Blackfire Continuous Profiler
Low-overhead continuous profiling for app performance optimization.
Open-Source Projects
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Discover top open-source software, updated regularly with real-world adoption signals.
Continuous eBPF profiling for cost-effective performance insights
Parca provides zero-instrumentation, continuous profiling across Kubernetes and systemd environments, delivering pprof-compatible data with low overhead to help reduce waste, boost performance, and troubleshoot incidents.
Parca delivers continuous, zero‑instrumentation profiling for modern cloud workloads. By leveraging eBPF, it automatically discovers targets in Kubernetes clusters or systemd services, collecting CPU and memory usage down to the line number with minimal overhead.
The profiler outputs standard pprof profiles, enabling seamless integration with existing tooling, while also ingesting external pprof data. Profiles are stored in an optimized, label‑driven store that supports powerful queries across dimensions such as version, region, or deployment.
Deploy the Parca agent alongside your services; it runs as a single binary with configurable storage options and optional persistence. The web UI, accessible on port 7070 by default, visualizes trends over time, helping teams identify hot paths, reduce resource waste, and accelerate incident resolution.
When teams consider Parca, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Identify CPU hot paths in a Kubernetes service
Pinpoint functions consuming the majority of CPU, enabling targeted optimizations that reduce resource usage.
Detect memory leaks in a Go application
Continuous memory profiling reveals growing allocations, allowing developers to fix leaks before they impact stability.
Compare performance across deployment versions
Label‑based queries show how code changes affect latency and CPU, supporting data‑driven rollbacks.
Correlate incidents with resource spikes
During outages, profiling data provides deep insight into execution paths, speeding root‑cause analysis.
No. Parca uses eBPF to profile processes without any code changes.
Parca works with any language that can produce pprof profiles and has native binaries, including C, C++, Rust, Go, and more.
Profiles are stored in an optimized columnar format, indexed by user‑defined labels for fast querying.
Yes, Parca can ingest any pprof‑formatted profile alongside data collected by its eBPF agent.
A Linux kernel with eBPF support (typically 4.14 or newer) and sufficient storage for profile data.
Project at a glance
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