[SPARK-2024] Add saveAsSequenceFile to PySpark

JIRA issue: https://issues.apache.org/jira/browse/SPARK-2024

This PR is a followup to #455 and adds capabilities for saving PySpark RDDs using SequenceFile or any Hadoop OutputFormats.

* Added RDD methods ```saveAsSequenceFile```, ```saveAsHadoopFile``` and ```saveAsHadoopDataset```, for both old and new MapReduce APIs.

* Default converter for converting common data types to Writables. Users may specify custom converters to convert to desired data types.

* No out-of-box support for reading/writing arrays, since ArrayWritable itself doesn't have a no-arg constructor for creating an empty instance upon reading. Users need to provide ArrayWritable subtypes. Custom converters for converting arrays to suitable ArrayWritable subtypes are also needed when writing. When reading, the default converter will convert any custom ArrayWritable subtypes to ```Object[]``` and they get pickled to Python tuples.

* Added HBase and Cassandra output examples to show how custom output formats and converters can be used.

cc MLnick mateiz ahirreddy pwendell

Author: Kan Zhang <kzhang@apache.org>

Closes #1338 from kanzhang/SPARK-2024 and squashes the following commits:

c01e3ef [Kan Zhang] [SPARK-2024] code formatting
6591e37 [Kan Zhang] [SPARK-2024] renaming pickled -> pickledRDD
d998ad6 [Kan Zhang] [SPARK-2024] refectoring to get method params below 10
57a7a5e [Kan Zhang] [SPARK-2024] correcting typo
75ca5bd [Kan Zhang] [SPARK-2024] Better type checking for batch serialized RDD
0bdec55 [Kan Zhang] [SPARK-2024] Refactoring newly added tests
9f39ff4 [Kan Zhang] [SPARK-2024] Adding 2 saveAsHadoopDataset tests
0c134f3 [Kan Zhang] [SPARK-2024] Test refactoring and adding couple unbatched cases
7a176df [Kan Zhang] [SPARK-2024] Add saveAsSequenceFile to PySpark

1 files changed, 23 insertions, 29 deletions

diff --git a/docs/programming-guide.md b/docs/programming-guide.md
index 90c6971301..a88bf27add 100644
--- a/docs/programming-guide.md
+++ b/docs/programming-guide.md
@@ -383,16 +383,16 @@ Apart from text files, Spark's Python API also supports several other data forma
 
 * `RDD.saveAsPickleFile` and `SparkContext.pickleFile` support saving an RDD in a simple format consisting of pickled Python objects. Batching is used on pickle serialization, with default batch size 10.
 
-* Details on reading `SequenceFile` and arbitrary Hadoop `InputFormat` are given below.
-
-### SequenceFile and Hadoop InputFormats
+* SequenceFile and Hadoop Input/Output Formats
 
 **Note** this feature is currently marked ```Experimental``` and is intended for advanced users. It may be replaced in future with read/write support based on SparkSQL, in which case SparkSQL is the preferred approach.
 
-#### 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:
+PySpark SequenceFile support loads an RDD of key-value pairs within Java, converts Writables to base Java types, and pickles the 
+resulting Java objects using [Pyrolite](https://github.com/irmen/Pyrolite/). When saving an RDD of key-value pairs to SequenceFile, 
+PySpark does the reverse. It unpickles Python objects into Java objects and then converts them to Writables. The following 
+Writables are automatically converted:
 
 <table class="table">
 <tr><th>Writable Type</th><th>Python Type</th></tr>
@@ -403,32 +403,30 @@ PySpark SequenceFile support loads an RDD within Java, and pickles the resulting
 <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
+Arrays are not handled out-of-the-box. Users need to specify custom `ArrayWritable` subtypes when reading or writing. When writing, 
+users also need to specify custom converters that convert arrays to custom `ArrayWritable` subtypes. When reading, the default 
+converter will convert custom `ArrayWritable` subtypes to Java `Object[]`, which then get pickled to Python tuples. To get 
+Python `array.array` for arrays of primitive types, users need to specify custom converters.
+
+**Saving and Loading SequenceFiles**
 
-Similarly to text files, SequenceFiles can be loaded by specifying the path. The key and value
+Similarly to text files, SequenceFiles can be saved and loaded by specifying the path. The key and value
 classes can be specified, but for standard Writables this is not required.
 
 {% 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')]
+>>> rdd = sc.parallelize(range(1, 4)).map(lambda x: (x, "a" * x ))
+>>> rdd.saveAsSequenceFile("path/to/file")
+>>> sorted(sc.sequenceFile("path/to/file").collect())
+[(1, u'a'), (2, u'aa'), (3, u'aaa')]
 {% endhighlight %}
 
-#### Loading Other Hadoop InputFormats
+**Saving and Loading Other Hadoop Input/Output Formats**
 
-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
+PySpark can also read any Hadoop InputFormat or write any Hadoop OutputFormat, for both 'new' and 'old' Hadoop MapReduce APIs. 
+If required, a Hadoop configuration can be passed in as a Python dict. Here is an example using the
 Elasticsearch ESInputFormat:
 
 {% highlight python %}
@@ -447,8 +445,7 @@ Note that, if the InputFormat simply depends on a Hadoop configuration and/or in
 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 (such as loading data from Cassandra / HBase) or custom
-classes that don't conform to the JavaBean requirements, then you will first need to 
+If you have custom serialized binary data (such as loading data from Cassandra / HBase), 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``` 
@@ -456,11 +453,8 @@ method. Remember to ensure that this class, along with any dependencies required
 classpath.
 
 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 of using HBase and Cassandra ```InputFormat```.
-
-Future support for writing data out as ```SequenceFileOutputFormat``` and other ```OutputFormats```, 
-is forthcoming.
+the [Converter examples]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/pythonconverters) 
+for examples of using Cassandra / HBase ```InputFormat``` and ```OutputFormat``` with custom converters.
 
 </div>