Add ClientPool and ThreadPool tests to RPC benchmark

Summary: Add script for plotting throughput

Reviewers: teon.banek, mculinovic, buda

Reviewed By: teon.banek

Subscribers: pullbot

Differential Revision: https://phabricator.memgraph.io/D1721
This commit is contained in:
Matej Ferencevic 2018-11-12 11:19:27 +01:00
parent 5df4d55ec1
commit 6d21e58b09
2 changed files with 209 additions and 8 deletions

View File

@ -44,15 +44,34 @@ DEFINE_bool(run_benchmark, true, "Set to false to only run server");
std::experimental::optional<communication::rpc::Server> server;
std::experimental::optional<communication::rpc::Client> clients[kThreadsNum];
std::experimental::optional<communication::rpc::ClientPool> client_pool;
std::experimental::optional<utils::ThreadPool> thread_pool;
static void BenchmarkRpc(benchmark::State &state) {
std::string data('a', state.range(0));
std::string data(state.range(0), 'a');
while (state.KeepRunning()) {
clients[state.thread_index]->Call<Echo>(data);
}
state.SetItemsProcessed(state.iterations());
}
static void BenchmarkRpcPool(benchmark::State &state) {
std::string data(state.range(0), 'a');
while (state.KeepRunning()) {
client_pool->Call<Echo>(data);
}
state.SetItemsProcessed(state.iterations());
}
static void BenchmarkRpcPoolAsync(benchmark::State &state) {
std::string data(state.range(0), 'a');
while (state.KeepRunning()) {
auto future = thread_pool->Run([&data] { client_pool->Call<Echo>(data); });
future.get();
}
state.SetItemsProcessed(state.iterations());
}
BENCHMARK(BenchmarkRpc)
->RangeMultiplier(4)
->Range(4, 1 << 13)
@ -60,10 +79,25 @@ BENCHMARK(BenchmarkRpc)
->Unit(benchmark::kNanosecond)
->UseRealTime();
BENCHMARK(BenchmarkRpcPool)
->RangeMultiplier(4)
->Range(4, 1 << 13)
->ThreadRange(1, kThreadsNum)
->Unit(benchmark::kNanosecond)
->UseRealTime();
BENCHMARK(BenchmarkRpcPoolAsync)
->RangeMultiplier(4)
->Range(4, 1 << 13)
->ThreadRange(1, kThreadsNum)
->Unit(benchmark::kNanosecond)
->UseRealTime();
int main(int argc, char **argv) {
::benchmark::Initialize(&argc, argv);
gflags::AllowCommandLineReparsing();
gflags::ParseCommandLineFlags(&argc, &argv, true);
google::InitGoogleLogging(argv[0]);
::benchmark::Initialize(&argc, argv);
if (FLAGS_run_server) {
server.emplace(
@ -81,16 +115,35 @@ int main(int argc, char **argv) {
if (FLAGS_run_benchmark) {
std::this_thread::sleep_for(std::chrono::milliseconds(200));
io::network ::Endpoint endpoint;
if (FLAGS_run_server) {
endpoint = server->endpoint();
} else {
endpoint = io::network::Endpoint(FLAGS_server_address, FLAGS_server_port);
}
for (int i = 0; i < kThreadsNum; ++i) {
if (FLAGS_run_server) {
clients[i].emplace(server->endpoint());
} else {
clients[i].emplace(
io::network::Endpoint(FLAGS_server_address, FLAGS_server_port));
}
clients[i].emplace(endpoint);
clients[i]->Call<Echo>("init");
}
// The client pool connects to the server only when there are no leftover
// unused RPC clients (during concurrent execution). To reduce the overhead
// of making connections to the server during the benchmark here we
// simultaneously call the Echo RPC on the client pool to make the client
// pool connect to the server `kThreadsNum` times.
client_pool.emplace(endpoint);
std::thread threads[kThreadsNum];
for (int i = 0; i < kThreadsNum; ++i) {
threads[i] =
std::thread([] { client_pool->Call<Echo>(std::string(10000, 'a')); });
}
for (int i = 0; i < kThreadsNum; ++i) {
threads[i].join();
}
thread_pool.emplace(kThreadsNum, "RPC client");
std::this_thread::sleep_for(std::chrono::milliseconds(200));
::benchmark::RunSpecifiedBenchmarks();

148
tools/plot/rpc_throughput Executable file
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@ -0,0 +1,148 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
RPC throughput barcharts generator (based on RPC benchmark JSON output).
To obtain data used for this script use:
`./tests/benchmark/rpc --benchmark_out=test.json --benchmark_out_format=json`
"""
import json
import collections
import os
import argparse
import numpy
import shutil
import matplotlib
# Must set "Agg" backend before importing pyplot
# This is so the script works on headless machines (without X11)
matplotlib.use("Agg")
import matplotlib.pyplot as plt
def parse_args():
argp = argparse.ArgumentParser(description=__doc__)
argp.add_argument("input", help="Load data from file.")
argp.add_argument("output", help="Save plots to this directory.")
argp.add_argument("--plot-width", type=int, default=1920,
help="Pixel width of generated plots.")
argp.add_argument("--plot-height", type=int, default=1080,
help="Pixel height of generated plots.")
argp.add_argument("--plot-dpi", type=int, default=96,
help="DPI of generated plots.")
return argp.parse_args()
def humanize(num):
suffix = ["", "k", "M", "G", "T", "P", "E", "Z", "Y"]
pos = 0
while num >= 1000.0:
if pos == len(suffix) - 1:
break
num /= 1000
pos += 1
return str(int(round(num, 0))) + suffix[pos]
def dehumanize(num):
pos = -1
suffix = ["k", "M", "G", "T", "P", "E", "Z", "Y"]
for index, suff in enumerate(suffix):
if num.endswith(suff):
pos = index
num = num[:-1]
break
return int(num) * 1000 ** (pos + 1)
def autolabel(ax, rects):
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width()/2., 1.00*height,
humanize(height),
ha="center", va="bottom")
def generate_plot(size, results, plot_width, plot_height, plot_dpi,
output_file):
# Font size.
plt.rc("font", size=10)
plt.rc("axes", titlesize=24)
plt.rc("axes", labelsize=16)
plt.rc("xtick", labelsize=12)
plt.rc("ytick", labelsize=12)
plt.rc("legend", fontsize=16)
plt.rc("figure", titlesize=24)
groups = sorted(results.keys())
categories = list(map(lambda x: x[0], results[groups[0]]))
# Plot.
ind = numpy.arange(len(groups))
width = 0.10
fig, ax = plt.subplots()
fig.set_size_inches(plot_width / plot_dpi,
plot_height / plot_dpi)
ax.set_xlabel("Concurrent threads")
ax.set_ylabel("Throughput (call/s)")
ax.set_facecolor("#dcdcdc")
ax.set_xticks(ind + width / len(categories))
ax.set_xticklabels(groups)
for line in ax.get_xgridlines():
line.set_linestyle(" ")
for line in ax.get_ygridlines():
line.set_linestyle("--")
ax.set_axisbelow(True)
plt.grid(True)
ax.set_title("RPC throughput (size: {})".format(size))
# Draw bars.
rects = []
for index, category in enumerate(categories):
category_results = [results[group][index][1] for group in groups]
rect = ax.bar(ind + index * width, category_results, width)
rects.append(rect)
autolabel(ax, rect)
ax.legend(rects, categories)
# Plot output.
plt.savefig(output_file, dpi=plot_dpi)
def main():
# Read the arguments.
args = parse_args()
# Load data.
with open(args.input) as f:
data = json.load(f)
# Process data.
results = collections.defaultdict(lambda: collections.defaultdict(list))
for benchmark in data["benchmarks"]:
info = benchmark["name"].split("/")
name, size = info[0:2]
threads = int(info[-1].split(":")[1])
throughput = benchmark["items_per_second"]
results[size][threads].append((name, throughput))
# Cleanup output directory.
if os.path.isdir(args.output):
shutil.rmtree(args.output)
if os.path.exists(args.output):
os.remove(args.output)
os.mkdir(args.output)
# Generate plots.
sizes = sorted(results.keys(), key=dehumanize)
for prefix, size in enumerate(sizes):
result = results[size]
output_file = os.path.join(args.output,
"{}_{}.png".format(prefix, size))
generate_plot(size, result, args.plot_width, args.plot_height,
args.plot_dpi, output_file)
if __name__ == "__main__":
main()