vp-q02-performance

To find performance bottlenecks across code paths, apply a mental-model-driven workflow to detect N+1 queries, missing indexes, expensive queries, and async blocking operations in multi-layered applications. It prioritizes hotspots based on dependency mappings.

Profiling expensive queries and blocking operations involves mapping findings to predefined mental-model constraints across API, DB, and ORM layers. This surfaces hotspots and guides targeted optimizations for latency and scalability issues.

Detect missing indexes by applying mental-model constraints to your codebase with multiple data access layers. The workflow identifies these performance hotspots and maps them to the corresponding dependencies for prioritized optimization.

Yes, you need layer architecture, entity model, and dependency graph dependencies to analyze concurrency and performance. These dependencies are required to map detected performance hotspots to the corresponding code paths and structural layers.

This performance analysis workflow is designed for codebases with multiple data access layers, APIs, and concurrency. Applying it to a simple single-layer API without complex concurrency may not yield meaningful hotspot prioritization or mapping results.

Async blocking happens when concurrent operations are mismanaged across data access layers. You can identify it by applying predefined mental-model constraints during code path analysis to surface these specific performance hotspots.