Tutorials System Design Tutorial
Auto Scaling Strategies — Complete Guide
Auto Scaling Strategies — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of System Design Tutorial on Toolliyo Academy.
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System Design Tutorial · Lesson 55 of 100
Auto Scaling Strategies
Basics ✓ → Scale → Interview
Scale · 2 — Distributed · ~10 min · Module 6: Cloud-Native Architecture
What is this?
Auto scaling adds/removes capacity from metrics — CPU, RPS, queue depth, custom business SLOs.
Why should you care?
ShopNest should grow for flash sales and shrink overnight to save cost.
See it live — copy this example
Sketch the architecture on paper. These lessons focus on concepts and trade-offs.
HPA: CPU 55% or RPS per pod
Worker scale: queue depth > 1000 → more consumers
Cooldown: avoid flapping
Scale to zero only for truly intermittent workersRun Example »
This lesson uses terminal or setup steps. Run commands on your computer — the live editor appears on coding lessons.
What happened?
- Pick metrics that track user pain.
- Cooldowns stop thrashing.
- Scale databases differently (often vertically or via replicas, not blind pod counts).
Practice next
- Choose CPU+RPS for ShopNest API HPA.
- Scale email workers on queue depth.
- Set min replicas > 0 for checkout.
- Add scale-on p95 latency if supported.
- Schedule higher mins during known sale windows.
Remember
Metric must match bottleneck. Mins for critical paths. Test scale speed.
Sale-day HPA
ShopNest APIs scale out on RPS.
Outcome: Capacity tracks demand without overnight waste.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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