Big O Space Complexity Calculator
Estimate peak memory use for a space complexity class at a given input size.
About Big O Space Complexity Calculator
Space complexity often decides feasibility before time complexity does, because exceeding memory fails outright rather than merely running slowly. Quadratic space becomes impractical at input sizes linear space handles easily. Streaming and in-place algorithms trade time for constant space, which is what makes very large inputs processable at all.
Formula
Big O Space Complexity
Memory = space growth function of n × bytes per unit
- auxiliary space
- Extra memory beyond the input itself
Worked example
With the default values below, Big O Space Complexity Calculator returns Estimated Memory Use of 7.6294. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Input size nminimum 1
- 1,000,000
- Space complexity classone of O(1) constant, O(log n) logarithmic, O(n) linear, O(n log n) linearithmic, O(n squared) quadratic, O(n cubed) cubic, O(2 to the n) exponential
- O(n) linear
- Bytes per unit of spaceminimum 1 bytes
- 8 bytes
- Available memoryminimum 0.001 MB
- 512 MB
Results
- Estimated Memory Use
- 7.6294 MB
- Estimated Memory Use
- 8,000,000 bytes
- Share of Available Memory Used
- 1.490%
- Largest Input That Fits
- 67,108,864
- Verdict
- Fits comfortably in memory
Step by step
- 1Values used
- 2Big O Space Complexity
- 3Estimated Memory Use= 7.6294 MB
- 4Estimated Memory Use= 8,000,000 bytes
- 5Share of Available Memory Used= 1.490
- 6Largest Input That Fits= 67,108,864
- 7Verdict= Fits comfortably in memory
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 4 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
How is Big O Space Complexity calculated?
Memory = space growth function of n × bytes per unit Space complexity often decides feasibility before time complexity does, because exceeding memory fails outright rather than merely running slowly. Quadratic space becomes impractical at input sizes linear space handles easily.
Why does Big O Space Complexity matter?
Streaming and in-place algorithms trade time for constant space, which is what makes very large inputs processable at all.
What values do I need to enter?
This calculator takes 4 inputs: Input size n, Space complexity class, Bytes per unit of space, Available memory. The pre-filled defaults are a realistic starting point — replace them with figures from your own environment for a result you can act on.