fix big closure with shuffle

Author: Davies Liu

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))