SPARK-1416: PySpark support for SequenceFile and Hadoop InputFormats

So I finally resurrected this PR. It seems the old one against the incubator mirror is no longer available, so I cannot reference it.

This adds initial support for reading Hadoop ```SequenceFile```s, as well as arbitrary Hadoop ```InputFormat```s, in PySpark.

# Overview
The basics are as follows:
1. ```PythonRDD``` object contains the relevant methods, that are in turn invoked by ```SparkContext``` in PySpark
2. The SequenceFile or InputFormat is read on the Scala side and converted from ```Writable``` instances to the relevant Scala classes (in the case of primitives)
3. Pyrolite is used to serialize Java objects. If this fails, the fallback is ```toString```
4. ```PickleSerializer``` on the Python side deserializes.

This works "out the box" for simple ```Writable```s:
* ```Text```
* ```IntWritable```, ```DoubleWritable```, ```FloatWritable```
* ```NullWritable```
* ```BooleanWritable```
* ```BytesWritable```
* ```MapWritable``` 

It also works for simple, "struct-like" classes. Due to the way Pyrolite works, this requires that the classes satisfy the JavaBeans convenstions (i.e. with fields and a no-arg constructor and getters/setters). (Perhaps in future some sugar for case classes and reflection could be added).

I've tested it out with ```ESInputFormat```  as an example and it works very nicely:
```python
conf = {"es.resource" : "index/type" }
rdd = sc.newAPIHadoopRDD("org.elasticsearch.hadoop.mr.EsInputFormat", "org.apache.hadoop.io.NullWritable", "org.elasticsearch.hadoop.mr.LinkedMapWritable", conf=conf)
rdd.first()
```

I suspect for things like HBase/Cassandra it will be a bit trickier to get it to work out the box.

# Some things still outstanding:
1. ~~Requires ```msgpack-python``` and will fail without it. As originally discussed with Josh, add a ```as_strings``` argument that defaults to ```False```, that can be used if ```msgpack-python``` is not available~~
2. ~~I see from https://github.com/apache/spark/pull/363 that Pyrolite is being used there for SerDe between Scala and Python. @ahirreddy @mateiz what is the plan behind this - is Pyrolite preferred? It seems from a cursory glance that adapting the ```msgpack```-based SerDe here to use Pyrolite wouldn't be too hard~~
3. ~~Support the key and value "wrapper" that would allow a Scala/Java function to be plugged in that would transform whatever the key/value Writable class is into something that can be serialized (e.g. convert some custom Writable to a JavaBean or ```java.util.Map``` that can be easily serialized)~~
4. Support ```saveAsSequenceFile``` and ```saveAsHadoopFile``` etc. This would require SerDe in the reverse direction, that can be handled by Pyrolite. Will work on this as a separate PR
Would it be possible to, instead of trying to pickle the first object of each partition, instead just try to take the first element in the RDD and pickle it's key and value. This would be cleaner and also avoid some potentially weird failure conditions if, e.g. for some reason we make different decisions for different partitions. If you check out the SQLContext this is exactly what it does:

https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala#L299