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authorgatorsmile <gatorsmile@gmail.com>2015-12-18 20:06:05 -0800
committerDavies Liu <davies.liu@gmail.com>2015-12-18 20:06:05 -0800
commit499ac3e69a102f9b10a1d7e14382fa191516f7b5 (patch)
treedcb84cda0ddb75094a39946dfd09f0ed29dd058c /python/pyspark/streaming/flume.py
parenta78a91f4d7239c14bd5d0b18cdc87d55594a8d8a (diff)
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[SPARK-12091] [PYSPARK] Deprecate the JAVA-specific deserialized storage levels
The current default storage level of Python persist API is MEMORY_ONLY_SER. This is different from the default level MEMORY_ONLY in the official document and RDD APIs. davies Is this inconsistency intentional? Thanks! Updates: Since the data is always serialized on the Python side, the storage levels of JAVA-specific deserialization are not removed, such as MEMORY_ONLY. Updates: Based on the reviewers' feedback. In Python, stored objects will always be serialized with the [Pickle](https://docs.python.org/2/library/pickle.html) library, so it does not matter whether you choose a serialized level. The available storage levels in Python include `MEMORY_ONLY`, `MEMORY_ONLY_2`, `MEMORY_AND_DISK`, `MEMORY_AND_DISK_2`, `DISK_ONLY`, `DISK_ONLY_2` and `OFF_HEAP`. Author: gatorsmile <gatorsmile@gmail.com> Closes #10092 from gatorsmile/persistStorageLevel.
Diffstat (limited to 'python/pyspark/streaming/flume.py')
-rw-r--r--python/pyspark/streaming/flume.py4
1 files changed, 2 insertions, 2 deletions
diff --git a/python/pyspark/streaming/flume.py b/python/pyspark/streaming/flume.py
index b3d1905365..b1fff0a5c7 100644
--- a/python/pyspark/streaming/flume.py
+++ b/python/pyspark/streaming/flume.py
@@ -40,7 +40,7 @@ class FlumeUtils(object):
@staticmethod
def createStream(ssc, hostname, port,
- storageLevel=StorageLevel.MEMORY_AND_DISK_SER_2,
+ storageLevel=StorageLevel.MEMORY_AND_DISK_2,
enableDecompression=False,
bodyDecoder=utf8_decoder):
"""
@@ -70,7 +70,7 @@ class FlumeUtils(object):
@staticmethod
def createPollingStream(ssc, addresses,
- storageLevel=StorageLevel.MEMORY_AND_DISK_SER_2,
+ storageLevel=StorageLevel.MEMORY_AND_DISK_2,
maxBatchSize=1000,
parallelism=5,
bodyDecoder=utf8_decoder):