📊 System Design: Capacity Estimation (Deep Dive)

⚡ Quick Dive

Overview & Key Takeaways

Capacity estimation helps you predict the resources your system will need to handle expected load, prevent downtime, and plan for scaling.

🎯 Why Capacity Estimation Matters

  • Avoids underprovisioning (crashes) and overprovisioning (wasted cost)
  • Helps choose storage engines, compute resources, and databases wisely
  • Provides confidence in meeting SLAs and SLOs
  • Sets a solid baseline for autoscaling or load balancing

🧠 Inputs to Estimate

  1. Traffic Load

📖 Extended Guide

Capacity estimation helps you predict the resources your system will need to handle expected load, prevent downtime, and plan for scaling.

🎯 Why Capacity Estimation Matters

  • Avoids underprovisioning (crashes) and overprovisioning (wasted cost)
  • Helps choose storage engines, compute resources, and databases wisely
  • Provides confidence in meeting SLAs and SLOs
  • Sets a solid baseline for autoscaling or load balancing

🧠 Inputs to Estimate

  1. Traffic Load

    • Requests per second (RPS) or queries per second (QPS)
    • Daily Active Users (DAU) / Monthly Active Users (MAU)
    • Peak load vs average load

  2. Data Size

    • Average payload size per request/response
    • Daily data growth rate (e.g., logs, user uploads, analytics)

  3. Retention Requirements

    • How long data needs to be stored (e.g., 30 days, 6 months, forever)

  4. Processing Overhead

    • CPU usage per request
    • Memory footprint per user/session

  5. Network Throughput

    • Bandwidth required for serving requests
    • Ingress/Egress load between services

🔍 Example Questions to Ask

- What is the expected RPS at launch?
- What’s the peak traffic multiplier? (e.g., 5x daily average)
- How big is each write or read operation in bytes?
- How long do we store user logs, uploads, or sessions?
- How many database read/write ops per second are expected?

📐 Estimation Formula Examples

📦 Storage Estimation

Daily Storage = Requests per Day × Payload Size  
Total Storage (N days) = Daily Storage × Retention Period

Example:
1,000,000 requests/day × 2 KB = ~2 GB/day  
Retention = 90 days → 180 GB total

🧮 Compute Estimation

Requests per second = Total Requests per Day / 86,400

If each request takes 20ms of CPU:
  CPU Load = RPS × 20ms
  = 1000 RPS × 20ms = 20 CPU cores

Add headroom (~30%) → 26 cores

🔄 Database IOPS Estimation

- Read Ops = RPS × Read ops/request
- Write Ops = RPS × Write ops/request

Add 50% overhead for replication/indexing

⚙️ Tools & Techniques

  • Spreadsheets – Manual calculations for traffic, storage, CPU
  • Load Testing Tools – Apache JMeter, k6, Locust
  • Monitoring Baselines – Use real metrics if available (Grafana, Datadog)
  • Capacity Planning Sheets – Structured templates to track estimates

🧱 Capacity Estimation Framework

1. Estimate baseline RPS / QPS
2. Factor in peak load (e.g., 2x or 10x burst traffic)
3. Estimate per-request data and processing size
4. Project data growth (daily, monthly, yearly)
5. Estimate backend capacity (DB IOPS, bandwidth)
6. Add buffer (25–50%) for safety and growth

💡 Pro Tips

  • Always plan for peak, not average.
  • Use real usage data from similar apps when possible.
  • Leave room for future features and traffic spikes.
  • Validate assumptions with engineers and business teams.
  • Revisit estimations as product evolves or usage patterns shift.
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