👁️ Introduction

Introduction to Observability #

What is Observability? #

Observability is the ability to understand the internal state of a system by examining its external outputs. In the context of distributed systems and Kubernetes, observability is crucial for understanding how your applications behave in production, diagnosing issues, and maintaining system health.

Modern applications running on Kubernetes are inherently complex, with multiple services, containers, and dependencies. Without proper observability, debugging issues becomes like finding a needle in a haystack. Observability gives you the tools and insights needed to understand what’s happening inside your systems.

The Three Pillars of Observability #

Observability is traditionally built on three foundational pillars, each providing different perspectives on your system’s behavior:

1. Logs #

Logs are time-ordered records of discrete events that happened within your application or infrastructure. They provide detailed, contextual information about what your system was doing at specific points in time.

Logs are particularly useful for:

  • Debugging specific errors or issues
  • Understanding application flow and business logic
  • Compliance and audit requirements
  • Root cause analysis

Common Technologies:

  • Elasticsearch - Search and analytics engine for log data
  • Fluentd - Open source data collector for unified logging layer
  • Loki - Horizontally-scalable, highly- available, multi-tenant log aggregation system

2. Metrics #

Metrics are numerical measurements taken over time. They provide quantitative data about your system’s performance, resource usage, and behavior patterns. Metrics are typically aggregated and stored as time series data.

Metrics excel at:

  • Monitoring system performance and health
  • Setting up alerts and notifications
  • Capacity planning and scaling decisions
  • Understanding trends and patterns over time

Common Technologies:

  • Prometheus - Open-source monitoring system with time series database
  • InfluxDB - Time series database optimized for metrics
  • StatsD - Network daemon for collecting and aggregating metrics

3. Traces #

Traces track the journey of a request as it flows through different services in a distributed system. They provide a detailed view of how services interact and where time is spent during request processing.

Traces are essential for:

  • Understanding service dependencies
  • Identifying performance bottlenecks
  • Debugging issues in distributed systems
  • Optimizing request flow and latency

Common Technologies:

  • Jaeger - Open source, distributed tracing platform
  • Zipkin - Distributed tracing system that helps gather timing data
  • OpenTelemetry - Collection of tools, APIs, and SDKs for telemetry data

How the Pillars Work Together #

While each pillar provides valuable insights on its own, they’re most powerful when used together:

  • Metrics help you identify that something is wrong
  • Logs help you understand what went wrong
  • Traces help you see where in your system the problem occurred

For example, you might notice high error rates in your metrics, use traces to identify which service is failing, and then examine logs from that service to understand the root cause.

Observability in Kubernetes #

Kubernetes adds additional complexity to observability because:

  • Applications run in ephemeral containers that can be created and destroyed frequently
  • Services are distributed across multiple nodes and pods
  • The platform itself (Kubernetes components) needs monitoring
  • Resource allocation and scheduling decisions affect application performance

A comprehensive Kubernetes observability strategy typically includes:

  • Application metrics and logs from your services
  • Infrastructure metrics from nodes, pods, and containers
  • Kubernetes platform metrics from API server, scheduler, and other components
  • Network observability for service-to-service communication

Getting Started #

In this chapter, we’ll explore two fundamental tools for Kubernetes observability:

  1. Prometheus - For collecting and storing metrics
  2. Grafana - For visualizing metrics and creating dashboards

These tools form the backbone of most Kubernetes monitoring setups and provide a solid foundation for understanding observability concepts.

Key Takeaways #

  • Observability helps you understand system behavior through external signals
  • The three pillars (logs, metrics, traces) provide complementary perspectives
  • Modern distributed systems require observability to be effective
  • Kubernetes environments need specialized observability approaches
  • Prometheus and Grafana are fundamental tools in the Kubernetes ecosystem

In the next sections, we’ll dive deeper into Prometheus for metrics collection and Grafana for visualization and dashboards.

August 17, 2025