There are a lot of articles about profiling in Go. Julia Evans for examples wrote “Profiling Go programs with pprof” and I rely on it when I do not remember how to properly use pprof.

Rakyll wrote “Custom pprof profiles”.

pprof is a powerful tool provided by Go that helps any developer to figure out what is going in the Go runtime. When you see a spike in memory in your running container the next question is who is using all that memory. Profiles tell you the answer.

But they need to be grabbed at the right time. The unique way to have a profile when you need it is by taking them continuously. Based on your application you should be able to specify how often you have to gather a profile.

This requires a proper infrastructure that we can call “Continuous profiles infrastructure”. It is made of collectors, repositories and you need an API to store, retrieve and query those profiles.

When we had to set it up at InfluxData we started to craft our own one until I saw profefe on GitHub. What I love about the project is its clear scope. It is a repository for profiles. You can push them in Profefe and it provides an API to get them out, it servers the profiles in a way that make them easy to visualize directly with go tool pprof , you can even merge them together and so on. It also have a clear interface that helps you to implement your own storage.

The project README.md well explains how it works but I am going to summarize the most important actions in this article.

Getting Started

There is a docker image that you can run with the command:

docker run -d -p 10100:10100 profefe/profefe

You can push a profile in profefe:

$ curl -X POST \ "http://localhost:10100/api/0/profiles?service=apid&type=cpu" \ --data-binary @pprof.profefe.samples.cpu.001.pb.gz {"code":200,"body":{"id":"bo51acqs8snb9srq3p10","type":"cpu","service":"apid","created_at":"2019-12-30T15:18:11.361815452Z"}}

You can retrieve it directly via its ID:

$ go tool pprof http://localhost:10100/api/0/profiles/bo51acqs8snb9srq3p10 Fetching profile over HTTP from http://localhost:10100/api/0/profiles/bo51acqs8snb9srq3p10 Saved profile in /home/gianarb/pprof/pprof.profefe.samples.cpu.002.pb.gz File: profefe Type: cpu Time: Dec 23, 2019 at 4:06pm (CET) Duration: 30s, Total samples = 0

There is a lot more you can do, when pushing a profile you can set key value pairs called labels and they can be used to query a portion of the profiles.

You can use env=prod|test|dev or region=us|eu and so on.

Retrieving a profile only via ID it’s not the unique way to visualize it. Profefe merges together profiles from the same type in a specific time range:

GET /api/0/profiles/merge?service=<service>&type=<type>&from=<created_from>&to=<created_to>&labels=<key=value,key=value>

It returns the raw compressed binary, it is compatible with go tool pprof as well as the single profile by id.

Conclusion

I didn’t develop profefe, Vladimir (@narqo) is the maintainer, I like it and how it is coded. I think it solves a very common issue. He wrote a detailed post about his project “Continuous Profiling and Go”

Wouldn’t it be great if we could go back in time to the point when the issue happened in production and collect all runtime profiles. Unfortunately, to my knowledge, we can’t do that.

One of my colleague Chris Goller wrote a self contained AWS S3 implementation that is now submitted as PR. We are running it since a couple of weeks now. It is hard to onboard developers in a new tool, even more during Christmas but the API layers makes it very comfortable and friendly to use. Next article will be about what we did to get it running in Kubernetes continuously profiling our containers.