How Replacing Nested Loops with Hash Sets Cut a Batch Job from 4 Minutes to 200ms

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A software developer discovered that a duplicate-checking function using two nested loops, which runs in O(n²) time, caused a nightly batch job to freeze for several minutes when processing a million-record dataset. The root cause was that the quadratic approach required roughly five billion comparisons for large inputs, consuming 90% of CPU and triggering server alerts. Replacing the nested loops with a hash-based Set reduced the algorithm to O(n) linear time, since each insertion and membership check on a Set is amortized constant-time. The optimized function completed the same batch job in under 200 milliseconds, compared to the previous four-minute runtime. The case illustrates why understanding Big-O notation allows developers to reason about performance before writing code, rather than discovering bottlenecks only under production load.

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

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