Tr/polling

[mrT23]

PR Type

Enhancement

---

Description

• Implemented synchronous comment processing using multiprocessing to handle async tasks more efficiently
• Added detailed logging for better traceability and debugging throughout the polling process
• Improved notification handling by introducing is_valid_notification function to validate and process notifications
• Enhanced comment processing by checking previous comments when the latest comment doesn't contain the user tag
• Adjusted polling interval from 5 to 3 seconds for more frequent checks
• Removed unused background task manager and related functions
• Added function to mark notifications as read after processing
• Updated error handling and logging throughout the script

---

Changes walkthrough 📝

	Relevant files
Enhancement	github_polling.py Enhance GitHub polling with synchronous processing and improved logging pr_agent/servers/github_polling.py Implemented synchronous comment processing using multiprocessing Added detailed logging for better traceability Improved notification handling and validation Adjusted polling interval and removed unused background task manager +151/-40

---

💡 PR-Agent usage:
Comment /help on the PR to get a list of all available PR-Agent tools and their descriptions

[qodo-merge-pro]

PR Reviewer Guide 🔍

⏱️ Estimated effort to review: 4 🔵🔵🔵🔵⚪

🏅 Score: 85

🧪 No relevant tests

🔒 No security concerns identified

🔀 Multiple PR themes Sub-PR theme: Implement multiprocessing for comment processing Relevant files: pr_agent/servers/github_polling.py Sub-PR theme: Enhance notification handling and validation Relevant files: pr_agent/servers/github_polling.py Sub-PR theme: Improve logging and error handling in polling process Relevant files: pr_agent/servers/github_polling.py

⚡ Key issues to review Performance Concern The is_valid_notification function makes a synchronous HTTP request inside an async function, which could potentially block the event loop. Error Handling The process_comment_sync function catches all exceptions but doesn't provide specific error handling for different types of errors. Code Duplication There are two similar functions process_comment_sync and process_comment with overlapping functionality. Consider refactoring to reduce duplication.

[mrT23]

/improve

[qodo-merge-pro]

PR Code Suggestions ✨

Latest suggestions up to 85754d2

Category	Suggestion	Score
Performance	Use asyncio tasks instead of multiprocessing for better performance with I/O-bound operations Instead of using multiprocessing.Process for parallel tasks, consider using asyncio.create_task() to create and manage concurrent tasks within the same event loop. This approach is more efficient for I/O-bound operations and avoids the overhead of creating separate processes. pr_agent/servers/github_polling.py [211-224] -processes = [] -for i, func, args in enumerate(task_queue): # Create parallel tasks - p = multiprocessing.Process(target=func, args=args) - processes.append(p) - p.start() - if i > max_allowed_parallel_tasks: +tasks = [] +for i, (func, args) in enumerate(task_queue): + if i >= max_allowed_parallel_tasks: get_logger().error( f"Dropping {len(task_queue) - max_allowed_parallel_tasks} tasks from polling session") break + task = asyncio.create_task(func(*args)) + tasks.append(task) task_queue.clear() +# Optionally, wait for tasks to complete +# await asyncio.gather(*tasks) + Apply this suggestion Suggestion importance[1-10]: 9 Why: This suggestion aligns well with the asynchronous nature of the code and can significantly improve performance for I/O-bound operations.	9
	Reuse a single PRAgent instance across all requests to improve performance Instead of creating a new PRAgent instance for each comment, consider creating a single instance and reusing it across all requests to improve performance and resource usage. pr_agent/servers/github_polling.py [65-70] -agent = PRAgent() -success = await agent.handle_request( +success = await self.agent.handle_request( pr_url, rest_of_comment, notify=lambda: git_provider.add_eyes_reaction(comment_id) ) Apply this suggestion Suggestion importance[1-10]: 8 Why: This suggestion can significantly improve performance by reducing object creation overhead, especially for frequent requests.	8
	Use a more efficient data structure for handling processed notification IDs Consider using a more efficient data structure for handled_ids. A set is a good choice for fast lookups, but if you need to maintain order or have a maximum size, consider using collections.OrderedDict as a bounded set. pr_agent/servers/github_polling.py [145] -handled_ids = set() +from collections import OrderedDict +handled_ids = OrderedDict() +MAX_HANDLED_IDS = 1000 # Adjust as needed Apply this suggestion Suggestion importance[1-10]: 6 Why: The suggestion to use OrderedDict is valid and can improve efficiency, but the current implementation with a set is already quite efficient for lookups.	6
Best practice	Implement a rate limiter for API requests to avoid hitting rate limits Consider implementing a rate limiter to avoid overwhelming the GitHub API with requests. This can help prevent hitting API rate limits and ensure smooth operation of the polling loop. pr_agent/servers/github_polling.py [180] -async with session.get(NOTIFICATION_URL, headers=headers, params=params) as response: +async with self.rate_limiter: + async with session.get(NOTIFICATION_URL, headers=headers, params=params) as response: Apply this suggestion Suggestion importance[1-10]: 9 Why: Implementing a rate limiter is crucial for maintaining stable operation and avoiding API rate limit issues, which is a significant improvement.	9

---

Previous suggestions

✅ Suggestions up to commit 7bda110

Category	Suggestion	Score
Best practice	Implement rate limiting to avoid exceeding GitHub API limits Consider implementing a rate limiting mechanism to avoid exceeding GitHub API rate limits. This can be done by tracking the number of requests made and adding delays when approaching the limit. pr_agent/servers/github_polling.py [155-163] async with aiohttp.ClientSession() as session: + rate_limit = 5000 # GitHub's default rate limit + requests_made = 0 while True: try: + if requests_made >= rate_limit: + await asyncio.sleep(3600) # Wait for an hour if rate limit is reached + requests_made = 0 await asyncio.sleep(3) get_logger().info("Polling for notifications") headers = { "Accept": "application/vnd.github.v3+json", "Authorization": f"Bearer {token}" } + requests_made += 1 Suggestion importance[1-10]: 9 Why: This is a crucial suggestion to prevent API rate limit issues, which could cause the application to fail or be temporarily blocked by GitHub.	9
Performance	✅ Use a process pool to manage concurrent tasks more efficiently Suggestion Impact: The commit implemented a limit on the number of concurrent tasks, which aligns with the suggestion's goal of managing system resources more efficiently. While it didn't use a process pool as suggested, it added a maximum limit of parallel tasks and drops excess tasks. code diff: + max_allowed_parallel_tasks = 10 if task_queue: processes = [] - for func, args in task_queue: # Create parallel tasks + for i, func, args in enumerate(task_queue): # Create parallel tasks p = multiprocessing.Process(target=func, args=args) processes.append(p) p.start() + if i > max_allowed_parallel_tasks: + get_logger().error( + f"Dropping {len(task_queue) - max_allowed_parallel_tasks} tasks from polling session") + break Instead of creating a new process for each task in the queue, consider using a process pool to reuse processes and limit the number of concurrent processes. This can help manage system resources more efficiently, especially when dealing with a high volume of notifications. pr_agent/servers/github_polling.py [202-207] -processes = [] -for func, args in task_queue: # Create parallel tasks - p = multiprocessing.Process(target=func, args=args) - processes.append(p) - p.start() -task_queue.clear() +with multiprocessing.Pool(processes=4) as pool: # Adjust the number of processes as needed + results = [pool.apply_async(func, args) for func, args in task_queue] + task_queue.clear() + # Optionally, you can wait for all tasks to complete: + # for result in results: + # result.get() Suggestion importance[1-10]: 8 Why: This suggestion offers a significant improvement in resource management and scalability, especially for handling multiple tasks concurrently.	8
	Optimize the handling of processed notification IDs to improve performance Consider using a more efficient data structure for handled_ids, such as a set, instead of repeatedly checking and adding to it. This can improve performance, especially when dealing with a large number of notifications. pr_agent/servers/github_polling.py [135-189] handled_ids = set() ... -handled_ids.add(notification['id']) -output = await is_valid_notification(notification, headers, handled_ids, session, user_id) -if output[0]: - _, handled_ids, comment, comment_body, pr_url, user_tag = output +if notification['id'] not in handled_ids: + handled_ids.add(notification['id']) + output = await is_valid_notification(notification, headers, handled_ids, session, user_id) + if output[0]: + _, comment, comment_body, pr_url, user_tag = output Suggestion importance[1-10]: 3 Why: The suggestion is valid but unnecessary as the code already uses a set for handled_ids, which is an efficient data structure for this purpose.	3
Enhancement	Implement error handling and retries for API requests to improve robustness Consider implementing error handling and retries for the API requests to improve the robustness of the polling mechanism. This can help handle temporary network issues or API downtime. pr_agent/servers/github_polling.py [172-179] -async with session.get(NOTIFICATION_URL, headers=headers, params=params) as response: - if response.status == 200: - if 'Last-Modified' in response.headers: - last_modified[0] = response.headers['Last-Modified'] - since[0] = None - notifications = await response.json() - if not notifications: - continue +max_retries = 3 +retry_delay = 5 +for attempt in range(max_retries): + try: + async with session.get(NOTIFICATION_URL, headers=headers, params=params) as response: + if response.status == 200: + if 'Last-Modified' in response.headers: + last_modified[0] = response.headers['Last-Modified'] + since[0] = None + notifications = await response.json() + if not notifications: + break + # Process notifications... + elif response.status == 429: # Rate limit exceeded + retry_after = int(response.headers.get('Retry-After', retry_delay)) + await asyncio.sleep(retry_after) + else: + raise Exception(f"Unexpected status code: {response.status}") + break # Success, exit retry loop + except aiohttp.ClientError as e: + if attempt == max_retries - 1: + raise + await asyncio.sleep(retry_delay) Suggestion importance[1-10]: 8 Why: This suggestion significantly improves the reliability of the application by handling network issues and API downtime gracefully.	8