This repository provides tools for computing statistics and creating plots from garbage collection traces produced by the Go 1.4 and 1.5 runtimes. To collect such a trace, run a Go program with
$ env GODEBUG=gctrace=1 <program>
The garbage collection trace will be written to stderr.
cmd/plot-gctrace plots the breakdown of GC CPU utilization and heap
size versus wall-clock time. plot-gctrace is a Python script, so it
can be executed directly.
cmd/gcstats computes and plots a variety of high-level statistics
from a GC trace. To install it, run
$ go get github.com/aclements/go-gcstats/cmd/gcstats
By default, gcstats prints a high-level summary of the pause time
distribution and the mutator utilization,
$ gcstats < gctrace
Pause times: max=1ms 99%ile=1ms 95%ile=929us mean=399us
Mean mutator utilization: 89%
10ms mutator utilization: min=51% 1%ile=55% 5%ile=55%
It can also plot the pause time distribution as a kernel density estimate or empirical CDF:
$ gcstats -stopcdf -show < gctrace
However, the pause time distribution doesn't show you how close
together the pauses are (all pauses could be under a millisecond, but
if they're only a microsecond apart, that's still bad!). Hence,
gcstats can also plot a mutator utilization topology, which shows
mutator utilization at various percentiles and over windows of time
ranging from 1ms to 1s.
$ gcstats -mut -show < gctrace
Note that some of the analyses require a small patch to the Go 1.4 runtime to add program execution times. To apply this patch to a Go 1.4 tree, run
$ cd $GOROOT
$ patch < $GOPATH/src/github.com/aclements/go-gcstats/go14.patch
The plotting facilities of these tools currently depend on Python 3 and matplotlib. In addition, Seaborn is recommended.
To install these packages on Debian and Ubuntu, use
apt-get install python3-matplotlib python3-scipy python3-pandas
pip3 install seaborn
For general instructions on installing Seaborn, see installing Seaborn.