log_processor_thread

q = queue.Queue(-1) # No limit on queue size queue_handler = QueueHandler(q) queue_listener = QueueListener(q, logging.FileHandler('query_performance.log')) # Add the queue handler to the logger logger.addHandler(queue_handler) # Start t…

if log_entry is None: break try: logger.handle(log_entry) except Exception as e: logger.error(f"Failed to log entry: {e}") q.task_done() # Start the log processing thread …

from logging.handlers import QueueHandler, QueueListener import queue import threading import time import json # Configure logging logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) # Create a queue handler and listener q…

# Add the queue handler to the logger logger.addHandler(queue_handler) # Start the queue listener queue_listener.start() # Function to process log entries in a separate thread def process_log_entries(): while True: log_entry =…

Using a dedicated thread for logging can help offload the logging task and reduce the impact on the main application. ### Example Implementation Here's an updated version of your code that incorporates these improvements: ```python impor…

log_processor_thread.start() # Define a function to log queries def log_query(query, user_id=None, query_params=None): log_entry = { "query": query, "user_id": user_id, "query_params": query_params, "tim…