GeoJSON: json vs msgpack vs shapely.wkb vs geobuf

benchmark.py

# Comparing serialization of complex GeoJSON geometries using:
# 
# - standard lib json, marshal, pickle, cPickle
# - umsgpack
# - shapely.wkb
# - geobuf (protobuf)
#
# The test case is a nearly circular polygon with 128 vertices.
#
# Python 2.7 because geobuf isn't possible on Python 3 (because
# protobuf).

import json
import marshal
import cPickle
import pickle
import timeit

import geobuf
from shapely.geometry import mapping, Point, shape
from shapely import wkb
import simplejson
import umsgpack


def time_roundtrip(serializer, statement):
    setup = 'from __main__ import geom, shape, {0}'.format(serializer)
    t = timeit.Timer(statement, setup=setup)
    print("{0} loads/dumps".format(serializer))
    print("%.2f usec/pass" % (1000000 * t.timeit(number=1000)/1000))
    print("")

geom = mapping(Point(0, 0).buffer(1.0, 32))

for serializer, statement in {
        'json': 'json.loads(json.dumps(geom))',
        'marshal': 'marshal.loads(marshal.dumps(geom))',
        'cPickle': 'cPickle.loads(cPickle.dumps(geom))',
        'pickle': 'pickle.loads(pickle.dumps(geom))',
        'umsgpack': 'umsgpack.loads(umsgpack.dumps(geom))',
        'wkb': 'wkb.loads(wkb.dumps(shape(geom)))',
        'geobuf': 'geobuf.decode(geobuf.encode(geom))'}.items():
    time_roundtrip(serializer, statement)

test.sh

$ python benchmark.py
json loads/dumps
386.17 usec/pass

marshal loads/dumps
17.83 usec/pass

cPickle loads/dumps
275.89 usec/pass

pickle loads/dumps
1603.66 usec/pass

umsgpack loads/dumps
1156.33 usec/pass

shapely loads/dumps
413.24 usec/pass

geobuf loads/dumps
2199.46 usec/pass