[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, 64 insertions, 31 deletions

diff --git a/python/pyspark/broadcast.py b/python/pyspark/broadcast.py
index 01cac3c72c..6b8a8b256a 100644
--- a/python/pyspark/broadcast.py
+++ b/python/pyspark/broadcast.py
@@ -15,21 +15,10 @@
 # limitations under the License.
 #
 
-"""
->>> from pyspark.context import SparkContext
->>> sc = SparkContext('local', 'test')
->>> b = sc.broadcast([1, 2, 3, 4, 5])
->>> b.value
-[1, 2, 3, 4, 5]
->>> sc.parallelize([0, 0]).flatMap(lambda x: b.value).collect()
-[1, 2, 3, 4, 5, 1, 2, 3, 4, 5]
->>> b.unpersist()
-
->>> large_broadcast = sc.broadcast(list(range(10000)))
-"""
 import os
-
-from pyspark.serializers import LargeObjectSerializer
+import cPickle
+import gc
+from tempfile import NamedTemporaryFile
 
 
 __all__ = ['Broadcast']
@@ -49,44 +38,88 @@ def _from_id(bid):
 class Broadcast(object):
 
     """
-    A broadcast variable created with
-    L{SparkContext.broadcast()<pyspark.context.SparkContext.broadcast>}.
+    A broadcast variable created with L{SparkContext.broadcast()}.
     Access its value through C{.value}.
+
+    Examples:
+
+    >>> from pyspark.context import SparkContext
+    >>> sc = SparkContext('local', 'test')
+    >>> b = sc.broadcast([1, 2, 3, 4, 5])
+    >>> b.value
+    [1, 2, 3, 4, 5]
+    >>> sc.parallelize([0, 0]).flatMap(lambda x: b.value).collect()
+    [1, 2, 3, 4, 5, 1, 2, 3, 4, 5]
+    >>> b.unpersist()
+
+    >>> large_broadcast = sc.broadcast(range(10000))
     """
 
-    def __init__(self, bid, value, java_broadcast=None,
-                 pickle_registry=None, path=None):
+    def __init__(self, sc=None, value=None, pickle_registry=None, path=None):
         """
-        Should not be called directly by users -- use
-        L{SparkContext.broadcast()<pyspark.context.SparkContext.broadcast>}
+        Should not be called directly by users -- use L{SparkContext.broadcast()}
         instead.
         """
-        self.bid = bid
-        if path is None:
-            self._value = value
-        self._jbroadcast = java_broadcast
-        self._pickle_registry = pickle_registry
-        self.path = path
+        if sc is not None:
+            f = NamedTemporaryFile(delete=False, dir=sc._temp_dir)
+            self._path = self.dump(value, f)
+            self._jbroadcast = sc._jvm.PythonRDD.readBroadcastFromFile(sc._jsc, self._path)
+            self._pickle_registry = pickle_registry
+        else:
+            self._jbroadcast = None
+            self._path = path
+
+    def dump(self, value, f):
+        if isinstance(value, basestring):
+            if isinstance(value, unicode):
+                f.write('U')
+                value = value.encode('utf8')
+            else:
+                f.write('S')
+            f.write(value)
+        else:
+            f.write('P')
+            cPickle.dump(value, f, 2)
+        f.close()
+        return f.name
+
+    def load(self, path):
+        with open(path, 'rb', 1 << 20) as f:
+            flag = f.read(1)
+            data = f.read()
+            if flag == 'P':
+                # cPickle.loads() may create lots of objects, disable GC
+                # temporary for better performance
+                gc.disable()
+                try:
+                    return cPickle.loads(data)
+                finally:
+                    gc.enable()
+            else:
+                return data.decode('utf8') if flag == 'U' else data
 
     @property
     def value(self):
         """ Return the broadcasted value
         """
-        if not hasattr(self, "_value") and self.path is not None:
-            ser = LargeObjectSerializer()
-            self._value = ser.load_stream(open(self.path)).next()
+        if not hasattr(self, "_value") and self._path is not None:
+            self._value = self.load(self._path)
         return self._value
 
     def unpersist(self, blocking=False):
         """
         Delete cached copies of this broadcast on the executors.
         """
+        if self._jbroadcast is None:
+            raise Exception("Broadcast can only be unpersisted in driver")
         self._jbroadcast.unpersist(blocking)
-        os.unlink(self.path)
+        os.unlink(self._path)
 
     def __reduce__(self):
+        if self._jbroadcast is None:
+            raise Exception("Broadcast can only be serialized in driver")
         self._pickle_registry.add(self)
-        return (_from_id, (self.bid, ))
+        return _from_id, (self._jbroadcast.id(),)
 
 
 if __name__ == "__main__":