Fast JSON serialization (and comparison with Pickle) for cluster computing in Python?
- by user248237
I have a set of data points, each described by a dictionary. The processing of each data point is independent and I submit each one as a separate job to a cluster. Each data point has a unique name, and my cluster submission wrapper simply calls a script that takes a data point's name and a file describing all the data points. That script then accesses the data point from the file and performs the computation.
Since each job has to load the set of all points only to retrieve the point to be run, I wanted to optimize this step by serializing the file describing the set of points into an easily retrievable format.
I tried using JSONpickle, using the following method, to serialize a dictionary describing all the data points to file:
def json_serialize(obj, filename, use_jsonpickle=True):
f = open(filename, 'w')
if use_jsonpickle:
import jsonpickle
json_obj = jsonpickle.encode(obj)
f.write(json_obj)
else:
simplejson.dump(obj, f, indent=1)
f.close()
The dictionary contains very simple objects (lists, strings, floats, etc.) and has a total of 54,000 keys. The json file is ~20 Megabytes in size.
It takes ~20 seconds to load this file into memory, which seems very slow to me. I switched to using pickle with the same exact object, and found that it generates a file that's about 7.8 megabytes in size, and can be loaded in ~1-2 seconds. This is a significant improvement, but it still seems like loading of a small object (less than 100,000 entries) should be faster. Aside from that, pickle is not human readable, which was the big advantage of JSON for me.
Is there a way to use JSON to get similar or better speed ups? If not, do you have other ideas on structuring this?
(Is the right solution to simply "slice" the file describing each event into a separate file and pass that on to the script that runs a data point in a cluster job? It seems like that could lead to a proliferation of files).
thanks.
