SPARK-1111: URL Validation Throws Error for HDFS URL's

Fixes an error where HDFS URL's cause an exception. Should be merged into master and 0.9.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #625 from pwendell/url-validation and squashes the following commits:

d14bfe3 [Patrick Wendell] SPARK-1111: URL Validation Throws Error for HDFS URL's
MLLIB-25: Implicit ALS runs out of memory for moderately large numbers of features

There's a step in implicit ALS where the matrix `Yt * Y` is computed. It's computed as the sum of matrices; an f x f matrix is created for each of n user/item rows in a partition. In `ALS.scala:214`:

```
        factors.flatMapValues{ case factorArray =>
          factorArray.map{ vector =>
            val x = new DoubleMatrix(vector)
            x.mmul(x.transpose())
          }
        }.reduceByKeyLocally((a, b) => a.addi(b))
         .values
         .reduce((a, b) => a.addi(b))
```

Completely correct, but there's a subtle but quite large memory problem here. map() is going to create all of these matrices in memory at once, when they don't need to ever all exist at the same time.
For example, if a partition has n = 100000 rows, and f = 200, then this intermediate product requires 32GB of heap. The computation will never work unless you can cough up workers with (more than) that much heap.

Fortunately there's a trivial change that fixes it; just add `.view` in there.

Author: Sean Owen <sowen@cloudera.com>

Closes #629 from srowen/ALSMatrixAllocationOptimization and squashes the following commits:

062cda9 [Sean Owen] Update style per review comments
e9a5d63 [Sean Owen] Avoid unnecessary out of memory situation by not simultaneously allocating lots of matrices
[SPARK-1113] External spilling - fix Int.MaxValue hash code collision bug

The original poster of this bug is @guojc, who opened a PR that preceded this one at https://github.com/apache/incubator-spark/pull/612.

ExternalAppendOnlyMap uses key hash code to order the buffer streams from which spilled files are read back into memory. When a buffer stream is empty, the default hash code for that stream is equal to Int.MaxValue. This is, however, a perfectly legitimate candidate for a key hash code. When reading from a spilled map containing such a key, a hash collision may occur, in which case we attempt to read from an empty stream and throw NoSuchElementException.

The fix is to maintain the invariant that empty buffer streams are never added back to the merge queue to be considered. This guarantees that we never read from an empty buffer stream, ever again.

This PR also includes two new tests for hash collisions.

Author: Andrew Or <andrewor14@gmail.com>

Closes #624 from andrewor14/spilling-bug and squashes the following commits:

9e7263d [Andrew Or] Slightly optimize next()
2037ae2 [Andrew Or] Move a few comments around...
cf95942 [Andrew Or] Remove default value of Int.MaxValue for minKeyHash
c11f03b [Andrew Or] Fix Int.MaxValue hash collision bug in ExternalAppendOnlyMap
21c1a39 [Andrew Or] Add hash collision tests to ExternalAppendOnlyMapSuite
SPARK-1117: update accumulator docs

The current doc hints spark doesn't support accumulators of type `Long`, which is wrong.

JIRA: https://spark-project.atlassian.net/browse/SPARK-1117

Author: Xiangrui Meng <meng@databricks.com>

Closes #631 from mengxr/acc and squashes the following commits:

45ecd25 [Xiangrui Meng] update accumulator docs
Fixed minor typo in worker.py

Fixed minor typo in worker.py

Author: jyotiska <jyotiska123@gmail.com>

Closes #630 from jyotiska/pyspark_code and squashes the following commits:

ee44201 [jyotiska] typo fixed in worker.py
doctest updated for mapValues, flatMapValues in rdd.py

Updated doctests for mapValues and flatMapValues in rdd.py

Author: jyotiska <jyotiska123@gmail.com>

Closes #621 from jyotiska/python_spark and squashes the following commits:

716f7cd [jyotiska] doctest updated for mapValues, flatMapValues in rdd.py
[SPARK-1055] fix the SCALA_VERSION and SPARK_VERSION in docker file

As reported in https://spark-project.atlassian.net/browse/SPARK-1055

"The used Spark version in the .../base/Dockerfile is stale on 0.8.1 and should be updated to 0.9.x to match the release."

Author: CodingCat <zhunansjtu@gmail.com>
Author: Nan Zhu <CodingCat@users.noreply.github.com>

Closes #634 from CodingCat/SPARK-1055 and squashes the following commits:

cb7330e [Nan Zhu] Update Dockerfile
adf8259 [CodingCat] fix the SCALA_VERSION and SPARK_VERSION in docker file
Migrate Java code to Scala or move it to src/main/java

These classes can't be migrated:
  StorageLevels: impossible to create static fields in Scala
  JavaSparkContextVarargsWorkaround: incompatible varargs
  JavaAPISuite: should test Java APIs in pure Java (for sanity)

Author: Punya Biswal <pbiswal@palantir.com>

Closes #605 from punya/move-java-sources and squashes the following commits:

25b00b2 [Punya Biswal] Remove redundant type param; reformat
853da46 [Punya Biswal] Use factory method rather than constructor
e5d53d9 [Punya Biswal] Migrate Java code to Scala or move it to src/main/java
[SPARK-1041] remove dead code in start script, remind user to set that in spark-env.sh

the lines in start-master.sh and start-slave.sh no longer work

in ec2, the host name has changed, e.g.

ubuntu@ip-172-31-36-93:~$ hostname
ip-172-31-36-93

also, the URL to fetch public DNS name also changed, e.g.

ubuntu@ip-172-31-36-93:~$ wget -q -O - http://instance-data.ec2.internal/latest/meta-data/public-hostname
ubuntu@ip-172-31-36-93:~$  (returns nothing)

since we have spark-ec2 project, we don't need to have such ec2-specific lines here, instead, user only need to set in spark-env.sh

Author: CodingCat <zhunansjtu@gmail.com>

Closes #588 from CodingCat/deadcode_in_sbin and squashes the following commits:

e4236e0 [CodingCat] remove dead code in start script, remind user set that in spark-env.sh
SPARK-1071: Tidy logging strategy and use of log4j

Prompted by a recent thread on the mailing list, I tried and failed to see if Spark can be made independent of log4j. There are a few cases where control of the underlying logging is pretty useful, and to do that, you have to bind to a specific logger.

Instead I propose some tidying that leaves Spark's use of log4j, but gets rid of warnings and should still enable downstream users to switch. The idea is to pipe everything (except log4j) through SLF4J, and have Spark use SLF4J directly when logging, and where Spark needs to output info (REPL and tests), bind from SLF4J to log4j.

This leaves the same behavior in Spark. It means that downstream users who want to use something except log4j should:

- Exclude dependencies on log4j, slf4j-log4j12 from Spark
- Include dependency on log4j-over-slf4j
- Include dependency on another logger X, and another slf4j-X
- Recreate any log config that Spark does, that is needed, in the other logger's config

That sounds about right.

Here are the key changes:

- Include the jcl-over-slf4j shim everywhere by depending on it in core.
- Exclude dependencies on commons-logging from third-party libraries.
- Include the jul-to-slf4j shim everywhere by depending on it in core.
- Exclude slf4j-* dependencies from third-party libraries to prevent collision or warnings
- Added missing slf4j-log4j12 binding to GraphX, Bagel module tests

And minor/incidental changes:

- Update to SLF4J 1.7.5, which happily matches Hadoop 2’s version and is a recommended update over 1.7.2
- (Remove a duplicate HBase dependency declaration in SparkBuild.scala)
- (Remove a duplicate mockito dependency declaration that was causing warnings and bugging me)

Author: Sean Owen <sowen@cloudera.com>

Closes #570 from srowen/SPARK-1071 and squashes the following commits:

52eac9f [Sean Owen] Add slf4j-over-log4j12 dependency to core (non-test) and remove it from things that depend on core.
77a7fa9 [Sean Owen] SPARK-1071: Tidy logging strategy and use of log4j
SPARK-1124: Fix infinite retries of reduce stage when a map stage failed

In the previous code, if you had a failing map stage and then tried to
run reduce stages on it repeatedly, the first reduce stage would fail
correctly, but the later ones would mistakenly believe that all map
outputs are available and start failing infinitely with fetch failures
from "null".
Fix removal from shuffleToMapStage to search for a key-value pair with
our stage instead of using our shuffleID.
Merge pull request #641 from mateiz/spark-1124-master

SPARK-1124: Fix infinite retries of reduce stage when a map stage failed

In the previous code, if you had a failing map stage and then tried to run reduce stages on it repeatedly, the first reduce stage would fail correctly, but the later ones would mistakenly believe that all map outputs are available and start failing infinitely with fetch failures from "null". See https://spark-project.atlassian.net/browse/SPARK-1124 for an example.

This PR also cleans up code style slightly where there was a variable named "s" and some weird map manipulation.
For outputformats that are Configurable, call setConf before sending data to them.

[SPARK-1108] This allows us to use, e.g. HBase's TableOutputFormat with PairRDDFunctions.saveAsNewAPIHadoopFile, which otherwise would throw NullPointerException because the output table name hasn't been configured.

Note this bug also affects branch-0.9

Author: Bryn Keller <bryn.keller@intel.com>

Closes #638 from xoltar/SPARK-1108 and squashes the following commits:

7e94e7d [Bryn Keller] Import, comment, and format cleanup per code review
7cbcaa1 [Bryn Keller] For outputformats that are Configurable, call setConf before sending data to them. This allows us to use, e.g. HBase TableOutputFormat, which otherwise would throw NullPointerException because the output table name hasn't been configured