks for all the included changes, and there is no performance difference. I am not sure where to put the benchmarks. Does Spark not have a benchmark suite?

This is an example benchmark I did:

test("benchmark") {
  val builder = new EdgePartitionBuilder[Int]
  for (i <- (1 to 10000000)) {
    builder.add(i.toLong, i.toLong, i)
  }
  val p = builder.toEdgePartition
  p.map(_.attr + 1).iterator.toList
}

It ran for 10 seconds both before and after this change.

Author: Daniel Darabos <darabos.daniel@gmail.com>

Closes #276 from darabos/spark-1188 and squashes the following commits:

574302b [Daniel Darabos] Restore "manual" copying in EdgePartition.map(Iterator). Add comment to discourage novices like myself from trying to simplify the code.
4117a64 [Daniel Darabos] Revert EdgePartitionSuite.
4955697 [Daniel Darabos] Create a copy of the Edge objects in EdgeRDD.compute(). This avoids exposing the object re-use, while still enables the more efficient behavior for internal code.
4ec77f8 [Daniel Darabos] Add comments about object re-use to the affected functions.
2da5e87 [Daniel Darabos] Restore object re-use in EdgePartition.
0182f2b [Daniel Darabos] Do not re-use objects in the EdgePartition/EdgeTriplet iterators. This avoids a silent data corruption issue (SPARK-1188) and has no performance impact in my measurements. It also simplifies the code.
c55f52f [Daniel Darabos] Tests that reproduce the problems from SPARK-1188.
[SPARK-1371][WIP] Compression support for Spark SQL in-memory columnar storage

JIRA issue: [SPARK-1373](https://issues.apache.org/jira/browse/SPARK-1373)

(Although tagged as WIP, this PR is structurally complete. The only things left unimplemented are 3 more compression algorithms: `BooleanBitSet`, `IntDelta` and `LongDelta`, which are trivial to add later in this or another separate PR.)

This PR contains compression support for Spark SQL in-memory columnar storage. Main interfaces include:

*   `CompressionScheme`

    Each `CompressionScheme` represents a concrete compression algorithm, which basically consists of an `Encoder` for compression and a `Decoder` for decompression. Algorithms implemented include:

    * `RunLengthEncoding`
    * `DictionaryEncoding`

    Algorithms to be implemented include:

    * `BooleanBitSet`
    * `IntDelta`
    * `LongDelta`

*   `CompressibleColumnBuilder`

    A stackable `ColumnBuilder` trait used to build byte buffers for compressible columns.  A best `CompressionScheme` that exhibits lowest compression ratio is chosen for each column according to statistical information gathered while elements are appended into the `ColumnBuilder`. However, if no `CompressionScheme` can achieve a compression ratio better than 80%, no compression will be done for this column to save CPU time.

    Memory layout of the final byte buffer is showed below:

    ```
     .--------------------------- Column type ID (4 bytes)
     |   .----------------------- Null count N (4 bytes)
     |   |   .------------------- Null positions (4 x N bytes, empty if null count is zero)
     |   |   |     .------------- Compression scheme ID (4 bytes)
     |   |   |     |   .--------- Compressed non-null elements
     V   V   V     V   V
    +---+---+-----+---+---------+
    |   |   | ... |   | ... ... |
    +---+---+-----+---+---------+
     \-----------/ \-----------/
        header         body
    ```

*   `CompressibleColumnAccessor`

    A stackable `ColumnAccessor` trait used to iterate (possibly) compressed data column.

*   `ColumnStats`

    Used to collect statistical information while loading data into in-memory columnar table. Optimizations like partition pruning rely on this information.

    Strictly speaking, `ColumnStats` related code is not part of the compression support. It's contained in this PR to ensure and validate the row-based API design (which is used to avoid boxing/unboxing cost whenever possible).

A major refactoring change since PR #205 is:

* Refactored all getter/setter methods for primitive types in various places into `ColumnType` classes to remove duplicated code.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #285 from liancheng/memColumnarCompression and squashes the following commits:

ed71bbd [Cheng Lian] Addressed all PR comments by @marmbrus
d3a4fa9 [Cheng Lian] Removed Ordering[T] in ColumnStats for better performance
5034453 [Cheng Lian] Bug fix, more tests, and more refactoring
c298b76 [Cheng Lian] Test suites refactored
2780d6a [Cheng Lian] [WIP] in-memory columnar compression support
211331c [Cheng Lian] WIP: in-memory columnar compression support
85cc59b [Cheng Lian] Refactored ColumnAccessors & ColumnBuilders to remove duplicate code
StopAfter / TopK related changes

1. Renamed StopAfter to Limit to be more consistent with naming in other relational databases.
2. Renamed TopK to TakeOrdered to be more consistent with Spark RDD API.
3. Avoid breaking lineage in Limit.
4. Added a bunch of override's to execution/basicOperators.scala.

@marmbrus @liancheng

Author: Reynold Xin <rxin@apache.org>
Author: Michael Armbrust <michael@databricks.com>

Closes #233 from rxin/limit and squashes the following commits:

13eb12a [Reynold Xin] Merge pull request #1 from marmbrus/limit
92b9727 [Michael Armbrust] More hacks to make Maps serialize with Kryo.
4fc8b4e [Reynold Xin] Merge branch 'master' of github.com:apache/spark into limit
87b7d37 [Reynold Xin] Use the proper serializer in limit.
9b79246 [Reynold Xin] Updated doc for Limit.
47d3327 [Reynold Xin] Copy tuples in Limit before shuffle.
231af3a [Reynold Xin] Limit/TakeOrdered: 1. Renamed StopAfter to Limit to be more consistent with naming in other relational databases. 2. Renamed TopK to TakeOrdered to be more consistent with Spark RDD API. 3. Avoid breaking lineage in Limit. 4. Added a bunch of override's to execution/basicOperators.scala.
[SPARK-1212, Part II] Support sparse data in MLlib

In PR https://github.com/apache/spark/pull/117, we added dense/sparse vector data model and updated KMeans to support sparse input. This PR is to replace all other `Array[Double]` usage by `Vector` in generalized linear models (GLMs) and Naive Bayes. Major changes:

1. `LabeledPoint` becomes `LabeledPoint(Double, Vector)`.
2. Methods that accept `RDD[Array[Double]]` now accept `RDD[Vector]`. We cannot support both in an elegant way because of type erasure.
3. Mark 'createModel' and 'predictPoint' protected because they are not for end users.
4. Add libSVMFile to MLContext.
5. NaiveBayes can accept arbitrary labels (introducing a breaking change to Python's `NaiveBayesModel`).
6. Gradient computation no longer creates temp vectors.
7. Column normalization and centering are removed from Lasso and Ridge because the operation will densify the data. Simple feature transformation can be done before training.

TODO:
1. ~~Use axpy when possible.~~
2. ~~Optimize Naive Bayes.~~

Author: Xiangrui Meng <meng@databricks.com>

Closes #245 from mengxr/vector and squashes the following commits:

eb6e793 [Xiangrui Meng] move libSVMFile to MLUtils and rename to loadLibSVMData
c26c4fc [Xiangrui Meng] update DecisionTree to use RDD[Vector]
11999c7 [Xiangrui Meng] Merge branch 'master' into vector
f7da54b [Xiangrui Meng] add minSplits to libSVMFile
da25e24 [Xiangrui Meng] revert the change to default addIntercept because it might change the behavior of existing code without warning
493f26f [Xiangrui Meng] Merge branch 'master' into vector
7c1bc01 [Xiangrui Meng] add a TODO to NB
b9b7ef7 [Xiangrui Meng] change default value of addIntercept to false
b01df54 [Xiangrui Meng] allow to change or clear threshold in LR and SVM
4addc50 [Xiangrui Meng] merge master
4ca5b1b [Xiangrui Meng] remove normalization from Lasso and update tests
f04fe8a [Xiangrui Meng] remove normalization from RidgeRegression and update tests
d088552 [Xiangrui Meng] use static constructor for MLContext
6f59eed [Xiangrui Meng] update libSVMFile to determine number of features automatically
3432e84 [Xiangrui Meng] update NaiveBayes to support sparse data
0f8759b [Xiangrui Meng] minor updates to NB
b11659c [Xiangrui Meng] style update
78c4671 [Xiangrui Meng] add libSVMFile to MLContext
f0fe616 [Xiangrui Meng] add a test for sparse linear regression
44733e1 [Xiangrui Meng] use in-place gradient computation
e981396 [Xiangrui Meng] use axpy in Updater
db808a1 [Xiangrui Meng] update JavaLR example
befa592 [Xiangrui Meng] passed scala/java tests
75c83a4 [Xiangrui Meng] passed test compile
1859701 [Xiangrui Meng] passed compile
834ada2 [Xiangrui Meng] optimized MLUtils.computeStats update some ml algorithms to use Vector (cont.)
135ab72 [Xiangrui Meng] merge glm
0e57aa4 [Xiangrui Meng] update Lasso and RidgeRegression to parse the weights correctly from GLM mark createModel protected mark predictPoint protected
d7f629f [Xiangrui Meng] fix a bug in GLM when intercept is not used
3f346ba [Xiangrui Meng] update some ml algorithms to use Vector
[SQL] SPARK-1364 Improve datatype and test coverage for ScalaReflection schema inference.

Author: Michael Armbrust <michael@databricks.com>

Closes #293 from marmbrus/reflectTypes and squashes the following commits:

f54e8e8 [Michael Armbrust] Improve datatype and test coverage for ScalaReflection schema inference.