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DeepFlow – open-source eBPF Distributed Tracing

success Zero Code success Full Stack rocketeBPF & Wasm

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Network-Centric Distributed Tracing with DeepFlow: Troubleshooting Your Microservices in Zero Code Download paper >>

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Zero Code distributed tracing powered by eBPF supports applications in any language and infrastructures including gateways, service meshes, databases, message queues, DNS and NICs, leaving no blind spots. Full Stack network performance metrics and file I/O events are automatically collected for each Span. Distributed tracing enters a new era: Zero Instrumentation.

DeepFlow collects profiling data at a cost of below 1% with Zero Code , plots OnCPU/OffCPU function call stack flame graphs, locates Full Stack performance bottleneck in application, library and kernel functions, and automatically relates them to distrubuted tracing data. DeepFlow can even analyze code performance through network profiling under old version kernels (2.6+).

DeepFlow can serve as storage backed for Prometheus, OpenTelemetry, SkyWalking and Pyroscope. It also provides SQL, PromQL and OLTP APIs to work as data source in popular observability stacks. It injects meta tags for all obervability signals including cloud resource, K8s container, K8s labels, K8s annotations, CMDB business attributes, etc., eliminating data silos.

SmartEncoding injects standardized and pre-encoded meta tags into all observability data, reducing storage overhead by 10x compared to ClickHouse String or LowCard method. Custom tags and observability data are stored separately, making tags available for almost unlimited dimensions and cardinalities with uncompromised query experience like BigTable.

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$ helm install deepflow –repo https://deepflowio.github.io/deepflow deepflow


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