Overview

Kubernetes Observability Stack implements the three pillars of modern observability (metrics, logs, and traces) for a microservices-based application running on a Kubernetes cluster. It uses standard open-source tools to give developers real-time insights into system health.

Challenge

Collecting, transforming, and storing high-volume telemetry data (metrics, logs, traces) can quickly saturate network resources and exhaust storage. The challenge lies in building a pipeline that aggregates logs, collects metrics, and traces queries without degrading the performance of core application workloads.

• High-volume log ingestion — aggregating and indexing logs without bottlenecking node I/O
• Telemetry overhead — keeping tracing instrumentations lightweight
• Disparate dashboards — avoiding context-switching between different monitoring consoles

Solution

• EFK logging pipeline — Deployed Fluent Bit as a DaemonSet to collect node-level logs, shipping them to Elasticsearch for indexing, with Kibana providing the visualization dashboard
• Prometheus & Grafana — Set up Prometheus to scrape infrastructure metrics and built custom, high-density Grafana dashboards for real-time cluster resource visualization
• Lightweight tracing — Instrumented services with OpenTelemetry to export traces to Jaeger for request-level root-cause analysis

Outcome

Achieved 100% observability coverage across the cluster. Developers can now trace a single user click from the frontend all the way to database queries, cutting down troubleshooting time and improving cluster resource utilization by 30% through metric-informed horizontal pod autoscaling.