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#!/usr/bin/env python3
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
import collections
import io
import math
import os
import pandas as pd
import seaborn
from matplotlib import pyplot
from tabulate import tabulate
import orjson
LIBRARIES = ("orjson", "ujson", "rapidjson", "simplejson", "json")
def aggregate():
benchmarks_dir = os.path.join(".benchmarks", os.listdir(".benchmarks")[0])
res = collections.defaultdict(dict)
for filename in os.listdir(benchmarks_dir):
with open(os.path.join(benchmarks_dir, filename), "r") as fileh:
data = orjson.loads(fileh.read())
for each in data["benchmarks"]:
res[each["group"]][each["extra_info"]["lib"]] = {
"data": [val * 1000 for val in each["stats"]["data"]],
"median": each["stats"]["median"] * 1000,
"ops": each["stats"]["ops"],
"correct": each["extra_info"]["correct"],
}
return res
def tab(obj):
buf = io.StringIO()
headers = (
"Library",
"Median latency (milliseconds)",
"Operations per second",
"Relative (latency)",
)
seaborn.set(rc={"figure.facecolor": (0, 0, 0, 0)})
seaborn.set_style("darkgrid")
barplot_data = []
for group, val in sorted(obj.items(), reverse=True):
buf.write("\n" + "#### " + group + "\n\n")
table = []
for lib in LIBRARIES:
correct = val[lib]["correct"]
table.append(
[
lib,
val[lib]["median"] if correct else None,
int(val[lib]["ops"]) if correct else None,
0,
]
)
barplot_data.append(
{
"operation": "deserialization"
if "deserialization" in group
else "serialization",
"group": group.strip("serialization")
.strip("deserialization")
.strip(),
"library": lib,
"latency": val[lib]["median"],
"operations": int(val[lib]["ops"]) if correct else None,
}
)
orjson_baseline = table[0][1]
for each in table:
each[3] = (
"%.1f" % (each[1] / orjson_baseline)
if isinstance(each[1], float)
else None
)
if group.startswith("github"):
each[1] = "%.2f" % each[1] if isinstance(each[1], float) else None
else:
each[1] = "%.1f" % each[1] if isinstance(each[1], float) else None
buf.write(tabulate(table, headers, tablefmt="github") + "\n")
for operation in ("deserialization", "serialization"):
per_op_data = list(
(each for each in barplot_data if each["operation"] == operation)
)
if not per_op_data:
continue
max_y = 0
json_baseline = {}
for each in per_op_data:
if each["group"] == "witter.json":
each["group"] = "twitter.json"
if each["library"] == "json":
json_baseline[each["group"]] = each["operations"]
for each in per_op_data:
relative = each["operations"] / json_baseline[each["group"]]
each["relative"] = relative
max_y = max(max_y, relative)
p = pd.DataFrame.from_dict(per_op_data)
p.groupby("group")
graph = seaborn.barplot(
p,
x="group",
y="relative",
orient="x",
hue="library",
errorbar="sd",
legend="brief",
)
graph.set_xlabel("Document")
graph.set_ylabel("Operations/second relative to stdlib json")
pyplot.title(operation)
# ensure Y range
max_y = int(math.ceil(max_y))
if max_y > 10 and max_y % 2 > 0:
max_y = max_y + 1
pyplot.gca().set_yticks(
list(
{1, max_y}.union(
set(int(y) for y in pyplot.gca().get_yticks() if int(y) <= max_y)
)
)
)
# print Y as percent
pyplot.gca().set_yticklabels([f"{x}x" for x in pyplot.gca().get_yticks()])
# reference for stdlib
pyplot.axhline(y=1, color="#999", linestyle="dashed")
pyplot.savefig(fname=f"doc/{operation}", dpi=300)
pyplot.close()
print(buf.getvalue())
tab(aggregate())
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