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Continuous profiling for analysis of CPU, memory usage over time, and down to the line number. Saving infrastructure cost, improving performance, and increasing reliability.

• eBPF Profiler: A single profiler, using eBPF, automatically discovering targets from Kubernetes or systemd across the entire infrastructure with very low overhead. Supports C, C++, Rust, Go, and more!

• Open Standards: Both producing pprof formatted profiles with the eBPF based profiler, and ingesting any pprof formatted profiles allowing for wide language adoption and interoperability with existing tooling.

• Optimized Storage & Querying: Efficiently storing profiling data while retaining raw data and allowing slicing and dicing of data through a label-based search. Aggregate profiling data infrastructure wide, view single profiles in time or compare on any dimension.

• Save Money: Many organizations have 20-30% of resources wasted with easily optimized code paths. The Parca Agent aims to lower the entry bar by requiring 0 instrumentation for the whole infrastructure. Deploy in your infrastructure and get started!
• Improve Performance: Using profiling data collected over time, Parca can with confidence and statistical significance determine hot paths to optimize. Additionally it can show differences between any label dimension, such as deploys, versions, and regions.
• Understand Incidents: Profiling data provides unique insight and depth into what a process executed over time. Memory leaks, but also momentary spikes in CPU or I/O causing unexpected behavior, is traditionally difficult to troubleshoot are a breeze with continuous profiling.

If you have any feedback, please open a discussion in the GitHub Discussions of this project.
We would love to learn what you think!

Check Parca's website for updated and in-depth installation guides and documentation!

parca.dev

You need to have Go, Node and Yarn installed.

Clone the project

git clone https://github.com/parca-dev/parca.git

Go to the project directory

cd parca

Build the UI and compile the Go binaries

make build

The binary was compiled to bin/parca .

./bin/parca

Now Parca is running locally and its web UI is available on http://localhost:7070/.

By default Parca is scraping it's own pprof endpoints and you should see profiles show up over time. The scrape configuration can be changed in the parca.yaml in the root of the repository.

Parca was originally developed by Polar Signals. Read the announcement blog post: https://www.polarsignals.com/blog/posts/2021/10/08/introducing-parca-we-got-funded/

Check out our Contributing Guide to get started!