Work the growth curve before optimizing
Estimate how operation counts change across common Big-O classes.
A script checks 100,000 imported customer rows for duplicate IDs by comparing each row with every other row. Complexity estimate: identify n, name the repeated operation, compare the growth shape, then choose a structure that changes the repeated operation. The common trap is to benchmark only the 1,000-row QA file and tune syntax while leaving the pairwise O(n^2) shape intact. Step 1 Name n: the production file has n = 100,000 rows. Complexity analysis starts by naming the input that drives growth. Here it is rows, not columns or files. Step 2 Name the operation: for each row, compare its…
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