(Up-to-date source of this post.)
- a mathematical way of describing scaling
- used to classify a system based on how it responds to changes in input size
- O is used because the growth rate of an algorithm's run-time is known as its order
Sub-linear scaling
- O(1) - constant - no matter the scale of the input, performance of the system does not change (ex. hash-table lookup in RAM; such algorithms are rare)
- O(log n) - logarithmic; ex. binary search grows slower as the size of the corpus being searched grows, but less then linearly
Linear scaling
- O(n) linear - ex. twice as much data requires twice as much processing time
Super-linear scaling
- O(n^m) - exponential - as input size grows the system slows down disproportionately
- O(n^2) - quadratic (but everybody says exponential when they mean quadratic)
Resources
- Practice of Cloud System Administration, Appendix C
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