Big O Notation of your app
Even though you probably spent 5 years studying Computer Science and extensively using asymptotic notation, the truth is in the mobile development world there is a common myth about running time and space requirements being useless.
Modern devices are way more powerful for users to notice a difference between bubble sort and merge sort. Or not? Should everyone know how to implement Ukkonen's algorithm if they develop a weather app? What's the "Big O" of your average app and how to determine it?
I have answers to these questions and by the end of the talk you will have them too.
What is this talk about?
Whether algorithmic complexity matters in everyday iOS development: when the difference between bubble sort and merge sort is invisible on a modern iPhone, when it is not, and how to estimate the Big O of your own app. Recorded at NSSpain 2020.
Does the talk assume a computer science background?
No - it starts from what Big O is (and how Big θ differs), walks the complexity ladder from O(1) to O(n!), and spends most of its time on examples taken from real app work.
Where does Big O show up in a normal iOS app?
The examples include storing API data, path-finding to draw connections between objects, snapping an object to the nearest edge, and using geohashes to speed up local storage - the talk works out the complexity of each.
Where can I watch it?
The full video is embedded at the top of this page, and the slide deck (PDF) is in the resources section.
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