performance-profiler

To find performance bottlenecks in Node.js, Python, or Go, you profile the application to measure CPU, memory, and I/O metrics. This process reads project dependencies and outputs a structured profile with load-test indicators.

Flamegraph profiling for Python and Go works by sampling call stacks to visualize CPU usage and execution time. It uses py-spy for Python and pprof for Go to pinpoint exact functions consuming resources.

Yes, you can detect memory leaks and analyze heap snapshots. Memory profiling examines GC pressure and heap allocations to identify slow memory growth in Node.js, Python, and Go applications.

Yes, bundle size analysis supports webpack and Next.js. It analyzes bundle weights to identify heavy dependencies and optimize frontend payload sizes for multi-service systems.

The best way to run load tests for slow endpoints is generating k6 scripts and Artillery scenarios with ramp-up patterns. This applies load to measure endpoint performance and verify optimizations.

You need to establish a baseline before optimizing application performance to ensure systematic before and after measurement. Profiling reads project files to establish this baseline, verify optimizations, and prevent regressions.