Continuous profiling for analysis of CPU, memory usage over time, and down to the line number. Saving infrastructure cost, improving performance, and increasing reliability.
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!
Development
You need to have Go, Node and Yarn installed.
Clone the project
git clone https://github.com/parca-dev/parca.gitGo to the project directory
cd parcaBuild the UI and compile the Go binaries
make buildRunning 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.
--http-address=":7070" Address to bind HTTP server to.
--port="" (DEPRECATED) Use http-address instead.
--log-level="info" Log level.
--log-format="logfmt" Configure if structured logging as JSON or as
logfmt
--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.
--enable-persistence Turn on persistent storage for the metastore and
profile storage.
--storage-granule-size=26265625
Granule size in bytes for storage.
--storage-active-memory=536870912
Amount of memory to use for active storage.
Defaults to 512MB.
--storage-path="data" Path to storage directory.
--storage-enable-wal Enables write ahead log for profile storage.
--storage-row-group-size=8192
Number of rows in each row group during
compaction and persistence. Setting to <= 0
results in a single row group per file.
--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
--debuginfo-cache-dir="/tmp"
Path to directory where debuginfo is cached.
--debuginfo-upload-max-size=1000000000
Maximum size of debuginfo upload in bytes.
--debuginfo-upload-max-duration=15m
Maximum duration of debuginfo upload.
--debuginfo-uploads-signed-url
Whether to use signed URLs for debuginfo
uploads.
--debuginfod-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
--debuginfod-http-request-timeout=5m
Timeout duration for HTTP request to upstream
debuginfod server. Defaults to 5m
--metastore="badger" Which metastore implementation to use
--profile-share-server="api.pprof.me:443"
gRPC address to send share profile requests to.
--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):
This project follows the all-contributors specification. Contributions of any kind welcome!