[SPARK-1065] [PySpark] improve supporting for large broadcast

Passing large object by py4j is very slow (cost much memory), so pass broadcast objects via files (similar to parallelize()).

Add an option to keep object in driver (it's False by default) to save memory in driver.

Author: Davies Liu <davies.liu@gmail.com>

Closes #1912 from davies/broadcast and squashes the following commits:

e06df4a [Davies Liu] load broadcast from disk in driver automatically
db3f232 [Davies Liu] fix serialization of accumulator
631a827 [Davies Liu] Merge branch 'master' into broadcast
c7baa8c [Davies Liu] compress serrialized broadcast and command
9a7161f [Davies Liu] fix doc tests
e93cf4b [Davies Liu] address comments: add test
6226189 [Davies Liu] improve large broadcast

1 files changed, 7 insertions, 0 deletions

diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 22b51110ed..f1fece998c 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -323,6 +323,13 @@ class TestRDDFunctions(PySparkTestCase):
         theDoes = self.sc.parallelize([jon, jane])
         self.assertEquals([jon, jane], theDoes.collect())
 
+    def test_large_broadcast(self):
+        N = 100000
+        data = [[float(i) for i in range(300)] for i in range(N)]
+        bdata = self.sc.broadcast(data)  # 270MB
+        m = self.sc.parallelize(range(1), 1).map(lambda x: len(bdata.value)).sum()
+        self.assertEquals(N, m)
+
 
 class TestIO(PySparkTestCase):