avoid expensive operations

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:

# 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.