Space Complexity Estimator Calculator

Work out space complexity estimator instantly with clear inputs, formula shown and shareable results.

About Space Complexity Estimator Calculator

Space complexity counts how much auxiliary memory an algorithm needs as the input grows. Multiplying the item count by the bytes per item gives a concrete figure to compare against available RAM.

Formula

Memory estimate

bytes ≈ items(n) × bytes per item

Worked example

With the default values below, Space Complexity Estimator Calculator returns Estimated memory of 7.629395. Change any input above and every figure here is recalculated in your browser.

Inputs used

Input size nminimum 1: 1,000,000
Bytes per itemminimum 1: 8
Space complexityone of O(1) auxiliary, O(log n), O(n), O(n log n), O(n²): O(n)

Results

Estimated memory: 7.629395 MB
In bytes: 8,000,000
In gigabytes: 0.00745058
Items held: 1,000,000

Step by step

1Values used
2Memory estimate
3Estimated memory= 7.629395 MB
4In bytes= 8,000,000
5In gigabytes= 0.00745058
6Items held= 1,000,000

Assumptions and limits

The result is derived only from the 3 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 8 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

Linear space for a million 8-byte items?

8 MB, comfortably in cache-adjacent memory.

Why avoid O(n²) space?

A million items would need 8 terabytes. Quadratic space becomes infeasible far sooner than quadratic time.