[SPARK-6886] [PySpark] fix big closure with shuffle

Currently, the created broadcast object will have same life cycle as RDD in Python. For multistage jobs, an PythonRDD will be created in JVM and the RDD in Python may be GCed, then the broadcast will be destroyed in JVM before the PythonRDD.

This PR change to use PythonRDD to track the lifecycle of the broadcast object. It also have a refactor about getNumPartitions() to avoid unnecessary creation of PythonRDD, which could be heavy.

cc JoshRosen

Author: Davies Liu <davies@databricks.com>

Closes #5496 from davies/big_closure and squashes the following commits:

9a0ea4c [Davies Liu] fix big closure with shuffle

python/pyspark/rdd.py

@@ -1197,7 +1197,7 @@ def take(self, num):
         [91, 92, 93]
         """
         items = []
-        totalParts = self._jrdd.partitions().size()
+        totalParts = self.getNumPartitions()
         partsScanned = 0
 
         while len(items) < num and partsScanned < totalParts:
@@ -1260,7 +1260,7 @@ def isEmpty(self):
         >>> sc.parallelize([1]).isEmpty()
         False
         """
-        return self._jrdd.partitions().size() == 0 or len(self.take(1)) == 0
+        return self.getNumPartitions() == 0 or len(self.take(1)) == 0
 
     def saveAsNewAPIHadoopDataset(self, conf, keyConverter=None, valueConverter=None):
         """
@@ -2235,11 +2235,9 @@ def _prepare_for_python_RDD(sc, command, obj=None):
     ser = CloudPickleSerializer()
     pickled_command = ser.dumps((command, sys.version_info[:2]))
     if len(pickled_command) > (1 << 20):  # 1M
+        # The broadcast will have same life cycle as created PythonRDD
         broadcast = sc.broadcast(pickled_command)
         pickled_command = ser.dumps(broadcast)
-        # tracking the life cycle by obj
-        if obj is not None:
-            obj._broadcast = broadcast
     broadcast_vars = ListConverter().convert(
         [x._jbroadcast for x in sc._pickled_broadcast_vars],
         sc._gateway._gateway_client)
@@ -2294,12 +2292,9 @@ def pipeline_func(split, iterator):
         self._jrdd_deserializer = self.ctx.serializer
         self._bypass_serializer = False
         self.partitioner = prev.partitioner if self.preservesPartitioning else None
-        self._broadcast = None
 
-    def __del__(self):
-        if self._broadcast:
-            self._broadcast.unpersist()
-            self._broadcast = None
+    def getNumPartitions(self):
+        return self._prev_jrdd.partitions().size()
 
     @property
     def _jrdd(self):

python/pyspark/tests.py

@@ -550,10 +550,8 @@ def test_large_closure(self):
         data = [float(i) for i in xrange(N)]
         rdd = self.sc.parallelize(range(1), 1).map(lambda x: len(data))
         self.assertEquals(N, rdd.first())
-        self.assertTrue(rdd._broadcast is not None)
-        rdd = self.sc.parallelize(range(1), 1).map(lambda x: 1)
-        self.assertEqual(1, rdd.first())
-        self.assertTrue(rdd._broadcast is None)
+        # regression test for SPARK-6886
+        self.assertEqual(1, rdd.map(lambda x: (x, 1)).groupByKey().count())
 
     def test_zip_with_different_serializers(self):
         a = self.sc.parallelize(range(5))