SPARK-660: Add StorageLevel support in Python

It uses reflection... I am not proud of that fact, but it at least ensures
compatibility (sans refactoring of the StorageLevel stuff).

4 files changed, 45 insertions, 1 deletions

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index ccd3833964..6ca56b3af6 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -28,6 +28,7 @@ import org.apache.spark.broadcast.Broadcast
 import org.apache.spark._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.rdd.PipedRDD
+import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.Utils
 
 
@@ -270,6 +271,16 @@ private[spark] object PythonRDD {
     JavaRDD.fromRDD(sc.sc.parallelize(objs, parallelism))
   }
 
+  /**
+   * Returns the StorageLevel with the given string name.
+   * Throws an exception if the name is not a valid StorageLevel.
+   */
+  def getStorageLevel(name: String) : StorageLevel = {
+    // In Scala, "val MEMORY_ONLY" produces a public getter by the same name.
+    val storageLevelGetter = StorageLevel.getClass().getDeclaredMethod(name)
+    return storageLevelGetter.invoke(StorageLevel).asInstanceOf[StorageLevel]
+  }
+
   def writeIteratorToPickleFile[T](items: java.util.Iterator[T], filename: String) {
     import scala.collection.JavaConverters._
     writeIteratorToPickleFile(items.asScala, filename)
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 8fbf296509..49f9b4610d 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -279,6 +279,20 @@ class SparkContext(object):
         """
         self._jsc.sc().setCheckpointDir(dirName, useExisting)
 
+class StorageLevelReader:
+    """
+    Mimics the Scala StorageLevel by directing all attribute requests
+    (e.g., StorageLevel.DISK_ONLY) to the JVM for reflection.
+    """
+
+    def __init__(self, sc):
+        self.sc = sc
+
+    def __getattr__(self, name):
+        try:
+            return self.sc._jvm.PythonRDD.getStorageLevel(name)
+        except:
+            print "Failed to find StorageLevel:", name
 
 def _test():
     import atexit
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 914118ccdd..332258f5d1 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -70,6 +70,24 @@ class RDD(object):
         self._jrdd.cache()
         return self
 
+    def persist(self, storageLevel):
+        """
+        Set this RDD's storage level to persist its values across operations after the first time
+        it is computed. This can only be used to assign a new storage level if the RDD does not
+        have a storage level set yet.
+        """
+        self.is_cached = True
+        self._jrdd.persist(storageLevel)
+        return self
+
+    def unpersist(self):
+        """
+        Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
+        """
+        self.is_cached = False
+        self._jrdd.unpersist()
+        return self
+
     def checkpoint(self):
         """
         Mark this RDD for checkpointing. It will be saved to a file inside the
diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py
index 54823f8037..9acc176d55 100644
--- a/python/pyspark/shell.py
+++ b/python/pyspark/shell.py
@@ -23,12 +23,13 @@ This file is designed to be launched as a PYTHONSTARTUP script.
 import os
 import platform
 import pyspark
-from pyspark.context import SparkContext
+from pyspark.context import SparkContext, StorageLevelReader
 
 # this is the equivalent of ADD_JARS
 add_files = os.environ.get("ADD_FILES").split(',') if os.environ.get("ADD_FILES") != None else None
 
 sc = SparkContext(os.environ.get("MASTER", "local"), "PySparkShell", pyFiles=add_files)
+StorageLevel = StorageLevelReader(sc)
 
 print """Welcome to
       ____              __