pixie
An open-source observability tool for Kubernetes applications that automatically collects telemetry using eBPF.
What is pixie?
Pixie is an open-source observability platform for Kubernetes that automatically collects telemetry data using eBPF technology. It provides instant visibility into cluster resources, network traffic, service performance, and application behavior without requiring manual instrumentation. Pixie solves the problem of complex observability setup by offering automatic data collection and in-cluster processing.
Kubernetes platform engineers, SREs, and developers who need immediate observability into their containerized applications without extensive configuration.
Developers choose Pixie because it provides automatic, zero-instrumentation observability with minimal performance overhead. Its unique combination of eBPF-based auto-telemetry, in-cluster edge compute, and flexible query language offers deep visibility that traditional monitoring tools require significant effort to achieve.
Overview
Instant Kubernetes-Native Application Observability
Use Cases
Best For
- Monitoring network traffic and DNS requests within Kubernetes clusters
- Debugging microservice communication with full-body request tracing
- Profiling database query performance across distributed applications
- Identifying performance bottlenecks with continuous CPU flame graphs
- Monitoring infrastructure health and resource usage by pod/node
- Deploying distributed bpftrace programs across cluster nodes
Not Ideal For
- Environments without Kubernetes or where eBPF cannot be enabled due to kernel restrictions
- Teams requiring centralized, long-term telemetry storage across multiple clusters for compliance or historical analysis
- Applications heavily reliant on custom or legacy protocols not automatically traced by Pixie's eBPF probes
Pros & Cons
Pros
Leverages eBPF to automatically gather full-body requests, resource metrics, and network data without any code changes, eliminating manual instrumentation overhead.
Processes data in-cluster using less than 5% of CPU, often under 2%, ensuring observability doesn't degrade application performance.
Provides PxL, a Pythonic query language, enabling custom analysis and visualization across UI, CLI, and APIs for tailored insights.
Automatically traces common protocols like HTTP, gRPC, and databases, offering immediate visibility into service communications and performance.
Cons
Designed solely for Kubernetes, making it irrelevant for non-containerized or hybrid environments, limiting its applicability.
Telemetry data is stored and processed locally within the cluster, so for historical data retention, integration with external systems is necessary.
Relies on eBPF, which requires specific kernel versions and can be blocked by security policies, adding deployment complexity.
Frequently Asked Questions
Related Projects
jaegerCNCF Jaeger, a Distributed Tracing Platform
eBPFebpf-go is a pure-Go library to read, modify and load eBPF programs and attach them to various hooks in the Linux kernel.
statsvizVisualise Go runtime metrics in real time
profileSimple profiling for Go
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