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.
[SPARK-1398] Removed findbugs jsr305 dependency

Should be a painless upgrade, and does offer some significant advantages should we want to leverage FindBugs more during the 1.0 lifecycle. http://findbugs.sourceforge.net/findbugs2.html

Author: Mark Hamstra <markhamstra@gmail.com>

Closes #307 from markhamstra/findbugs and squashes the following commits:

99f2d09 [Mark Hamstra] Removed unnecessary findbugs jsr305 dependency
Spark parquet improvements

A few improvements to the Parquet support for SQL queries:
- Instead of files a ParquetRelation is now backed by a directory, which simplifies importing data from other
  sources
- InsertIntoParquetTable operation now supports switching between overwriting or appending (at least in
  HiveQL)
- tests now use the new API
- Parquet logging can be set to WARNING level (Default)
- Default compression for Parquet files (GZIP, as in parquet-mr)

Author: Andre Schumacher <andre.schumacher@iki.fi>

Closes #195 from AndreSchumacher/spark_parquet_improvements and squashes the following commits:

54df314 [Andre Schumacher] SPARK-1383 [SQL] Improvements to ParquetRelation
[SPARK-1360] Add Timestamp Support for SQL

This PR includes:
1) Add new data type Timestamp
2) Add more data type casting base on Hive's Rule
3) Fix bug missing data type in both parsers (HiveQl & SQLParser).

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

Closes #275 from chenghao-intel/timestamp and squashes the following commits:

df709e5 [Cheng Hao] Move orc_ends_with_nulls to blacklist
24b04b0 [Cheng Hao] Put 3 cases into the black lists(describe_pretty,describe_syntax,lateral_view_outer)
fc512c2 [Cheng Hao] remove the unnecessary data type equality check in data casting
d0d1919 [Cheng Hao] Add more data type for scala reflection
3259808 [Cheng Hao] Add the new Golden files
3823b97 [Cheng Hao] Update the UnitTest cases & add timestamp type for HiveQL
54a0489 [Cheng Hao] fix bug mapping to 0 (which is supposed to be null) when NumberFormatException occurs
9cb505c [Cheng Hao] Fix issues according to PR comments
e529168 [Cheng Hao] Fix bug of converting from String
6fc8100 [Cheng Hao] Update Unit Test & CodeStyle
8a1d4d6 [Cheng Hao] Add DataType for SqlParser
ce4385e [Cheng Hao] Add TimestampType Support
Spark 1162 Implemented takeOrdered in pyspark.

Since python does not have a library for max heap and usual tricks like inverting values etc.. does not work for all cases.

We have our own implementation of max heap.

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #97 from ScrapCodes/SPARK-1162/pyspark-top-takeOrdered2 and squashes the following commits:

35f86ba [Prashant Sharma] code review
2b1124d [Prashant Sharma] fixed tests
e8a08e2 [Prashant Sharma] Code review comments.
49e6ba7 [Prashant Sharma] SPARK-1162 added takeOrdered to pyspark
[SQL] SPARK-1333 First draft of java API

WIP: Some work remains...
 * [x] Hive support
 * [x] Tests
 * [x] Update docs

Feedback welcome!

Author: Michael Armbrust <michael@databricks.com>

Closes #248 from marmbrus/javaSchemaRDD and squashes the following commits:

b393913 [Michael Armbrust] @srowen 's java style suggestions.
f531eb1 [Michael Armbrust] Address matei's comments.
33a1b1a [Michael Armbrust] Ignore JavaHiveSuite.
822f626 [Michael Armbrust] improve docs.
ab91750 [Michael Armbrust] Improve Java SQL API: * Change JavaRow => Row * Add support for querying RDDs of JavaBeans * Docs * Tests * Hive support
0b859c8 [Michael Armbrust] First draft of java API.
[SPARK-1134] Fix and document passing of arguments to IPython

This is based on @dianacarroll's previous pull request https://github.com/apache/spark/pull/227, and @joshrosen's comments on https://github.com/apache/spark/pull/38. Since we do want to allow passing arguments to IPython, this does the following:
* It documents that IPython can't be used with standalone jobs for now. (Later versions of IPython will deal with PYTHONSTARTUP properly and enable this, see https://github.com/ipython/ipython/pull/5226, but no released version has that fix.)
* If you run `pyspark` with `IPYTHON=1`, it passes your command-line arguments to it. This way you can do stuff like `IPYTHON=1 bin/pyspark notebook`.
* The old `IPYTHON_OPTS` remains, but I've removed it from the documentation. This is in case people read an old tutorial that uses it.

This is not a perfect solution and I'd also be okay with keeping things as they are today (ignoring `$@` for IPython and using IPYTHON_OPTS), and only doing the doc change. With this change though, when IPython fixes https://github.com/ipython/ipython/pull/5226, people will immediately be able to do `IPYTHON=1 bin/pyspark myscript.py` to run a standalone script and get all the benefits of running scripts in IPython (presumably better debugging and such). Without it, there will be no way to run scripts in IPython.

@joshrosen you should probably take the final call on this.

Author: Diana Carroll <dcarroll@cloudera.com>

Closes #294 from mateiz/spark-1134 and squashes the following commits:

747bb13 [Diana Carroll] SPARK-1134 bug with ipython prevents non-interactive use with spark; only call ipython if no command line arguments were supplied
[BUILD FIX] Fix compilation of Spark SQL Java API.

The JavaAPI and the Parquet improvements PRs didn't conflict, but broke the build.

Author: Michael Armbrust <michael@databricks.com>

Closes #316 from marmbrus/hotFixJavaApi and squashes the following commits:

0b84c2d [Michael Armbrust] Fix compilation of Spark SQL Java API.
Fix jenkins from giving the green light to builds that don't compile.

 Adding `| grep` swallows the non-zero return code from sbt failures. See [here](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/13735/consoleFull) for a Jenkins run that fails to compile, but still gets a green light.

Note the [BUILD FIX] commit isn't actually part of this PR, but github is out of date.

Author: Michael Armbrust <michael@databricks.com>

Closes #317 from marmbrus/fixJenkins and squashes the following commits:

7c77ff9 [Michael Armbrust] Remove output filter that was swallowing non-zero exit codes for test failures.
Revert "[SPARK-1398] Removed findbugs jsr305 dependency"

This reverts commit 92a86b285f8a4af1bdf577dd4c4ea0fd5ca8d682.
SPARK-1337: Application web UI garbage collects newest stages

Simple fix...

Author: Patrick Wendell <pwendell@gmail.com>

Closes #320 from pwendell/stage-clean-up and squashes the following commits:

29be62e [Patrick Wendell] SPARK-1337: Application web UI garbage collects newest stages instead old ones
SPARK-1350. Always use JAVA_HOME to run executor container JVMs.

Author: Sandy Ryza <sandy@cloudera.com>

Closes #313 from sryza/sandy-spark-1350 and squashes the following commits:

bb6d187 [Sandy Ryza] SPARK-1350. Always use JAVA_HOME to run executor container JVMs.
SPARK-1404: Always upgrade spark-env.sh vars to environment vars

This was broken when spark-env.sh was made idempotent, as the idempotence check is an environment variable, but the spark-env.sh variables may not have been.

Tested in zsh, bash, and sh.

Author: Aaron Davidson <aaron@databricks.com>

Closes #310 from aarondav/SPARK-1404 and squashes the following commits:

c3406a5 [Aaron Davidson] Add extra export in spark-shell
6a0e340 [Aaron Davidson] SPARK-1404: Always upgrade spark-env.sh vars to environment vars
[SPARK-1133] Add whole text files reader in MLlib

Here is a pointer to the former [PR164](https://github.com/apache/spark/pull/164).

I add the pull request for the JIRA issue [SPARK-1133](https://spark-project.atlassian.net/browse/SPARK-1133), which brings a new files reader API in MLlib.

Author: Xusen Yin <yinxusen@gmail.com>

Closes #252 from yinxusen/whole-files-input and squashes the following commits:

7191be6 [Xusen Yin] refine comments
0af3faf [Xusen Yin] add JavaAPI test
01745ee [Xusen Yin] fix deletion error
cc97dca [Xusen Yin] move whole text file API to Spark core
d792cee [Xusen Yin] remove the typo character "+"
6bdf2c2 [Xusen Yin] test for small local file system block size
a1f1e7e [Xusen Yin] add two extra spaces
28cb0fe [Xusen Yin] add whole text files reader