How DeepFlow Automates Full‑Stack Observability for Cloud‑Native Environments

01 From Heartbreak to Hope

Developers and SREs often clash over performance metrics and alert handling; the talk argues for a DevOps approach that unifies responsibilities and questions whether traditional SRE roles are still needed.

02 Building a Full‑Stack Metrics System

DeepFlow’s AutoMetrics leverages eBPF to automatically collect system and application metrics—including CPU, memory, I/O, network, RED (Request/Error/Delay) and business‑level indicators—without any code changes. Collected data is enriched with unified tags via AutoTagging , stored in ClickHouse, and accessed through a standard SQL API. Integration with Prometheus and Telegraf is achieved in two simple steps using remote_write or HTTP output.

03 Building Blind‑Spot‑Free Tracing

DeepFlow’s AutoTracing uses eBPF to capture distributed traces across all services and languages without instrumentation. Demonstrations with the Istio Bookinfo demo and a Spring Boot demo show that DeepFlow discovers far more spans than OpenTelemetry + Jaeger, revealing complete network paths, cross‑node communication, and even hidden MySQL transaction spans.

04 Empowering NewOps

By providing self‑service observability dashboards in Grafana and detailed trace visualizations in SkyWalking, DeepFlow helps operations teams transition from manual support to product‑oriented platforms, enabling developers to diagnose issues autonomously.

05 What Is DeepFlow

DeepFlow is an open‑source, highly automated observability platform built for cloud‑native applications. It combines eBPF, WASM and OpenTelemetry to deliver AutoMetrics, AutoTracing, AutoTagging and SmartEncoding, reducing instrumentation effort and operational complexity while offering a programmable, high‑performance data engine.