Datadog
Observability platform for metrics, logs, and traces
Pixie
Instant, code‑free observability for Kubernetes applications and clusters
Pixie provides automatic, eBPF‑based telemetry and a Pythonic query language to visualize network, infrastructure, and application performance directly inside your Kubernetes cluster, using under 5% CPU.
Overview
Audience
Pixie is built for developers, SREs, and platform engineers who run containerized workloads on Kubernetes and need instant visibility without adding instrumentation code.
Capabilities
Using eBPF, Pixie automatically captures full‑body requests, resource metrics, network flows, and application profiles across the entire cluster. The data is stored and queried locally, keeping CPU usage under 5 % in most cases. PxL, a Python‑style query language, lets users explore traffic maps, flame graphs, latency per service, database query performance, and continuous profiling through the web UI, CLI, or API.
Deployment
Installation takes minutes via the provided Helm chart or operator. All telemetry components run as in‑cluster edge services, eliminating the need for external collectors. Once deployed, teams can start querying immediately, create custom dashboards, and integrate Pixie into CI/CD pipelines or alerting systems.
Highlights
Automatic eBPF telemetry with zero code changes
In‑cluster edge compute uses <5 % CPU
PxL Pythonic query language across UI, CLI, API
Full‑stack visibility: network, infra, services, databases, profiling
Pros
- Zero‑instrumentation data collection
- Low resource overhead for production clusters
- Unified query language for ad‑hoc analysis
- Interactive UI plus CLI and API access
Considerations
- Requires Kubernetes with eBPF‑compatible kernel
- Limited to environments where Pixie components can run
- Learning curve for PxL syntax
- Additional pods increase cluster surface area
Managed products teams compare with
When teams consider Pixie, 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.
Fit guide
Great for
- Teams needing instant observability of Kubernetes workloads
- Developers debugging microservice traffic without redeploying
- SREs monitoring latency, errors, and resource usage in real time
- Organizations that prefer open‑source, extensible tooling
Not ideal when
- Non‑Kubernetes or bare‑metal deployments
- Clusters running older kernels lacking eBPF support
- Extremely resource‑constrained edge clusters
- Environments requiring commercial SLA guarantees
How teams use it
Network Monitoring
Visualize intra‑cluster traffic flows, DNS queries, TCP drops, and retransmissions in real time.
Service Performance
Measure per‑service latency, identify slow endpoints, and view full‑body request traces.
Database Query Profiling
Track query latency, error rates, and throughput per pod and per normalized query.
Continuous Application Profiling
Generate flame graphs and CPU usage profiles to pinpoint performance bottlenecks in running code.
Tech snapshot
C++53%
Go22%
TypeScript10%
Starlark6%
Python5%
C2%
Frequently asked questions
What is Pixie?
Pixie is an observability platform that automatically collects telemetry from Kubernetes clusters using eBPF and provides a queryable interface for deep insight.
How does Pixie gather data without instrumenting code?
It leverages eBPF probes built into the Linux kernel to capture network packets, system calls, and resource metrics directly from the running pods.
What impact does Pixie have on cluster performance?
The edge compute component is designed to use less than 5 % of CPU in typical workloads, often under 2 % after optimization.
How can I query the collected data?
Pixie offers PxL, a Python‑style query language accessible via the web UI, CLI, or programmatic API.
Is Pixie open source and free to use?
Yes, Pixie is released under the Apache‑2.0 license and can be deployed freely on any compatible Kubernetes cluster.
Project at a glance
Active- Stars
- 6,330
- Watchers
- 6,330
- Forks
- 487
LicenseApache-2.0
Repo age5 years old
Last commitlast week
Primary languageC++
Last synced yesterday