Next Halloween I’m dressing as O(n^2)
Blog post from Sentry
A recent incident highlighted the pitfalls of O(n^2) algorithms in production, as a step in a size analysis process for mobile apps unexpectedly increased from seconds to 12 minutes. This slowdown was attributed to inefficient deobfuscation logic in Python, which led to billions of iterations due to the quadratic growth of operations as the number of classes increased. After optimizing the code with better data structures, the runtime was reduced significantly. A similar issue occurred in an iOS app analysis, where a cache was not being utilized, resulting in a slow O(n^2) operation in a C++ library. The problem was exacerbated by an unusual increase in data size, revealing the dormant inefficiency. These cases underscore the importance of scrutinizing and optimizing O(n^2) algorithms, which can be deceptively fast in testing but problematic in production, aligning with performance expert Bruce Dawson's insights on poorly scaling algorithms.
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