Continuous profiling for analysis of CPU, memory usage over time, and down to the line number. Saving infrastructure cost, improving performance, and increasing reliability.
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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!
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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.
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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!
You need to have Go, Node and Pnpm 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 buildThe 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.
Use --mode=otlp-forwarder --store-address=<host:port> to scrape pprof endpoints,
convert the profiles with OpenTelemetry's pprof translator, and export them to an
upstream that supports OTLP profiles over gRPC. No local profile store is started.
The existing --bearer-token, --bearer-token-file, --grpc-headers, and TLS flags
apply to this connection. Use --insecure for a plaintext upstream.
All target labels retained after relabeling and any --external-label values
become OTLP resource attributes, without renaming. Labels inside the pprof data
remain attributes on the OTLP samples. Relabeling changes only target metadata,
not labels within profiles. As with other scraping modes, discovery's internal
__meta_* labels must be copied by relabeling to retain them.
Example configs map target metadata to OpenTelemetry semantic-convention names:
- Static targets sets
service.name,service.namespace,service.instance.id, anddeployment.environment.name. - Kubernetes pods sets service attributes and
k8s.*attributes from pod discovery metadata.
Run either example with --config-path=deploy/otlp/<example>.yaml and the mode
and store address above. Parca's job, instance, and __name__ labels are also
forwarded as resource attributes for compatibility. The examples do not rename
pprof sample types or units. The upstream must support the OTLP profiles
v1development protocol used by this build. OTLP forwarding supports pprof
WriteRaw requests and OTLP Export, but not Parca's Arrow or debuginfo APIs.
The existing scraper-only and forwarder modes continue to use Parca's protocol.
Flags:
Usage: parca [flags]
Flags:
-h, --help Show context-sensitive help.
--config-path="parca.yaml"
Path to config file.
--mode="all" All runs all components. Scraper-only and
forwarder send profiles to a remote Parca
gRPC endpoint. OTLP-forwarder converts pprof
profiles and sends them to an OTLP profiles
gRPC endpoint.
--http-address=":7070" Address to bind HTTP server to.
--http-read-timeout=5s Timeout duration for HTTP server to read
request body.
--http-write-timeout=1m Timeout duration for HTTP server to write
response body.
--port="" (DEPRECATED) Use http-address instead.
--log-level="info" Log level.
--log-format="logfmt" Configure if structured logging as JSON or as
logfmt
--otlp-address=STRING The endpoint to send OTLP traces to.
--otlp-exporter="grpc" The OTLP exporter to use.
--otlp-insecure If true, disables TLS for OTLP exporters (both
gRPC and HTTP).
--cors-allowed-origins=CORS-ALLOWED-ORIGINS,...
Allowed CORS origins.
--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-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-snapshot-trigger-size=134217728
Number of bytes to trigger a snapshot. Defaults
to 1/4 of active memory. This is only used if
enable-wal is set.
--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.
--storage-index-on-disk Whether to store the index on disk instead
of in memory. Useful to reduce the memory
footprint of the store.
--symbolizer-demangle-mode="simple"
Mode to demangle C++ symbols. Default mode
is simplified: no parameters, no templates,
no return type
--symbolizer-external-addr-2-line-path=""
Path to addr2line utility, to be used for
symbolization instead of native implementation
--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=debuginfod.elfutils.org,...
Upstream debuginfod servers. Defaults to
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
--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
($PARCA_BEARER_TOKEN).
--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 scraped profiles in
forwarding modes.
--grpc-headers=KEY=VALUE;...
Additional gRPC headers to send with each
request to the remote store (key=value pairs).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! It explains how compile Parca, run it with Tilt as container in Kubernetes and send a Pull Request.
Thanks goes to these wonderful people (emoji key):
This project follows the all-contributors specification. Contributions of any kind welcome!
We use Meticulous to automatically test our UI for any unwanted changes.
