foundationdb/contrib/grv_proxy_model/plot.py

139 lines
4.9 KiB
Python
Executable File

#
# plot.py
#
# This source file is part of the FoundationDB open source project
#
# Copyright 2013-2020 Apple Inc. and the FoundationDB project authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import matplotlib.pyplot as plt
class Plotter:
def __init__(self, results):
self.results = results
def add_plot(data, time_resolution, label, use_avg=False):
out_data = {}
counts = {}
for t in data.keys():
out_data.setdefault(t // time_resolution * time_resolution, 0)
counts.setdefault(t // time_resolution * time_resolution, 0)
out_data[t // time_resolution * time_resolution] += data[t]
counts[t // time_resolution * time_resolution] += 1
if use_avg:
out_data = {t: v / counts[t] for t, v in out_data.items()}
plt.plot(list(out_data.keys()), list(out_data.values()), label=label)
def add_plot_with_times(data, label):
plt.plot(list(data.keys()), list(data.values()), label=label)
def display(self, time_resolution=0.1):
plt.figure(figsize=(40, 9))
plt.subplot(3, 3, 1)
for priority in self.results.started.keys():
Plotter.add_plot(self.results.started[priority], time_resolution, priority)
plt.xlabel("Time (s)")
plt.ylabel("Released/s")
plt.legend()
plt.subplot(3, 3, 2)
for priority in self.results.queued.keys():
Plotter.add_plot(self.results.queued[priority], time_resolution, priority)
plt.xlabel("Time (s)")
plt.ylabel("Requests/s")
plt.legend()
plt.subplot(3, 3, 3)
for priority in self.results.unprocessed_queue_sizes.keys():
data = {
k: max(v)
for (k, v) in self.results.unprocessed_queue_sizes[priority].items()
}
Plotter.add_plot(data, time_resolution, priority)
plt.xlabel("Time (s)")
plt.ylabel("Max queue size")
plt.legend()
num = 4
for priority in self.results.latencies.keys():
plt.subplot(3, 3, num)
median_latencies = {
k: v[int(0.5 * len(v))] if len(v) > 0 else 0
for (k, v) in self.results.latencies[priority].items()
}
percentile90_latencies = {
k: v[int(0.9 * len(v))] if len(v) > 0 else 0
for (k, v) in self.results.latencies[priority].items()
}
max_latencies = {
k: max(v) if len(v) > 0 else 0
for (k, v) in self.results.latencies[priority].items()
}
Plotter.add_plot(median_latencies, time_resolution, "median")
Plotter.add_plot(percentile90_latencies, time_resolution, "90th percentile")
Plotter.add_plot(max_latencies, time_resolution, "max")
plt.xlabel("Time (s)")
plt.ylabel(str(priority) + " Latency (s)")
plt.yscale("log")
plt.legend()
num += 1
for priority in self.results.rate.keys():
plt.subplot(3, 3, num)
if len(self.results.rate[priority]) > 0:
Plotter.add_plot(
self.results.rate[priority], time_resolution, "Rate", use_avg=True
)
if len(self.results.released[priority]) > 0:
Plotter.add_plot(
self.results.released[priority],
time_resolution,
"Released",
use_avg=True,
)
if len(self.results.limit[priority]) > 0:
Plotter.add_plot(
self.results.limit[priority], time_resolution, "Limit", use_avg=True
)
if len(self.results.limit_and_budget[priority]) > 0:
Plotter.add_plot(
self.results.limit_and_budget[priority],
time_resolution,
"Limit and budget",
use_avg=True,
)
if len(self.results.budget[priority]) > 0:
Plotter.add_plot(
self.results.budget[priority],
time_resolution,
"Budget",
use_avg=True,
)
plt.xlabel("Time (s)")
plt.ylabel("Value (" + str(priority) + ")")
plt.legend()
num += 1
plt.show()