Big O Notation Surprise: From Linear to Logarithmic
Description
A surreal, deep-fried multi-panel meme that jokes about algorithmic complexity. In the first panel, a distorted frog asks, 'MOM CAN YOU GIVE ME LOOP'. The second panel shows a woman with glasses asking, 'FOR O(N)?'. In the third panel, the frog replies, 'YEES'. The fourth panel subverts the expectation, showing another frog with the text, 'ACTUALLY USES IT FOR O(LOG(N)) LIKE A BOSS'. The final, bottom panel shows a stretched frog next to a block of complex mathematical equations with the caption 'COMPLEXITY TIME'. The humor stems from the subversion of expectations in a technical context. A standard loop is often associated with linear O(n) time complexity. The developer (represented by the frog) cleverly implements a much more efficient logarithmic O(log n) algorithm, a move that is considered a mark of a skilled programmer. The meme celebrates the satisfaction of finding an optimized solution where a simpler, less efficient one was expected
Comments
19Comment deleted
The difference between a junior and a senior dev: a junior sees a loop and thinks O(n), a senior sees a loop and immediately wonders if they can get away with O(log n)
“Nice flex turning the hot loop into O(log N)… now do the part where we stop hitting the database N times so your algorithmic virtue doesn’t stay I/O-bound.”
When you tell the PM it'll take O(n) time to search the database but secretly implement a B-tree index because you know they'll add 'just one more requirement' that would've made the linear scan crawl to a halt in production
Ah yes, the classic developer move: confidently optimizing from O(n) to O(log n) only to discover your 'elegant' solution has introduced so many edge cases, nested conditionals, and auxiliary data structures that the constant factors now dominate runtime for any dataset under 10 million records. Meanwhile, the naive O(n) implementation your junior colleague wrote runs faster in production because it fits in L1 cache. But hey, at least your code review comments about Big O notation made you look smart before the performance regression tickets started rolling in
Ask for O(n), ship for(i=1;i<n;i*=2); then burn O(n) minutes debating the log base in code review
Ask for a loop vaguely; let stakeholders assume O(n!) doom while you drop O(log n) elegance
Turned a "just add a loop" request into O(log n); felt like a boss - until the flame graph showed 93% spent on JSON parsing and TLS handshakes
Can anybody explain it? Comment deleted
Algorithm optimization Comment deleted
https://www.freecodecamp.org/news/big-o-notation-why-it-matters-and-why-it-doesnt-1674cfa8a23c/ Comment deleted
It was quite hard to read it Comment deleted
How could he use O(n) algorithm as O(log n)? Is that quick sort, or what? Comment deleted
Perhaps it is probabilistic algorithm. Simple classic one, hiring problem (or in another words finding maximum value linearly), makes use of the random distribution that turns expected average from O(n) to O(logn) Comment deleted
Woah Comment deleted
You see, if the probability distribution of values are uniform, then with first pick we have 1/n chance of hitting max, then 1/n-1 and so on. We notice that we have the Harmonic series, which is bounded by logn Comment deleted
So, if you “randomly” permute the array before feeding into such algorithm, you can turn linear expected average to logarithmic Comment deleted
Permuting is O(n) :P Comment deleted
You need to pick random offsets each iteration Comment deleted
And presumably keep a hashmap of which ones are visited already making complexity even higher Comment deleted