Introduction

Observability is a foundational discsipline in Site Reliability Engineering (SRE). According to Google’s Site Reliability Engineering book, observability is more than just dashboards and logs — it’s a mindset that empowers teams to understand and improve system behavior in real time and during failure.

In this post, we highlight key lessons from the SRE book series to help you design and operate more observable, reliable systems.

Why Observability Matters

SRE accepts that distributed systems will fail. Instead of avoiding failure, the goal is to:

• Expose internal system state (transparency)
• Diagnose issues without guesswork (inspectability)
• React quickly and confidently (recoverability)

Observability is the foundation for these capabilities.

“Hope is not a strategy. Luck is not a factor. Fear is not an option.”

Lessons from the SRE Book

1. SLIs, SLOs, and SLAs: Aligning Metrics with Business Commitments

To quantify and manage reliability, Google SREs recommend tracking:

• SLIs (Service Level Indicators) – measurable metrics that reflect user experience (e.g., latency, availability).
• SLOs (Service Level Objectives) – internal goals built on SLIs (e.g., 99.9% of requests complete in under 100ms).
• SLAs (Service Level Agreements) – external contracts built on SLOs, often with financial or legal implications.

“SLOs are key to making data-driven decisions about reliability; they’re at the core of SRE practices.” — The Site Reliability Workbook 📘 Implementing SLOs

2. Black-box and White-box Monitoring

Effective observability requires both:

• Black-box monitoring – measures what users experience (e.g., HTTP uptime checks, synthetic transaction tests like Datadog Synthetics).
• White-box monitoring – exposes internal system states (e.g., metrics, traces, memory usage).

This layered approach enables SREs to catch both symptoms and root causes.

“Your monitoring system should address two questions: what’s broken, and why?” — Site Reliability Engineering, Chapter 6 📘 Monitoring Distributed Systems

3. Event Detection and the Four Golden Signals

The SRE Book recommends always tracking the Four Golden Signals:

• Latency – response time of requests
• Traffic – load on the system (e.g., requests per second)
• Errors – rate of failed requests
• Saturation – how close systems are to capacity limits

Monitoring these gives teams high-leverage visibility into user experience and system stress.

To balance responsiveness and insight:

• Use aggregated, low-cardinality metrics for fast, lightweight alerting
• Use high-cardinality logs and traces for in-depth debugging and root cause analysis

“Every page response should require intelligence. If a page merely merits a robotic response, it shouldn’t be a page.” — Site Reliability Engineering, Chapter 6 📘 Monitoring Distributed Systems

4. Postmortems Depend on Observability

When things go wrong, observability data supports effective incident analysis. According to the book:

• Postmortems should be blameless and data-driven
• Logs, metrics, and traces must be time-aligned and retained
• Traceability helps identify what failed and why

“A postmortem is a written record of an incident, its impact, the actions taken to mitigate or resolve it, the root cause(s), and the follow-up actions to prevent the incident from recurring.” — Site Reliability Engineering, Chapter 15 📘 Postmortem Culture

Practices to Apply

Final Thoughts

Observability is not a box to check — it’s the backbone of sustainable, scalable reliability engineering. The SRE book shows that with the right telemetry and culture, teams can build systems that fail gracefully and recover fast.

📘 Further Reading:

• Site Reliability Engineering Book Collection
  • SRE Workbook
  • Site Reliability Engineering
  • Building Secure & Reliable Systems
• Best Practices for Production Services