Capacity Planning — Notes

Interview script

1. Clarify scale: users, MAU, DAU, geography.
2. Estimate avg + peak QPS.
3. Estimate storage (raw + replication factor + retention + indexes).
4. Estimate bandwidth (read-out per request × QPS).
5. Estimate compute: per-server cap → server count + 30% headroom.
6. Tier storage (hot/warm/cold).
7. Identify bottleneck → that's where the architecture diagram lives.

Quick conversions

• 1 day = 86,400 s ≈ 10⁵.
• 1 year ≈ 3.15 × 10⁷ s.
• 1 GB = 10⁹ B (decimal) or 2³⁰ ≈ 1.07 × 10⁹ B (binary). Both close enough.
• 1 Gbps ≈ 125 MB/s.

Common ratios

• DAU/MAU: 10-30% (social), 60-80% (essential — banking, email).
• Read:Write: 100:1 (content), 10:1 (cart), 1:1 (chat), 0.1:1 (analytics ingest).
• Avg:Peak: 3-5× for consumer; up to 10× for retail seasonal.

Refs

• Jeff Dean: "Numbers every developer should know" (Google ~2010).
• "Designing Data-Intensive Applications" — capacity throughout.
• Mark Brooker AWS blog (load balancing, capacity, queues).
• "Site Reliability Engineering" — chapters on capacity planning.