SPARK-1582 Invoke Thread.interrupt() when cancelling jobs

Sometimes executor threads are blocked waiting for IO or monitors, and the current implementation of job cancellation may never recover these threads. By simply invoking Thread.interrupt() during cancellation, we can often safely unblock the threads and use them for subsequent work.

Note that this feature must remain optional for now because of a bug in HDFS where Thread.interrupt() may cause nodes to be marked as permanently dead (as the InterruptedException is reinterpreted as an IOException during communication with some node).

Author: Aaron Davidson <aaron@databricks.com>

Closes #498 from aarondav/cancel and squashes the following commits:

e52b829 [Aaron Davidson] Don't use job.properties when null
82f78bb [Aaron Davidson] Update DAGSchedulerSuite
b67f472 [Aaron Davidson] Add comment on why interruptOnCancel is in setJobGroup
4cb9fd6 [Aaron Davidson] SPARK-1582 Invoke Thread.interrupt() when cancelling jobs
[SPARK-1540] Add an optional Ordering parameter to PairRDDFunctions.

In https://issues.apache.org/jira/browse/SPARK-1540 we'd like to look at Spark's API to see if we can take advantage of Comparable keys in more places, which will make external spilling more efficient. This PR is a first step towards that that shows how to pass an Ordering when available and still continue functioning otherwise. It does this using a new implicit parameter with a default value of null.

The API is currently only in Scala -- in Java we'd have to add new versions of mapToPair and such that take a Comparator, or a new method to add a "type hint" to an RDD. We can address those later though.

Unfortunately requiring all keys to be Comparable would not work without requiring RDDs in general to contain only Comparable types. The reason is that methods such as distinct() and intersection() do a shuffle, but should be usable on RDDs of any type. So ordering will have to remain an optimization for the types that can be ordered. I think this isn't a horrible outcome though because one of the nice things about Spark's API is that it works on objects of *any* type, without requiring you to specify a schema or implement Writable or stuff like that.

Author: Matei Zaharia <matei@databricks.com>

This patch had conflicts when merged, resolved by
Committer: Reynold Xin <rxin@apache.org>

Closes #487 from mateiz/ordered-keys and squashes the following commits:

bd565f6 [Matei Zaharia] Pass an Ordering to only one version of groupBy because the Scala language spec doesn't allow having an optional parameter on all of them (this was only compiling in Scala 2.10 due to a bug).
4629965 [Matei Zaharia] Add tests for other versions of groupBy
3beae85 [Matei Zaharia] Added a test for implicit orderings
80b7a3b [Matei Zaharia] Add an optional Ordering parameter to PairRDDFunctions.