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use context managers concurrency

commaai/openpilot
Based on 5 comments
Python

When working with concurrent operations involving shared resources or processes, use context managers and explicit synchronization to ensure proper cleanup and thread safety. This prevents resource leaks, race conditions, and ensures graceful shutdown even when operations are interrupted.

Concurrency Python

Reviewer Prompt

When working with concurrent operations involving shared resources or processes, use context managers and explicit synchronization to ensure proper cleanup and thread safety. This prevents resource leaks, race conditions, and ensures graceful shutdown even when operations are interrupted.

For process management, wrap subprocess operations in context managers instead of relying on atexit handlers:

# Instead of atexit.register()
with ProcessManager() as pm:
    xvfb_proc = pm.start_process(xvfb_cmd, env)
    ui_proc = pm.start_process(ui_cmd, env)
    # processes automatically cleaned up on exit

For shared resources accessed by multiple threads, use explicit locking:

class AudioProcessor:
    def __init__(self):
        self.lock = threading.Lock()  # Add a lock for thread safety
        self.sound_data = []
    
    def process_audio(self, data):
        with self.lock:
            self.sound_data.append(data)

When adding concurrency to existing APIs, maintain backward compatibility by encapsulating threading details within the implementation rather than exposing them to consumers. This allows you to improve responsiveness (like preventing UI freezing) without breaking existing code that depends on the original synchronous interface.

5
Comments Analyzed
Python
Primary Language
Concurrency
Category

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