[SPARK-2258 / 2266] Fix a few worker UI bugs

**SPARK-2258.** Worker UI displays zombie processes if the executor throws an exception before a process is launched. This is because we only inform the Worker of the change if the process is already launched, which in this case it isn't.

**SPARK-2266.** We expose "Some(app-id)" on the log page. This is fairly minor.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1213 from andrewor14/fix-worker-ui and squashes the following commits:

c1223fe [Andrew Or] Fix worker UI bugs
[SPARK-2270] Kryo cannot serialize results returned by asJavaIterable

and thus groupBy/cogroup are broken in Java APIs when Kryo is used).

@pwendell this should be merged into 1.0.1.

Thanks @sorenmacbeth for reporting this & helping out with the fix.

Author: Reynold Xin <rxin@apache.org>

Closes #1206 from rxin/kryo-iterable-2270 and squashes the following commits:

09da0aa [Reynold Xin] Updated the comment.
009bf64 [Reynold Xin] [SPARK-2270] Kryo cannot serialize results returned by asJavaIterable (and thus groupBy/cogroup are broken in Java APIs when Kryo is used).
[SPARK-2204] Launch tasks on the proper executors in mesos fine-grained mode

The scheduler for Mesos in fine-grained mode launches tasks on the wrong executors. `MesosSchedulerBackend.resourceOffers(SchedulerDriver, List[Offer])` is assuming that `TaskSchedulerImpl.resourceOffers(Seq[WorkerOffer])` is returning task lists in the same order as the offers it was passed, but in the current implementation `TaskSchedulerImpl.resourceOffers` shuffles the offers to avoid assigning the tasks always to the same executors. The result is that the tasks are launched on the wrong executors. The jobs are sometimes able to complete, but most of the time they fail. It seems that as soon as something goes wrong with a task for some reason Spark is not able to recover since it's mistaken as to where the tasks are actually running. Also, it seems that the more the cluster is under load the more likely the job is to fail because there's a higher probability that Spark is trying to launch a task on a slave that doesn't actually have enough resources, again because it's using the wrong offers.

The solution is to not assume that the order in which the tasks are returned is the same as the offers, and simply launch the tasks on the executor decided by `TaskSchedulerImpl.resourceOffers`. What I am not sure about is that I considered slaveId and executorId to be the same, which is true at least in my setup, but I don't know if that is always true.

I tested this on top of the 1.0.0 release and it seems to work fine on our cluster.

Author: Sebastien Rainville <sebastien@hopper.com>

Closes #1140 from sebastienrainville/fine-grained-mode-fix-master and squashes the following commits:

a98b0e0 [Sebastien Rainville] Use a HashMap to retrieve the offer indices
d6ffe54 [Sebastien Rainville] Launch tasks on the proper executors in mesos fine-grained mode
[SQL] SPARK-1800 Add broadcast hash join operator & associated hints.

This PR is based off Michael's [PR 734](https://github.com/apache/spark/pull/734) and includes a bunch of cleanups.

Moreover, this PR also
- makes `SparkLogicalPlan` take a `tableName: String`, which facilitates testing.
- moves join-related tests to a single file.

Author: Zongheng Yang <zongheng.y@gmail.com>
Author: Michael Armbrust <michael@databricks.com>

Closes #1163 from concretevitamin/auto-broadcast-hash-join and squashes the following commits:

d0f4991 [Zongheng Yang] Fix bug in broadcast hash join & add test to cover it.
af080d7 [Zongheng Yang] Fix in joinIterators()'s next().
440d277 [Zongheng Yang] Fixes to imports; add back requiredChildDistribution (lost when merging)
208d5f6 [Zongheng Yang] Make LeftSemiJoinHash mix in HashJoin.
ad6c7cc [Zongheng Yang] Minor cleanups.
814b3bf [Zongheng Yang] Merge branch 'master' into auto-broadcast-hash-join
a8a093e [Zongheng Yang] Minor cleanups.
6fd8443 [Zongheng Yang] Cut down size estimation related stuff.
a4267be [Zongheng Yang] Add test for broadcast hash join and related necessary refactorings:
0e64b08 [Zongheng Yang] Scalastyle fix.
91461c2 [Zongheng Yang] Merge branch 'master' into auto-broadcast-hash-join
7c7158b [Zongheng Yang] Prototype of auto conversion to broadcast hash join.
0ad122f [Zongheng Yang] Merge branch 'master' into auto-broadcast-hash-join
3e5d77c [Zongheng Yang] WIP: giant and messy WIP.
a92ed0c [Michael Armbrust] Formatting.
76ca434 [Michael Armbrust] A simple strategy that broadcasts tables only when they are found in a configuration hint.
cf6b381 [Michael Armbrust] Split out generic logic for hash joins and create two concrete physical operators: BroadcastHashJoin and ShuffledHashJoin.
a8420ca [Michael Armbrust] Copy records in executeCollect to avoid issues with mutable rows.
[SPARK-2283][SQL] Reset test environment before running PruningSuite

JIRA issue: [SPARK-2283](https://issues.apache.org/jira/browse/SPARK-2283)

If `PruningSuite` is run right after `HiveCompatibilitySuite`, the first test case fails because `srcpart` table is cached in-memory by `HiveCompatibilitySuite`, but column pruning is not implemented for `InMemoryColumnarTableScan` operator yet.

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

Closes #1221 from liancheng/spark-2283 and squashes the following commits:

dc0b663 [Cheng Lian] SPARK-2283: reset test environment before running PruningSuite
[SPARK-1749] Job cancellation when SchedulerBackend does not implement killTask

This is a fixed up version of #686 (cc @markhamstra @pwendell).  The last commit (the only one I authored) reflects the changes I made from Mark's original patch.

Author: Mark Hamstra <markhamstra@gmail.com>
Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #1219 from kayousterhout/mark-SPARK-1749 and squashes the following commits:

42dfa7e [Kay Ousterhout] Got rid of terrible double-negative name
80b3205 [Kay Ousterhout] Don't notify listeners of job failure if it wasn't successfully cancelled.
d156d33 [Mark Hamstra] Do nothing in no-kill submitTasks
9312baa [Mark Hamstra] code review update
cc353c8 [Mark Hamstra] scalastyle
e61f7f8 [Mark Hamstra] Catch UnsupportedOperationException when DAGScheduler tries to cancel a job on a SchedulerBackend that does not implement killTask
[SPARK-2284][UI] Mark all failed tasks as failures.

Previously only tasks failed with ExceptionFailure reason was marked as failure.

Author: Reynold Xin <rxin@apache.org>

Closes #1224 from rxin/SPARK-2284 and squashes the following commits:

be79dbd [Reynold Xin] [SPARK-2284][UI] Mark all failed tasks as failures.
[SPARK-2172] PySpark cannot import mllib modules in YARN-client mode

Include pyspark/mllib python sources as resources in the mllib.jar.
This way they will be included in the final assembly

Author: Szul, Piotr <Piotr.Szul@csiro.au>

Closes #1223 from piotrszul/branch-1.0 and squashes the following commits:

69d5174 [Szul, Piotr] Removed unsed resource directory src/main/resource from mllib pom
f8c52a0 [Szul, Piotr] [SPARK-2172] PySpark cannot import mllib modules in YARN-client mode Include pyspark/mllib python sources as resources in the jar

(cherry picked from commit fa167194ce1b5898e4d7232346c9f86b2897a722)
Signed-off-by: Reynold Xin <rxin@apache.org>
[SPARK-2254] [SQL] ScalaRefection should mark primitive types as non-nullable.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1193 from ueshin/issues/SPARK-2254 and squashes the following commits:

cfd6088 [Takuya UESHIN] Modify ScalaRefection.schemaFor method to return nullability of Scala Type.
Remove use of spark.worker.instances

spark.worker.instances was added as part of this commit: https://github.com/apache/spark/commit/1617816090e7b20124a512a43860a21232ebf511

My understanding is that SPARK_WORKER_INSTANCES is supported for backwards compatibility,
but spark.worker.instances is never used (SparkSubmit.scala sets spark.executor.instances) so should
not have been added.

@sryza @pwendell @tgravescs LMK if I'm understanding this correctly

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #1214 from kayousterhout/yarn_config and squashes the following commits:

3d7c491 [Kay Ousterhout] Remove use of spark.worker.instances