Backend Code Review (Python/FastAPI)

Finding blocking calls in async FastAPI routes requires static AST analysis to scan data access patterns and dependencies, surfacing synchronous I/O operations that degrade throughput in a structured report.

Concurrency hazards in Python FastAPI backends stem from architectural anti-patterns like global DB sessions and improper dependency injection, creating shared state risks and long-running async bottlenecks.

Auditing a FastAPI service for async performance bottlenecks involves analyzing routes and data access patterns to identify long-running async functions and inefficiencies, producing a structured remediation report.

Static analysis detects improper dependency injection in FastAPI by scanning the abstract syntax tree to highlight architectural anti-patterns and improper DI usage within your backend codebase.

AST analysis works with Async SQLAlchemy and HTTP clients by scanning codebases leveraging these libraries to surface blocking calls and architectural issues across data access patterns.

Review your FastAPI backend architecture for logic quality gaps when preparing to improve throughput and reliability, ensuring no long-running async functions or concurrency risks impact performance.