First things first: why do we need logging in the first place? Well, let’s say your application is a complex web of threads that run simultaneously like a bunch of hyperactive monkeys on crack. And then something goes wrong maybe one of those monkeys accidentally drops a banana and causes chaos. You want to know which monkey did it so you can give them an extra-large bucket of water for their bath time. That’s where logging comes in handy!
Now, some best practices that will make your multithreaded application look like a well-oiled machine instead of a bunch of hyperactive monkeys on crack. First of all: use a thread-safe logger. This means you should avoid using the built-in Python logging module because it’s not thread-safe by default. Instead, opt for third-party libraries such as loguru or asyncio_logging that are specifically designed to handle multithreaded applications.
Secondly: use a unique logger for each thread. This will help you keep track of which monkey did what and when. You can do this by creating a new logger object in each thread using the logging module’s getLogger() function, like so:
# Import the necessary modules
import logging # Import the logging module to handle logging
from threading import Thread # Import the Thread class from the threading module to handle multithreading
# Define a function to be executed by each thread
def my_function():
# Do some stuff here
logger = logging.getLogger(__name__) # Create a new logger object for each thread using the name of the current module
logger.debug("I am a monkey!") # Log a debug message to track the actions of each thread
# Check if the script is being run directly
if __name__ == '__main__':
logger = logging.getLogger(__name__) # Create a logger object for the main thread
logger.setLevel(logging.DEBUG) # Set the logging level to DEBUG
formatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s') # Create a formatter to format the log messages
file_handler = logging.FileHandler("myapp.log") # Create a file handler to log messages to a file
file_handler.setLevel(logging.DEBUG) # Set the logging level for the file handler to DEBUG
file_handler.setFormatter(formatter) # Set the formatter for the file handler
console_handler = logging.StreamHandler() # Create a console handler to log messages to the console
console_handler.setLevel(logging.DEBUG) # Set the logging level for the console handler to DEBUG
console_handler.setFormatter(formatter) # Set the formatter for the console handler
logger.addHandler(file_handler) # Add the file handler to the logger
logger.addHandler(console_handler) # Add the console handler to the logger
threads = [] # Create an empty list to store the threads
for i in range(10):
t = Thread(target=my_function, name="Monkey " + str(i)) # Create a new thread with a unique name
t.start() # Start the thread
threads.append(t) # Add the thread to the list of threads
for thread in threads:
thread.join() # Wait for each thread to finish before moving on to the next one
In this example, we’re creating a new logger object inside the my_function() function and logging debug messages using that specific logger. This will help us keep track of which monkey did what and when.
Lastly, make sure to flush your logs regularly. This means you should call the logger’s handler’s flush() method after each log message is written. This ensures that all log messages are immediately sent to their destination (e.g., file or console) instead of being buffered in memory. You can do this by adding a context manager around your logging statements, like so:
# Import the necessary modules
import logging # Import the logging module to enable logging functionality
from threading import Thread # Import the Thread class from the threading module to enable multi-threading
# Define a function to be executed by each thread
def my_function():
# Do some stuff here
with logging.LoggerAdapter(__name__, {'thread': current_thread().name}) as logger: # Create a logger object with the current thread's name as a context
logger.debug("I am a monkey!") # Log a debug message using the logger object
if __name__ == '__main__':
...
threads = [] # Create an empty list to store the threads
for i in range(10):
t = Thread(target=my_function, name="Monkey " + str(i)) # Create a thread object with the target function and a unique name
t.start() # Start the thread
threads.append(t) # Add the thread to the list
for thread in threads:
thread.join() # Wait for all threads to finish before continuing with the main thread
In this example, we’re using the logging-contextlib library to create a logger adapter that includes information about the current thread. This will help us keep track of which monkey did what and when.
And there you have it some best practices for logging in multithreaded applications! Remember: use a thread-safe logger, use unique loggers for each thread, and flush your logs regularly.