Bloom Filter Size Calculator
Size a standard Bloom filter for an entered insertion count and target approximate false-positive probability.
YOUR CALCULATION
Result
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Calculation steps
Work through one step at a time or reveal the complete calculation. The steps use your current inputs.
How to calculate
Choose a whole-bit array and positive whole hash count, then report the finite-size exponential false-positive approximation.
m=ceil(−n ln(p)/(ln 2)²); k=round((m/n)ln 2); actual p≈(1−exp(−kn/m))^k
Worked example
For 100000 items and target probability 0.01, the array needs 958506 bits and seven hash probes, about 119814 whole bytes.
Assumptions and input details
Ideal independent uniform hashes and a standard insert-only Bloom filter. No deletion, exact membership, storage overhead, hash cost or adversarial false-positive guarantee. The displayed rate is an approximation.
Using this calculator
Can I enter my own problem?
Yes. Replace the example values with your own inputs. The result, calculation steps, tables and applicable diagrams update from those inputs. Blank or invalid values clear the result; example values are never silently substituted for missing inputs.
Are the results exact?
Results use floating-point arithmetic and displayed rounding. Read the formula, assumptions and any stated data precision before interpreting the result.
Is my input uploaded?
The calculation runs in your browser. No account, file upload or external AI service is required. Inputs are not saved between page reloads.
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