Performance-Profiling

Table of Contents Introduction Observability Foundations for Distributed Systems 2.1. The Three Pillars: Metrics, Traces, Logs 2.2. Challenges in Modern Cloud‑Native Environments OpenTelemetry: The Vendor‑Neutral Telemetry Framework 3.1. Core Concepts 3.2. Instrumentation Libraries & SDKs 3.3. Exporters & Collectors eBPF: In‑Kernel, Low‑Overhead Instrumentation 4.1. What is eBPF? 4.2. Typical Use‑Cases for Observability Why Combine OpenTelemetry and eBPF? Architecture Blueprint 6.1. Data Flow Diagram 6.2. Component Interaction High‑Performance Profiling with eBPF 7.1. Capturing CPU, Memory, and I/O 7.2. Sample eBPF Programs (BCC & libbpf) Instrumenting Applications with OpenTelemetry 8.1. Automatic vs Manual Instrumentation 8.2. Go Example: Tracing an HTTP Service 8.3. Python Example: Exporting Metrics to Prometheus Bridging eBPF Data into OpenTelemetry Pipelines 9.1. Custom Exporter for eBPF Metrics 9.2. Using OpenTelemetry Collector with eBPF Receiver Visualization & Alerting 10.1. Grafana Dashboards for eBPF‑derived Metrics 10.2. Jaeger/Tempo for Distributed Traces Real‑World Case Study: Scaling a Microservice Platform Best Practices & Common Pitfalls Conclusion Resources Introduction Observability has become the cornerstone of modern distributed systems. As microservice architectures, serverless functions, and edge workloads proliferate, engineers need deep, low‑latency insight into what their code is doing across the entire stack—from the kernel up to the application layer. Traditional monitoring tools either incur prohibitive overhead or lack the granularity required to troubleshoot performance regressions in real time. ...