内容简介：Usually, this approach gives rather stable results; for example, benchmarking MySQL with Sysbench OLTP Read-Only workload I get a variance of less than one percent between 1-minute runs.In this case, though, I was seeing some
When I run a benchmark and want to measure the CPU efficiency of something, I find it’s often a good choice to run a benchmark program, as well as the database, on the same server. This is in order to eliminate network impact and to look at single-thread performance, to eliminate contention.
Usually, this approach gives rather stable results; for example, benchmarking MySQL with Sysbench OLTP Read-Only workload I get a variance of less than one percent between 1-minute runs.
In this case, though, I was seeing some 20 percent difference between the runs, which looked pretty random and would not go away even with longer 10-minute runs.
The benchmark I did was benchmarking MySQL through ProxySQL (all running on the same machine):
Sysbench -> ProxySQL -> MySQL
As I thought more about possible reasons, I thought CPU scheduling might be a problem. As requests pass from one process to another, does Linux Kernel schedule requests at the same CPU core or a different one? Even though only one process can really be busy processing a request at the same time in this setup, there is the question of CPU cache usage, as well as other implications of scheduling work on the single CPU core or a different one.
To validate my assumptions, I used taskset , a little utility available in modern Linux distributions, which allows the mapping of a set process CPU affinity, essentially mapping it to some of the CPU cores.
I set MySQL and ProxySQL to be limited to different CPU cores:
taskset -pc 0 `pidof mysqld` taskset -pc 1 8601 #pid of ProxySQL main process
And run sysbench bound to the given CPU core too:
taskset -c 2 sysbench --rand-type=uniform --db-driver=mysql --db-ps-mode=auto --mysql-socket="/tmp/proxysql.sock" --mysql-user=sbtest --mysql-password=sbtest --mysql-db=sbtest /usr/share/sysbench/oltp_point_select.lua --table-size=10000000 --threads=1 --time=60 --rate=0 --percentile=99 run
With this change, I’m back to having very stable benchmark results. So if you ever run to a similar problem, see if setting process affinity with taskset helps!
以上所述就是小编给大家介绍的《Benchmarking: More Stable Results with CPU Affinity Setting》，希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对 码农网 的支持！
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深入理解程序设计
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