mirror of
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133 lines
4.0 KiB
Python
133 lines
4.0 KiB
Python
"""
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The definitions of the measurements we want to take
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"""
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import json
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import scriptconfig as scfg
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import ubelt as ub
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from json_benchmarks import libraries
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class MeasurementConfig(scfg.Config):
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default = {
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"disable": scfg.Value(
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[],
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choices=libraries.KNOWN_MODNAMES,
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help=ub.paragraph(
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"""
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Remove specified libraries from the benchmarks
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"""
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),
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),
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"factor": scfg.Value(
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1.0,
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help=ub.paragraph(
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"""
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Specify as a fraction to speed up benchmarks for development /
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testing
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"""
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),
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),
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"cache_dir": scfg.Value(
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None,
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help=ub.paragraph(
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"""
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Location for benchmark cache.
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Defaults to $XDG_CACHE/ujson/benchmark_results/
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"""
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),
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),
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}
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def normalize(self):
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dpath = self["cache_dir"]
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if dpath is None:
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dpath = ub.Path.appdir("ujson/benchmark_results")
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dpath = ub.Path(dpath)
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self["cache_dir"] = dpath
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def benchmark_json():
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from json_benchmarks import benchmarker, datagen, libraries
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json_impls = libraries.available_json_impls()
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data_lut = datagen.json_test_data_generators()
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# These are the parameters that we benchmark over
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common_basis = {
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"impl": list(json_impls.keys()),
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"func": ["dumps", "loads"],
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}
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sized_basis = {
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"input": [
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"Array with doubles",
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"Array with UTF-8 strings",
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# 'Medium complex object',
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"Array with True values",
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"Array of Dict[str, int]",
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# 'Dict of List[Dict[str, int]]',
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# 'Complex object'
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],
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"size": [1, 2, 4, 8, 16, 32, 128, 256, 512, 1024, 2048, 4096, 8192],
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}
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predefined_basis = {
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"input": ["Complex object"],
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"size": [None],
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}
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basis = [
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ub.dict_union(common_basis, predefined_basis),
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ub.dict_union(common_basis, sized_basis),
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]
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# The Benchmarker class is a new experimental API around timerit to
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# abstract away the details of timing a process over a grid of parameters,
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# serializing the results, and aggregating results from disparate runs.
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benchmark = benchmarker.Benchmarker(
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name="bench_json",
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num=100,
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bestof=10,
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verbose=3,
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basis=basis,
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)
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def is_blocked(params):
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if params["input"] == "Complex object":
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# Some libraries can't handle the complex object
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if params["impl"] in {"orjson", "libpy_simdjson"}:
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return True
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# For each variation of your experiment, create a row.
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for params in benchmark.iter_params():
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if is_blocked(params):
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continue
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# Make any modifications you need to compute input kwargs for each
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# method here.
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impl_info = json_impls[params["impl"]]
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params["impl_version"] = impl_info["version"]
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method = impl_info[params["func"]]
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if method is None:
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# Not all libraries implement all methods
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continue
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py_data = data_lut[params["input"]](params["size"])
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if params["func"] == "dumps":
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data = py_data
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elif params["func"] == "loads":
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data = json.dumps(py_data)
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# Timerit will run some user-specified number of loops.
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# and compute time stats with similar methodology to timeit
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try:
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for timer in benchmark.measure():
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# Put any setup logic you dont want to time here.
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# ...
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with timer:
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# Put the logic you want to time here
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method(data)
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except Exception as ex:
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print(f"Failed to time: ex={ex}. Skipping")
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dpath = ub.Path.appdir("ujson/benchmark_results").ensuredir()
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result_fpath = benchmark.dump_in_dpath(dpath)
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return result_fpath
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