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Parca: Continuous profiling for analysis of CPU, memory usage over time, and down to the line number.

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

Screenshot of Parca

Features

  • 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.

Why?

  • 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.

Feedback & Support

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

Installation & Documentation

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

parca.dev

Development

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

Running the compiled Parca binary

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.

Configuration

Flags:

Usage: parca

Flags:
  -h, --help                       Show context-sensitive help.
      --config-path="parca.yaml"
                                   Path to config file.
      --mode="all"                 Scraper only runs a scraper that sends to a
                                   remote gRPC endpoint. All runs all
                                   components.
      --log-level="info"           log level.
      --port=":7070"               Port string for server
      --cors-allowed-origins=CORS-ALLOWED-ORIGINS,...
                                   Allowed CORS origins.
      --otlp-address=STRING        OpenTelemetry collector address to send
                                   traces to.
      --version                    Show application version.
      --path-prefix=""             Path prefix for the UI
      --mutex-profile-fraction=0
                                   Fraction of mutex profile samples to collect.
      --block-profile-rate=0       Sample rate for block profile.
      --storage-debug-value-log    Log every value written to the database into
                                   a separate file. This is only for debugging
                                   purposes to produce data to replay situations
                                   in tests.
      --storage-granule-size=8196
                                   Granule size for storage.
      --storage-active-memory=536870912
                                   Amount of memory to use for active storage.
                                   Defaults to 512MB.
      --symbolizer-demangle-mode="simple"
                                   Mode to demangle C++ symbols. Default mode is
                                   simplified: no parameters, no templates, no
                                   return type
      --symbolizer-number-of-tries=3
                                   Number of tries to attempt to symbolize an
                                   unsybolized location
      --metastore="badgerinmemory"
                                   Which metastore implementation to use
      --debug-infod-upstream-servers=https://debuginfod.elfutils.org,...
                                   Upstream debuginfod servers. Defaults to
                                   https://debuginfod.elfutils.org. It is an
                                   ordered list of servers to try. Learn more at
                                   https://sourceware.org/elfutils/Debuginfod.html
      --debug-infod-http-request-timeout=5m
                                   Timeout duration for HTTP request to upstream
                                   debuginfod server. Defaults to 5m
      --store-address=STRING       gRPC address to send profiles and symbols to.
      --bearer-token=STRING        Bearer token to authenticate with store.
      --bearer-token-file=STRING
                                   File to read bearer token from to
                                   authenticate with store.
      --insecure                   Send gRPC requests via plaintext instead of
                                   TLS.
      --insecure-skip-verify       Skip TLS certificate verification.
      --external-label=KEY=VALUE;...
                                   Label(s) to attach to all profiles in
                                   scraper-only mode.

Credits

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/

Contributing

Check out our Contributing Guide to get started! It explains how compile Parca, run it with Tilt as container in Kubernetes and send a Pull Request.

Contributors

Thanks goes to these wonderful people (emoji key):


Frederic Branczyk


Thor


Matthias Loibl


Kemal Akkoyun


Sumera Priyadarsini


Jssica Lins


Holger Freyther


Sergiusz Urbaniak


Pawe Krupa


Ben Ye


Felix


Christian Bargmann


Yomi Eluwande


Manoj Vivek


Monica Wojciechowska


Manuel Rger


Avinash Upadhyaya K R


Ikko Ashimine


Maxime Brunet


rohit


Ujjwal Goyal


Javier Honduvilla Coto


Marsel Mavletkulov


Kautilya Tripathi

This project follows the all-contributors specification. Contributions of any kind welcome!



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