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How do you approach back-of-the-envelope capacity estimation?

Q: How do you approach back-of-the-envelope capacity estimation?

## Back-of-the-envelope estimation The goal isn't precision — it's an **order-of-magnitude** sanity check that tells you whether you need one box or a thousand, and where the bottleneck is. Work top-down from users to load: DAU requests per day avg QPS <rect class='d-box-accent' x='400' y='52' width='7

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DAU = 100_000_000
actions_per_user = 20
seconds_per_day = 86_400
peak_factor = 3

requests_per_day = DAU * actions_per_user
avg_qps  = requests_per_day / seconds_per_day
peak_qps = avg_qps * peak_factor

new_items_per_day = 1_000_000
bytes_per_item = 1_000                       # ~1 KB
storage_5y_tb = new_items_per_day * bytes_per_item * 365 * 5 / 1e12

print("avg QPS        :", round(avg_qps))
print("peak QPS       :", round(peak_qps))
print("5-year storage :", round(storage_5y_tb, 1), "TB")

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