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Level 9 / Project 05 - Capacity Planning Model

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— This project — — Flashcards — —

Focus

  • Growth modeling: linear, exponential, and step-function curves
  • Strategy pattern for pluggable growth curve selection
  • Resource profiling with current usage and capacity limits
  • Forecast generation with headroom recommendations
  • What-if scenario analysis for capacity decisions

Why this project exists

Capacity planning prevents outages by projecting resource needs before demand exceeds supply. A service growing at 15% month-over-month will exhaust its database connections in 8 months — but without a model, the team only discovers this during a production incident. This project models compute, storage, and bandwidth growth using configurable curves and generates capacity forecasts with months-until-exhaustion calculations — the same approach infrastructure teams use at every major tech company.

Run (copy/paste)

cd <repo-root>/projects/level-9/05-capacity-planning-model
python project.py --demo
pytest -q

Expected terminal output

{
  "resources": [...],
  "forecasts": [...],
  "months_until_exhaustion": {"compute": 14, "storage": 8, ...}
}
7 passed

Expected artifacts

  • Console JSON output with capacity forecasts
  • Passing tests
  • Updated notes.md

Alter it (required)

  1. Add a seasonal growth function that models periodic spikes (e.g. Black Friday traffic).
  2. Add a --chart flag that outputs a text-based ASCII chart of the capacity forecast.
  3. Add cost estimation — multiply forecasted usage by per-unit cost to project spend.

Break it (required)

  1. Set growth_rate=0 for exponential growth — does the forecast handle it?
  2. Create a resource profile with capacity < current_usage — is it flagged as already exhausted?
  3. Pass months=0 to forecast() — does it return an empty forecast or error?

Fix it (required)

  1. Validate that growth_rate > 0 for exponential models.
  2. Add "already exhausted" detection for resources that are over capacity at month 0.
  3. Validate months >= 1 in the forecast method.

Explain it (teach-back)

  1. What is capacity planning and why is it critical for infrastructure teams?
  2. How do linear, exponential, and step growth functions differ in real-world modeling?
  3. What is the "months until exhaustion" calculation and why is it a key metric?
  4. How do what-if scenarios help teams decide when to add infrastructure?

Mastery check

You can move on when you can: - explain the difference between linear and exponential growth forecasting, - run a what-if scenario that compares current vs optimized resource profiles, - describe how capacity planning prevents outages vs reactive scaling, - add a new growth model without modifying existing forecast logic.

Mastery Check

  • Can you explain the architectural trade-offs in your solution?
  • Could you refactor this for a completely different use case?
  • Can you identify at least two alternative approaches and explain why you chose yours?
  • Could you debug this without print statements, using only breakpoint()?
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