diff --git a/docs/programming-guide.md b/docs/programming-guide.md
index 7d77e640d0e4b..3e333876bf85f 100644
--- a/docs/programming-guide.md
+++ b/docs/programming-guide.md
@@ -378,9 +378,82 @@ Some notes on reading files with Spark:
 
 * The `textFile` method also takes an optional second argument for controlling the number of slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices than blocks.
 
-Apart reading files as a collection of lines,
+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
+
+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
+
+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>
+    <td>dict of public properties (via JavaBean getters and setters) + __class__ for the class type</td></tr>
+</table>
+
+### 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.
+
+{% highlight python %}
+>>> rdd = sc.sequenceFile("path/to/sequencefile/of/doubles")
+>>> rdd.collect()         # this example has DoubleWritable keys and Text values
+[(1.0, u'aa'),
+ (2.0, u'bb'),
+ (2.0, u'aa'),
+ (3.0, u'cc'),
+ (2.0, u'bb'),
+ (1.0, u'aa')]
+>>> help(sc.sequenceFile) # Show sequencefile documentation
+{% endhighlight %}
+
+### 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
+Elasticsearch ESInputFormat:
+
+{% highlight python %}
+$ SPARK_CLASSPATH=/path/to/elasticsearch-hadoop.jar ./bin/pyspark
+>>> conf = {"es.resource" : "index/type"}   # assume Elasticsearch is running on localhost defaults
+>>> rdd = sc.newAPIHadoopRDD("org.elasticsearch.hadoop.mr.EsInputFormat",\
+    "org.apache.hadoop.io.NullWritable", "org.elasticsearch.hadoop.mr.LinkedMapWritable", conf=conf)
+>>> rdd.first()         # the result is a MapWritable that is converted to a Python dict
+(u'Elasticsearch ID',
+ {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
+transform that data on the Scala/Java side to something which can be handled by Pyrolite's pickler.
+
+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.
+
 </div>