An open-source observability tool for Kubernetes applications that automatically collects telemetry using eBPF.
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.
Instant Kubernetes-Native Application Observability
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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.
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.