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
Properly cleanup DAGScheduler on job cancellation.

Previously, when jobs were cancelled, not all of the state in the
DAGScheduler was cleaned up, leading to a slow memory leak in the
DAGScheduler.  As we expose easier ways ot cancel jobs, it's more
important to fix these issues.

This commit adds 3 tests.  “run shuffle with map stage failure” is
a new test to more thoroughly test this functionality, and passes on
both the old and new versions of the code.  “trivial job
cancellation” fails on the old code because all state wasn’t cleaned
up correctly when jobs were cancelled (we didn’t remove the job from
resultStageToJob).  “failure of stage used by two jobs” fails on the
old code because taskScheduler.cancelTasks wasn’t called for one of
the stages (see test comments).
Mark review comments