Identify and optimize resource-intensive operations in frequently executed code paths. This includes avoiding memory-doubling operations like `copy.deepcopy()` on large objects, preventing blocking I/O in async contexts, and eliminating expensive debug logging operations. Key areas to review: - **Memory operations**: Avoid deep copying large objects without considering alternatives like in-place modification or streaming - **Async contexts**: Move blocking operations (file I/O, environment variable reads) to startup initialization rather than request-time execution - **Debug logging**: Avoid expensive serialization operations, especially `json.dumps()` on large objects in hot paths - **Unbounded growth**: Implement limits on data structures to prevent memory leaks Example from the discussions: ```python # Problematic - doubles memory usage redacted_response = copy.deepcopy(response_json) # Better - consider in-place modification or streaming for large objects # Or move expensive operations out of request path # Problematic - blocking I/O in async context session = ClientSession(trust_env=True) # Reads files synchronously # Better - read environment at startup proxy_settings = get_environment_proxies() # At startup session = ClientSession(proxies=proxy_settings) # At request time ``` Always profile and measure the impact of operations in performance-critical paths, especially when dealing with large data or high-frequency execution.