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