use broadcast automatically for large closure

Author: davies

python/pyspark/rdd.py

@@ -2061,8 +2061,12 @@ def _jrdd(self):
             self._jrdd_deserializer = NoOpSerializer()
         command = (self.func, self._prev_jrdd_deserializer,
                    self._jrdd_deserializer)
+        # the serialized command will be compressed by broadcast
         ser = CloudPickleSerializer()
         pickled_command = ser.dumps(command)
+        if pickled_command > (1 << 20):  # 1M
+            broadcast = self.ctx.broadcast(pickled_command)
+            pickled_command = ser.dumps(broadcast)
         broadcast_vars = ListConverter().convert(
             [x._jbroadcast for x in self.ctx._pickled_broadcast_vars],
             self.ctx._gateway._gateway_client)

python/pyspark/sql.py

@@ -27,7 +27,7 @@
 from array import array
 from operator import itemgetter
 
-from pyspark.rdd import RDD, PipelinedRDD
+from pyspark.rdd import RDD
 from pyspark.serializers import BatchedSerializer, PickleSerializer, CloudPickleSerializer
 from pyspark.storagelevel import StorageLevel
 
@@ -974,7 +974,11 @@ def registerFunction(self, name, f, returnType=StringType()):
         command = (func,
                    BatchedSerializer(PickleSerializer(), 1024),
                    BatchedSerializer(PickleSerializer(), 1024))
-        pickled_command = CloudPickleSerializer().dumps(command)
+        ser = CloudPickleSerializer()
+        pickled_command = ser.dumps(command)
+        if pickled_command > (1 << 20):  # 1M
+            broadcast = self._sc.broadcast(pickled_command)
+            pickled_command = ser.dumps(broadcast)
         broadcast_vars = ListConverter().convert(
             [x._jbroadcast for x in self._sc._pickled_broadcast_vars],
             self._sc._gateway._gateway_client)

python/pyspark/tests.py

@@ -434,6 +434,12 @@ def test_large_broadcast(self):
         m = self.sc.parallelize(range(1), 1).map(lambda x: len(bdata.value)).sum()
         self.assertEquals(N, m)
 
+    def test_large_closure(self):
+        N = 1000000
+        data = [float(i) for i in xrange(N)]
+        m = self.sc.parallelize(range(1), 1).map(lambda x: len(data)).sum()
+        self.assertEquals(N, m)
+
     def test_zip_with_different_serializers(self):
         a = self.sc.parallelize(range(5))
         b = self.sc.parallelize(range(100, 105))

python/pyspark/worker.py

@@ -81,6 +81,8 @@ def main(infile, outfile):
 
         _accumulatorRegistry.clear()
         command = pickleSer._read_with_length(infile)
+        if isinstance(command, Broadcast):
+            command = pickleSer.loads(command.value)
         (func, deserializer, serializer) = command
         init_time = time.time()
         iterator = deserializer.load_stream(infile)