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Measured optimization of backends under real traffic: latency, throughput and cold starts.
Performance work starts with measurement, not opinion. On a marketplace API we cut Lambda cold starts from about 8 seconds to about 1.5 by introducing esbuild bundling, pruning dependencies and lazy-loading SDK clients; rewrote the slowest MySQL queries behind large order listings; and replaced an operation that took hours with Painless scripts updating thousands of OpenSearch documents in minutes. The method is always the same: profile, fix the biggest cost, prove the gain, repeat.
Profile the real system under real traffic. Numbers first.
The slowest query and the heaviest endpoint first, not the most interesting one.
One change at a time, each verified against the baseline.
Alerts and budgets so the regression is caught before your users catch it.
By measuring where the time goes: profiling and tracing before any code changes. The bottleneck is rarely where intuition says.
Usually, and substantially: bundling, dependency pruning and lazy loading took the services we worked on from about 8s to about 1.5s.
Often the same work: smaller bundles, fewer wasted invocations and right-sized resources cut both latency and the bill.
The test suite runs against every optimization, and risky rewrites get characterization tests first.
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