Update docs for PySpark input formats

docs/programming-guide.md

@@ -381,32 +381,32 @@ Some notes on reading files with Spark:
 Apart from reading files as a collection of lines,
 `SparkContext.wholeTextFiles` lets you read a directory containing multiple small text files, and returns each of them as (filename, content) pairs. This is in contrast with `textFile`, which would return one record per line in each file.
 
-## SequenceFile and Hadoop InputFormats
+### SequenceFile and Hadoop InputFormats
 
 In addition to reading text files, PySpark supports reading [SequenceFile](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html) 
 and any arbitrary [InputFormat](http://hadoop.apache.org/docs/current/api/org/apache/hadoop/mapred/InputFormat.html).
 
-### Writable Support
+#### Writable Support
 
 PySpark SequenceFile support loads an RDD within Java, and pickles the resulting Java objects using
 [Pyrolite](https://github.com/irmen/Pyrolite/). The following Writables are automatically converted:
 
 <table class="table">
-<tr><th>Writable Type</th><th>Scala Type</th><th>Python Type</th></tr>
-<tr><td>Text</td><td>String</td><td>unicode str</td></tr>
-<tr><td>IntWritable</td><td>Int</td><td>int</td></tr>
-<tr><td>FloatWritable</td><td>Float</td><td>float</td></tr>
-<tr><td>DoubleWritable</td><td>Double</td><td>float</td></tr>
-<tr><td>BooleanWritable</td><td>Boolean</td><td>bool</td></tr>
-<tr><td>BytesWritable</td><td>Array[Byte]</td><td>bytearray</td></tr>
-<tr><td>NullWritable</td><td>null</td><td>None</td></tr>
-<tr><td>ArrayWritable</td><td>Array[T]</td><td>list of primitives, or tuple of objects</td></tr>
-<tr><td>MapWritable</td><td>java.util.Map[K, V]</td><td>dict</td></tr>
-<tr><td>Custom Class</td><td>Custom Class conforming to Java Bean conventions</td>
+<tr><th>Writable Type</th><th>Python Type</th></tr>
+<tr><td>Text</td><td>unicode str</td></tr>
+<tr><td>IntWritable</td><td>int</td></tr>
+<tr><td>FloatWritable</td><td>float</td></tr>
+<tr><td>DoubleWritable</td><td>float</td></tr>
+<tr><td>BooleanWritable</td><td>bool</td></tr>
+<tr><td>BytesWritable</td><td>bytearray</td></tr>
+<tr><td>NullWritable</td><td>None</td></tr>
+<tr><td>ArrayWritable</td><td>list of primitives, or tuple of objects</td></tr>
+<tr><td>MapWritable</td><td>dict</td></tr>
+<tr><td>Custom Class conforming to Java Bean conventions</td>
     <td>dict of public properties (via JavaBean getters and setters) + __class__ for the class type</td></tr>
 </table>
 
-### Loading SequenceFiles
+#### Loading SequenceFiles
 
 Similarly to text files, SequenceFiles can be loaded by specifying the path. The key and value
 classes can be specified, but for standard Writables it should work without requiring this.
@@ -420,10 +420,9 @@ classes can be specified, but for standard Writables it should work without requ
  (3.0, u'cc'),
  (2.0, u'bb'),
  (1.0, u'aa')]
->>> help(sc.sequenceFile) # Show sequencefile documentation
 {% endhighlight %}
 
-### Loading Arbitrary Hadoop InputFormats
+#### Loading Arbitrary Hadoop InputFormats
 
 PySpark can also read any Hadoop InputFormat, for both 'new' and 'old' Hadoop APIs. If required,
 a Hadoop configuration can be passed in as a Python dict. Here is an example using the
@@ -439,23 +438,28 @@ $ SPARK_CLASSPATH=/path/to/elasticsearch-hadoop.jar ./bin/pyspark
  {u'field1': True,
   u'field2': u'Some Text',
   u'field3': 12345})
->>> help(sc.newAPIHadoopRDD) # Show help for new API Hadoop RDD
 {% endhighlight %}
 
 Note that, if the InputFormat simply depends on a Hadoop configuration and/or input path, and
 the key and value classes can easily be converted according to the above table,
 then this approach should work well for such cases.
 
 If you have custom serialized binary data (like pulling data from Cassandra / HBase) or custom
-classes that don't conform to the JavaBean requirements, then you will probably have to first
+classes that don't conform to the JavaBean requirements, then you will first need to 
 transform that data on the Scala/Java side to something which can be handled by Pyrolite's pickler.
+A [Converter](api/scala/index.html#org.apache.spark.api.python.Converter) trait is provided 
+for this. Simply extend this trait and implement your transformation code in the ```convert``` 
+method. The ensure this class is packaged into your Spark job jar and included on the PySpark 
+classpath.
 
-Future support for custom 'converter' functions for keys/values that allows this to be written in Java/Scala,
-and called from Python, as well as support for writing data out as SequenceFileOutputFormat
-and other OutputFormats, is forthcoming.
+See the [Python examples]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python) and 
+the [Converter examples]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/pythonconverters) 
+for examples using HBase and Cassandra.
 
-</div>
+Future support for writing data out as SequenceFileOutputFormat and other OutputFormats, 
+is forthcoming.
 
+</div>
 
 </div>