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Cache Size For Hit Ratio Calculator

Work out cache size for hit ratio instantly with clear inputs, formula shown and shareable results.

About Cache Size For Hit Ratio Calculator

Access patterns are typically Zipf distributed, so a small share of keys serves most requests. Under a Zipf exponent alpha, reaching hit ratio h needs roughly h^(1/alpha) of the key space, which for alpha near 1 means about 90 percent of keys for a 90 percent hit ratio but far fewer for a more skewed workload. Per-entry metadata of around 60 bytes is added because it dominates for small values.

Formula

Zipf cache sizing

keys needed ~ total keys x target^(1/alpha); size = keys x (value bytes + per-entry overhead)

Worked example

With the default values below, Cache Size For Hit Ratio Calculator returns Cache size required of 6.05 GiB. Change any input above and every figure here is recalculated in your browser.

Inputs used

Distinct keys in the working setminimum 1
5,000,000
Target hit ratioaccepts 1–99.99 %
90 %
Average value sizeminimum 1 bytes
1,400 bytes
Zipf skew exponentaccepts 0.1–2
0.9

Results

Cache size required
6.05 GiB
Keys to hold in cache
4,447,627
Share of the key space cached
88.95%
Assumed per-entry overhead
60 B

Step by step

  1. 1Values used
  2. 2Zipf cache sizing
  3. 3Cache size required= 6,493,535,420
  4. 4Keys to hold in cache= 4,447,627
  5. 5Share of the key space cached= 88.95
  6. 6Assumed per-entry overhead= 60

Assumptions and limits

  • The result is derived only from the 4 inputs listed above. Anything not among them is not part of the calculation.
  • In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
  • Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

Why does the skew exponent matter so much?

It determines how concentrated the popularity is. At alpha 1.2 a few percent of keys serve most traffic; at alpha 0.5 popularity is nearly flat and caching helps far less.

How do I measure the real curve?

Log key accesses for a period and plot the cumulative hit ratio against cache size. That empirical curve beats any analytical model.

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