Loops that blow up with big data

Identify potentially quadratic or worse operations in frequently used code paths, including nested joins in application memory, copy-on-every-iteration patterns and repeated scans. Determine realistic input sizes and benchmark the highest-impact candidates before changing their algorithms.

What it returns

Each candidate with realistic input size and a benchmark at that size; ignore candidates whose real inputs stay small.

Skip it when

Code whose inputs are small and capped by design.

Changes

Changes nothing

Sessions

One session

Tools

A speed-test setup. The experimental agent-complexity-optimizer scanner can suggest places to look.

Watch for

Static scanners flag nested loops over tiny arrays. Without real sizes the list is mostly noise.

Also called

Growth complexity analysis

More in Performance and scale.