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"""Script for testing the performance of pickling/unpickling.
This will pickle/unpickle several real world-representative objects a few
thousand times. The methodology below was chosen for was chosen to be similar
to real-world scenarios which operate on single objects at a time. Note that if
we did something like
pickle.dumps([dict(some_dict) for _ in range(10000)])
this isn't equivalent to dumping the dict 10000 times: pickle uses a
highly-efficient encoding for the n-1 following copies.
"""
import sys
import datetime
import random
import sys
__author__ = "collinwinter@google.com (Collin Winter)"
DICT = {
"ads_flags": 0,
"age": 18,
"birthday": datetime.date(1980, 5, 7),
"bulletin_count": 0,
"comment_count": 0,
"country": "BR",
"encrypted_id": "G9urXXAJwjE",
"favorite_count": 9,
"first_name": "",
"flags": 412317970704,
"friend_count": 0,
"gender": "m",
"gender_for_display": "Male",
"id": 302935349,
"is_custom_profile_icon": 0,
"last_name": "",
"locale_preference": "pt_BR",
"member": 0,
"tags": ["a", "b", "c", "d", "e", "f", "g"],
"profile_foo_id": 827119638,
"secure_encrypted_id": "Z_xxx2dYx3t4YAdnmfgyKw",
"session_number": 2,
"signup_id": "201-19225-223",
"status": "A",
"theme": 1,
"time_created": 1225237014,
"time_updated": 1233134493,
"unread_message_count": 0,
"user_group": "0",
"username": "collinwinter",
"play_count": 9,
"view_count": 7,
"zip": "",
}
TUPLE = (
[
265867233,
265868503,
265252341,
265243910,
265879514,
266219766,
266021701,
265843726,
265592821,
265246784,
265853180,
45526486,
265463699,
265848143,
265863062,
265392591,
265877490,
265823665,
265828884,
265753032,
],
60,
)
def mutate_dict(orig_dict, random_source):
new_dict = dict(orig_dict)
for key, value in new_dict.items():
rand_val = random_source.random() * sys.maxsize
if isinstance(key, (int, bytes, str)):
new_dict[key] = type(key)(rand_val)
return new_dict
random_source = random.Random(5) # Fixed seed.
DICT_GROUP = [mutate_dict(DICT, random_source) for _ in range(3)]
def bench_pickle(loops, pickle, protocol):
range_it = range(loops)
# micro-optimization: use fast local variables
dumps = pickle.dumps
objs = (DICT, TUPLE, DICT_GROUP)
for _ in range_it:
for obj in objs:
# 20 dumps
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
def bench_unpickle(loops, pickle, protocol):
pickled_dict = pickle.dumps(DICT, protocol)
pickled_tuple = pickle.dumps(TUPLE, protocol)
pickled_dict_group = pickle.dumps(DICT_GROUP, protocol)
range_it = range(loops)
# micro-optimization: use fast local variables
loads = pickle.loads
objs = (pickled_dict, pickled_tuple, pickled_dict_group)
for _ in range_it:
for obj in objs:
# 20 loads dict
loads(obj)
loads(obj)
loads(obj)
loads(obj)
loads(obj)
loads(obj)
loads(obj)
loads(obj)
loads(obj)
loads(obj)
loads(obj)
loads(obj)
loads(obj)
loads(obj)
loads(obj)
loads(obj)
loads(obj)
loads(obj)
loads(obj)
loads(obj)
LIST = [[list(range(10)), list(range(10))] for _ in range(10)]
def bench_pickle_list(loops, pickle, protocol):
range_it = range(loops)
# micro-optimization: use fast local variables
dumps = pickle.dumps
obj = LIST
protocol = protocol
for _ in range_it:
# 10 dumps list
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
dumps(obj, protocol)
def bench_unpickle_list(loops, pickle, protocol):
pickled_list = pickle.dumps(LIST, protocol)
range_it = range(loops)
# micro-optimization: use fast local variables
loads = pickle.loads
for _ in range_it:
# 10 loads list
loads(pickled_list)
loads(pickled_list)
loads(pickled_list)
loads(pickled_list)
loads(pickled_list)
loads(pickled_list)
loads(pickled_list)
loads(pickled_list)
loads(pickled_list)
loads(pickled_list)
MICRO_DICT = dict((key, dict.fromkeys(range(10))) for key in range(100))
def bench_pickle_dict(loops, pickle, protocol):
range_it = range(loops)
# micro-optimization: use fast local variables
protocol = protocol
obj = MICRO_DICT
for _ in range_it:
# 5 dumps dict
pickle.dumps(obj, protocol)
pickle.dumps(obj, protocol)
pickle.dumps(obj, protocol)
pickle.dumps(obj, protocol)
pickle.dumps(obj, protocol)
BENCHMARKS = {
# 20 inner-loops: don't count the 3 pickled objects
"pickle": (bench_pickle, 20),
# 20 inner-loops: don't count the 3 unpickled objects
"unpickle": (bench_unpickle, 20),
"pickle_list": (bench_pickle_list, 10),
"unpickle_list": (bench_unpickle_list, 10),
"pickle_dict": (bench_pickle_dict, 5),
}
def is_accelerated_module(module):
return getattr(module.Pickler, "__module__", "<jython>") != "pickle"
def add_cmdline_args(cmd, args):
if args.pure_python:
cmd.append("--pure-python")
cmd.extend(("--protocol", str(args.protocol)))
cmd.append(args.benchmark)
def run_benchmark_pure_python(benchmark):
benchmarks = sorted(BENCHMARKS)
benchmark, inner_loops = BENCHMARKS[benchmark]
sys.modules["_pickle"] = None
import pickle
if is_accelerated_module(pickle):
raise RuntimeError("Unexpected C accelerators for pickle")
benchmark(1, pickle, pickle.HIGHEST_PROTOCOL)
def run_benchmark_c(benchmark):
benchmarks = sorted(BENCHMARKS)
benchmark, inner_loops = BENCHMARKS[benchmark]
import pickle
if not is_accelerated_module(pickle):
raise RuntimeError("Missing C accelerators for pickle")
benchmark(1, pickle, pickle.HIGHEST_PROTOCOL)
if __name__ == "__main__":
bench = sys.argv[1]
if sys.argv[2] == "--pure-python":
run_benchmark_pure_python(bench)
else:
run_benchmark_c(bench)
|
