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Guided Lab Brief

Capacity Estimation Drill

Translate traffic assumptions into concrete compute, cache, and storage sizing decisions.

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Overview

Translate traffic assumptions into concrete compute, cache, and storage sizing decisions.

Capacity math should shape architecture before implementation.

You will build 6 architecture steps that model production dependencies.

You will run 1 failure experiment to observe bottlenecks and recovery behavior.

Success target: Tier budgets hold under peak load with measurable safety margin.

Learning Objectives

  • Can convert assumptions into tier-level capacity numbers
  • Can justify headroom and cache targets quantitatively
  • Can discuss capacity risks using p95 and saturation metrics

Prerequisites

  • Topic Hub: RPS budgeting
  • Chapter 28: Capacity Estimation
  • Prior Lab: Your First System

Experiments

  1. Reduce API instances below required headroom to simulate saturation

Failure Modes to Trigger

  • Trigger: Reduce API instances below required headroom to simulate saturation

    Observe: Single instance saturates CPU and queueing delay drives p95 latency out of SLO.

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