mm-perf

To find N+1 queries and slow functions, performance profiling analyzes code execution paths and database queries to detect missing indexes, inefficient joins, and nested loops. It requires ast and regex libraries for static code analysis.

Yes, performance profiling supports JavaScript and TypeScript codebases, alongside Python. It analyzes code execution paths to identify inefficient allocations and nested loops using ast and regex libraries.

The best way to get a surface scan of database performance bottlenecks is to run the profiler with a quick flag. This initiates a rapid scan to detect missing indexes, inefficient joins, and N+1 queries across your codebase.

To detect inefficient allocations during code profiling, the tool analyzes your code's execution paths using ast and regex libraries. It identifies memory bottlenecks and nested loops to help optimize overall system performance.

Yes, database query analysis works for Python applications to detect missing indexes and inefficient joins. It identifies N+1 queries and database performance bottlenecks by evaluating query patterns within your Python codebase.

Code profiling requires ast and regex libraries to parse and analyze code execution paths statically. These dependencies allow the profiler to accurately identify slow functions, nested loops, and inefficient allocations without executing the code.