Queue Handlers: A Two-Part Solution for Logging Performance Critical Threads

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First things first, what are these magical creatures? Well, they’re essentially a way to handle log messages in a separate thread from the main program flow. This is useful when you have threads doing some serious heavy lifting and don’t want their performance to be affected by logging output. Instead of writing directly to a file or console, Queue Handlers store the logs in memory until they can be processed separately.

Now, Let’s kick this off with how we set up this two-part solution for our Python programs. First, you need to create your own custom logger class that uses a QueueHandler instead of the default File or StreamHandler. Here’s an example:

# Import necessary libraries
import logging
from queue import Queue
from threading import Thread

# Create a custom logger class that inherits from the base Logger class
class CustomLogger(logging.Logger):
    # Initialize the logger with a name and a queue for storing log messages
    def __init__(self, name):
        super().__init__(name)
        self.queue = Queue()
        self.handler = None
        
    # Configure the logger with a custom QueueHandler and start a separate thread for processing logs
    def configure(self):
        # Check if the handler has already been set up
        if not self.handler:
            # Create a formatter for the log messages
            formatter = logging.Formatter('%(asctime)s %(processName)-10s %(name)s %(levelname)-8s %(message)s')
            # Create a QueueHandler and set its level and formatter
            h = QueueHandler()
            h.setLevel(logging.DEBUG)
            h.setFormatter(formatter)
            # Add a message to the queue to indicate that the log processing thread is starting
            self.queue.put_nowait('Starting log processing thread...')
            # Start a new thread to process the logs using the QueueHandler
            Thread(target=self._process_logs, args=(h,)).start()
        # Add the custom handler to the logger and set its level
        self.addHandler(h)
        self.setLevel(logging.DEBUG)
        
    # Stop the log processing thread by adding None to the queue
    def stop(self):
        self.queue.put_nowait(None)
    
    # Method for processing logs in a separate thread
    def _process_logs(self, handler):
        # Loop until None is added to the queue
        while True:
            # Get the next log message from the queue
            record = self.queue.get()
            # Check if None was added to the queue, indicating that the thread should stop
            if record is None:
                break
            # Handle the log message using the custom handler
            handler.handle(record)
            
    # Methods for logging at different levels
    def debug(self, msg):
        self._log('debug', msg)
    
    def info(self, msg):
        self._log('info', msg)
    
    def warning(self, msg):
        self._log('warning', msg)
    
    def error(self, msg):
        self._log('error', msg)
    
    def critical(self, msg):
        self._log('critical', msg)
    
    # Method for adding log messages to the queue for processing in a separate thread
    def _log(self, level, msg):
        # Create a LogRecord object with the specified level, name, message, and other optional arguments
        record = logging.makeLogRecord(level=level, name='my_logger', msg=msg, args=None, exc_info=None)
        # Add the LogRecord to the queue for processing
        self.queue.put_nowait(record)

As you can see, we’ve created a custom logger class that sets up the QueueHandler and starts a separate thread to process logs in the background. We also added some convenience methods for logging at different levels (debug, info, warning, error, critical).

Now how this solution affects performance. By using a QueueHandler instead of writing directly to disk or console, we can avoid any I/O overhead that would otherwise slow down our threads. This is especially useful for logging in tight loops where the amount of info generated might take too much time to wade through.

However, there are some caveats to consider. First, you need to make sure your log processing thread doesn’t become a bottleneck itself by doing too much work or taking too long to process logs. Secondly, if you have multiple threads logging at the same time, you may want to use separate QueueHandlers for each thread to avoid contention and ensure that all logs are processed in order.

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