"""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__", "") != "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)