[SPARK-4548] []SPARK-4517] improve performance of python broadcast

Re-implement the Python broadcast using file:

1) serialize the python object using cPickle, write into disks.
2) Create a wrapper in JVM (for the dumped file), it read data from during serialization
3) Using TorrentBroadcast or HttpBroadcast to transfer the data (compressed) into executors
4) During deserialization, writing the data into disk.
5) Passing the path into Python worker, read data from disk and unpickle it into python object, until the first access.

It fixes the performance regression introduced in #2659, has similar performance as 1.1, but support object larger than 2G, also improve the memory efficiency (only one compressed copy in driver and executor).

Testing with a 500M broadcast and 4 tasks (excluding the benefit from reused worker in 1.2):

         name |   1.1   | 1.2 with this patch |  improvement
---------|--------|---------|--------
      python-broadcast-w-bytes  |	25.20  |	9.33   |	170.13% |
        python-broadcast-w-set	  |     4.13	   |    4.50  |	-8.35%  |

Testing with 100 tasks (16 CPUs):

         name |   1.1   | 1.2 with this patch |  improvement
---------|--------|---------|--------
     python-broadcast-w-bytes	| 38.16	| 8.40	 | 353.98%
        python-broadcast-w-set	| 23.29	| 9.59 |	142.80%

Author: Davies Liu <davies@databricks.com>

Closes #3417 from davies/pybroadcast and squashes the following commits:

50a58e0 [Davies Liu] address comments
b98de1d [Davies Liu] disable gc while unpickle
e5ee6b9 [Davies Liu] support large string
09303b8 [Davies Liu] read all data into memory
dde02dd [Davies Liu] improve performance of python broadcast

1 files changed, 2 insertions, 10 deletions

diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index ec67ec8d0f..ed7351d60c 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -29,7 +29,7 @@ from pyspark.conf import SparkConf
 from pyspark.files import SparkFiles
 from pyspark.java_gateway import launch_gateway
 from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer, \
-    PairDeserializer, AutoBatchedSerializer, NoOpSerializer, LargeObjectSerializer
+    PairDeserializer, AutoBatchedSerializer, NoOpSerializer
 from pyspark.storagelevel import StorageLevel
 from pyspark.rdd import RDD
 from pyspark.traceback_utils import CallSite, first_spark_call
@@ -624,15 +624,7 @@ class SparkContext(object):
         object for reading it in distributed functions. The variable will
         be sent to each cluster only once.
         """
-        ser = LargeObjectSerializer()
-
-        # pass large object by py4j is very slow and need much memory
-        tempFile = NamedTemporaryFile(delete=False, dir=self._temp_dir)
-        ser.dump_stream([value], tempFile)
-        tempFile.close()
-        jbroadcast = self._jvm.PythonRDD.readBroadcastFromFile(self._jsc, tempFile.name)
-        return Broadcast(jbroadcast.id(), None, jbroadcast,
-                         self._pickled_broadcast_vars, tempFile.name)
+        return Broadcast(self, value, self._pickled_broadcast_vars)
 
     def accumulator(self, value, accum_param=None):
         """