performance optimization When Using Python Dictionary for Large Data Sets with Frequent Updates
After trying multiple solutions online, I still can't figure this out. After trying multiple solutions online, I still can't figure this out... I'm currently working on a Python application using version 3.9, where I need to maintain a dictionary that stores user session data. The data structure is simple, with user IDs as keys and dictionaries containing session info as values. However, I've noticed important performance degradation when I frequently update this dictionary, particularly when the number of users exceeds 10,000. Here's a simplified version of what I'm doing: ```python user_sessions = {} def update_session(user_id, session_data): if user_id in user_sessions: user_sessions[user_id].update(session_data) else: user_sessions[user_id] = session_data ``` The `update` method works fine, but I am experiencing noticeable latency when the dictionary grows larger. I've tried using `collections.defaultdict`, but it didn't seem to improve performance. I have also considered using a different data structure like a list or a custom class, but that would require important changes to my existing logic. Is there a more efficient way to handle frequent updates to a large dictionary? Should I batch my updates or perhaps utilize a different approach altogether? Any insights into optimizing performance for this scenario would be greatly appreciated! What's the best practice here? For context: I'm using Python on macOS. Any ideas what could be causing this?