A kubectl plugin that allows you to profile production applications with low-overhead by generating FlameGraphs

Running kubectlf-flame does not require any modification to existing pods.

• Requirements
• Usage
• Installing
• How it works
• Contribute
• Maintainers
• License

• Supported languages: Go, Java (any JVM based language), Python, Ruby, and NodeJS
• Kubernetes cluster that use Docker as the container runtime (tested on GKE, EKS and AKS)

In order to profile a Java application in pod mypod for 1 minute and save the flamegraph as /tmp/flamegraph.svg run:

kubectl flame mypod -t 1m --lang java -f /tmp/flamegraph.svg

Profiling Java application in alpine based containers require using --alpine flag:

kubectl flame mypod -t 1m -f /tmp/flamegraph.svg --lang java --alpine

NOTICE: this is only required for Java apps, the --alpine flag is unnecessary for Go profiling.

Pods that contains more than one container require specifying the target container as an argument:

kubectl flame mypod -t 1m --lang go -f /tmp/flamegraph.svg mycontainer

Profiling Go application in pods that contains more than one process require specifying the target process name via --pgrep flag:

kubectl flame mypod -t 1m --lang go -f /tmp/flamegraph.svg --pgrep go-app

Java profiling assumes that the process name is java. Use --pgrep flag if your process name is different.

You can install kubectl flame using the Krew, the package manager for kubectl plugins.

Once you have Krew installed just run:

kubectl krew install flame

See the release page for the full list of pre-built assets.

kubectl-flame launch a Kubernetes Job on the same node as the target pod. Under the hood kubectl-flame use async-profiler in order to generate flame graphs for Java applications. Interaction with the target JVM is done via a shared /tmp folder. Golang support is based on ebpf profiling. Python support is based on py-spy. Ruby support is based on rbspy. NodeJS support is based on perf. In order for Javascript Symbols to be resolved, node process needs to be run with --perf-basic-prof flag.

Please refer to the contributing.md file for information about how to get involved. We welcome issues, questions, and pull requests.

• Eden Federman: [email protected]

This project is licensed under the terms of the Apache 2.0 open source license. Please refer to LICENSE for the full terms.