asyncio dev

Introduction to Asynchronous Programming

In classic programming, you write code that runs in a single thread. This means that each step of your program has to finish before the next step can start.

Asynchronous programming is different. It allows you to write code that can run multiple tasks at the same time, even if they're not all finished yet. This is possible because asynchronous programs use a special kind of thread called a "coroutine".

Coroutines are like regular threads, but they can be paused and resumed later. This allows asynchronous programs to run multiple tasks concurrently, even if they're not all ready to run at the same time.

Common Mistakes and Traps

Here are some common mistakes and traps that you should avoid when developing with asyncio:

Blocking I/O: Blocking I/O is when your program has to wait for a specific event to happen before it can continue. For example, waiting for a network request to complete. Blocking I/O can cause your program to freeze up, so it's important to avoid it whenever possible.
Deadlocks: Deadlocks occur when two or more coroutines are waiting for each other to finish before they can continue. This can cause your program to freeze up indefinitely. To avoid deadlocks, you should always use await to wait for coroutines to finish.
Exceptions: Exceptions can occur in asynchronous code just like they can in regular code. However, it's important to handle exceptions differently in asynchronous code. If an exception occurs in a coroutine, the coroutine will be terminated and the exception will be propagated to the caller. To handle exceptions in asynchronous code, you should use the try/except statement.

How to Avoid Common Mistakes and Traps

Here are some tips on how to avoid common mistakes and traps when developing with asyncio:

Use non-blocking I/O: Non-blocking I/O is when your program can continue running even if it's waiting for a specific event to happen. For example, you can use the asyncio.sleep() function to schedule a coroutine to run at a later time.
Avoid deadlocks: To avoid deadlocks, you should always use await to wait for coroutines to finish. This will ensure that the coroutine will not continue running until its dependencies have finished.
Handle exceptions properly: To handle exceptions in asynchronous code, you should use the try/except statement. This will allow you to catch exceptions and handle them appropriately.

Real-World Applications

Asynchronous programming is used in a variety of real-world applications, including:

Web development: Asynchronous programming is used in web development to handle multiple requests concurrently. This can improve the performance of web applications by reducing latency.
Data processing: Asynchronous programming can be used to process large datasets concurrently. This can improve the speed of data processing tasks.
Network programming: Asynchronous programming can be used to handle multiple network connections concurrently. This can improve the performance of network applications by reducing latency.

Code Snippet

The following code snippet shows you how to perform a simple asynchronous network request:

import asyncio

async def main():
    reader, writer = await asyncio.open_connection('example.com', 80)
    writer.write(b'GET / HTTP/1.1\r\nHost: example.com\r\n\r\n')
    data = await reader.read()
    print(data.decode())

asyncio.run(main())

This code snippet creates a new TCP connection to the specified host and port, sends an HTTP GET request, and reads the response. The await keyword is used to pause the coroutine until the network request is complete.

Conclusion

Asynchronous programming is a powerful tool that can be used to improve the performance of your Python programs. However, it's important to be aware of the common pitfalls and traps before you start developing with asyncio. By following the tips in this article, you can avoid common mistakes and write efficient and effective asynchronous code.

Debug Mode for Asyncio

Asyncio is like a helper that makes your code run smoothly without waiting for things to finish. It runs in "production mode" by default, which is great for when your app is up and running. But sometimes when you're building your app and want to find any issues or bugs, it's useful to switch to "debug mode."

How to Turn on Debug Mode:

Go to your settings and find the "PYTHONASYNCIODEBUG" option. Change it to "1" to turn on debug mode.
You can also enable debug mode by putting this code in your Python script: "asyncio.run(main(), debug=True)"
Lastly, you can also enable it by typing "loop.set_debug()" in your code.

What Debug Mode Does:

It shows you more information about what asyncio is doing, like which tasks are running and when they're finished.
It helps you find any problems in your code that might slow down your app.
It gives you warnings when your code might be using too many resources, like memory or processor time.

Examples:

Code without Debug Mode:

import asyncio

async def main():
    task1 = asyncio.create_task(do_something())
    task2 = asyncio.create_task(do_something_else())
    await task1
    await task2

Code with Debug Mode:

import asyncio
import logging

logging.basicConfig(level=logging.DEBUG)

async def main():
    asyncio.set_debug(True)
    task1 = asyncio.create_task(do_something())
    task2 = asyncio.create_task(do_something_else())
    await task1
    await task2

In debug mode, you'll see more detailed information about each task and when it's completed. This helps you understand how your code is running and if there are any potential issues.

Real-World Applications:

Debugging a web server that's responding slowly
Identifying resource leaks or bottlenecks in a complex application
Investigating performance issues in a multithreaded program

Simplified Explanation of asyncio Debug Mode:

1. "Forgotten Await" Pitfall:

Sometimes, you can write code that looks like a coroutine (a function that can pause and resume execution), but you forget to actually "await" the coroutine, which means the code won't run. Debug mode in asyncio checks for these forgotten awaits and logs them to help you catch the mistake.

Example:

async def my_function():
    # Do something...

# Oops, forgot to await this function!

2. Thread Safety Checks:

Some asyncio functions, like loop.call_soon and loop.call_at, are not safe to call from multiple threads at the same time. Debug mode in asyncio raises an exception if you try to call these functions from the wrong thread.

Example:

import asyncio
import threading

def thread_function():
    loop = asyncio.get_event_loop()
    loop.call_soon(my_function)

t = threading.Thread(target=thread_function)
t.start()
t.join()

3. Slow I/O Operation Logging:

When debug mode is enabled, asyncio logs how long it takes to perform an I/O operation (like reading from a file or sending data over a network). If an I/O operation takes too long, asyncio will log a warning to help you identify potential performance issues.

Example:

import asyncio
import time

async def read_file():
    with open('large_file.txt', 'r') as f:
        data = f.read()

loop = asyncio.get_event_loop()
asyncio.ensure_future(read_file())

# Loop runs until read_file() is complete
loop.run_until_complete(read_file())

4. Slow Callback Logging:

When debug mode is enabled, asyncio logs any callback function (a function passed as an argument to asyncio) that takes longer than 100 milliseconds to execute. You can adjust this threshold by setting the loop.slow_callback_duration attribute.

Example:

import asyncio
import time

def slow_callback():
    time.sleep(0.2)
    print('Slow callback')

loop = asyncio.get_event_loop()
loop.call_soon(slow_callback)
loop.run_forever()

Potential Applications:

Debugging forgotten awaits: asyncio debug mode can help you identify and fix forgotten await calls, which can lead to hard-to-debug errors.
Ensuring thread safety: asyncio debug mode can help you catch errors that occur when using non-threadsafe asyncio APIs incorrectly.
Profiling I/O performance: asyncio debug mode can help you identify slow I/O operations that may need to be optimized.
Tracking slow callbacks: asyncio debug mode can help you identify long-running callbacks that may be affecting performance.

Concurrency and Multithreading

Imagine you have a toy car race with multiple toy cars. Each car represents a task that needs to be done.

Event Loop

The race track is like an event loop. It's a special place where all the cars (tasks) can run one at a time, but they can't pass each other.

Tasks

The toy cars are like tasks. They can be different types of tasks, like fetching data or sending messages.

Multithreading

Imagine you have multiple race tracks (threads). Each track can run multiple cars (tasks) at the same time. This is called multithreading.

Thread Safety

The race tracks and cars are not always safe to use from anywhere. If you want to play with a car from a different track, you need to ask the track nicely using the call_soon_threadsafe method.

Running Coroutines from Other Threads

If you have a special car (coroutine) that you want to run on a different track, you can use the run_coroutine_threadsafe function. It will give you a special pass to let the car run on the other track.

Real-World Applications

Concurrency and multithreading are useful in many real-world applications, such as:

Web servers: Handling multiple requests at the same time
File downloads: Fetching multiple files in parallel
Data processing: Analyzing large datasets using multiple processors

Example Code

# Example of running a coroutine from another thread

async def coro_func():
    return await asyncio.sleep(1, 42)

def run_coro_from_thread():
    loop = asyncio.new_event_loop()
    result_future = asyncio.run_coroutine_threadsafe(coro_func(), loop)
    result = result_future.result()
    # Do something with the result

Simplified Explanation of asyncio Loop Methods

1. Event Loops:

Imagine your computer as a playground with many kids (tasks) playing. The event loop is like a teacher who keeps everyone organized. It watches for when kids raise their hands (events) and calls them up to take turns doing stuff (tasks).

2. run_in_executor:

Sometimes, a kid (task) needs to do something that takes a long time, like playing outside (blocking). This can slow down the whole playground (event loop). So, the teacher (event loop) can say, "Go play outside with another group" (executor pool). That way, the playground (event loop) can keep taking turns with the other kids (tasks) while the long task (blocking task) is happening outside.

Code Example:

import asyncio
import concurrent.futures

async def main():
    # Create an executor pool (playground outside)
    executor = concurrent.futures.ThreadPoolExecutor()

    # Send a long task (playing outside) to the executor pool
    future = loop.run_in_executor(executor, long_task)

    # The event loop (teacher) keeps playing with the other kids (tasks)
    await other_tasks()

    # When the long task (playing outside) is done, get the result
    result = future.result()

3. Pipelines and File Descriptors:

Sometimes, the kids (tasks) need to talk to the outside world (files, sockets). The event loop (teacher) can set up "listening stations" (pipelines, file descriptors) where the kids (tasks) can leave messages for the outside world or check for incoming messages. This way, the event loop (teacher) doesn't have to stop playing with the other kids (tasks) to keep an eye on the outside world.

Code Example:

import asyncio

async def read_file(path):
    with open(path, "r") as f:
        data = await asyncio.read(f.fileno(), 1024)
    return data

4. Subprocess:

Imagine a kid (task) who wants to play a different game (another process). The event loop (teacher) can set up a "playroom" (subprocess) where the kid (task) can go and play the other game. This way, the event loop (teacher) can keep playing with the other kids (tasks) while the kid (task) is playing the other game.

Code Example:

import asyncio

async def run_subprocess(cmd):
    process = await asyncio.create_subprocess_exec(*cmd)
    stdout, stderr = await process.communicate()
    return stdout, stderr

Potential Applications:

run_in_executor: Performing long-running tasks in the background (e.g., I/O operations, CPU-intensive calculations) without blocking the event loop.
Pipelines and File Descriptors: Monitoring file changes, receiving data from sockets, or sending data to external devices without blocking the event loop.
Subprocess: Running external commands or programs concurrently with the event loop.

Running Blocking Code

Blocking Code:

Imagine you have a toy car that can only move forward. If you want to turn it around, you have to stop it first. This is like blocking code.

It's like when you are running a race and you stop to tie your shoe. All the other runners have to wait for you to finish.

Executor:

An executor is like a helper that can take the toy car and turn it around for you, without stopping the race.

You can ask the executor to do the task for you, and it will run the task in a different lane, so the other runners don't have to wait.

Real-World Code Example:

Imagine you have a program that reads data from a file and then processes it. The file reading is a blocking operation, meaning it halts the whole program until the file is read.

Using an executor, you can tell the program to start reading the file. Then, the program can continue processing other data while the executor is reading the file in the background.

Potential Applications:

Database Queries: Queries to the database can block the event loop and slow down the application. Using an executor, database operations can be offloaded to a separate thread.
CPU-Intensive Tasks: Tasks that require heavy computation, such as image processing or scientific computations, can be moved to an executor to prevent blocking the event loop.
File Operations: Reading or writing large files can block the event loop. An executor can be used to perform file I/O operations concurrently.

Improved Code Snippet:

import asyncio

async def main():
    # Define the blocking task
    def blocking_task():
        time.sleep(1)  # Simulate a blocking operation
        return 'Result'

    # Create an executor
    executor = asyncio.get_event_loop().run_in_executor(None, blocking_task)

    # Run the blocking task in a separate thread
    result = await executor

    # Process the result...

Logging in AsyncIO

AsyncIO uses the Python logging module to track events and errors in your code. You can access the AsyncIO logger using the name "asyncio".

Default Log Level

By default, AsyncIO logs at the INFO level, which means it shows you important messages about what's happening in your code.

Adjusting Log Level

If you want to see more or less detail in the logs, you can adjust the log level. For example, to see only warnings and errors, you would use the WARNING level:

import logging

logger = logging.getLogger("asyncio")
logger.setLevel(logging.WARNING)

Network Logging

When you use network logging, it's important to remember that it can slow down your event loop. To prevent this, you can use a separate thread or non-blocking IO for logging.

Real-World Example

Imagine you're building a web server with AsyncIO. You might want to log any requests to your server, but you don't want it to slow down the server. To handle this, you could set up a separate thread to handle the logging:

import logging
import threading

logger = logging.getLogger("asyncio")

def log_requests():
    while True:
        request = get_next_request()
        logger.info(f"Received request from {request.client_address}")

thread = threading.Thread(target=log_requests)
thread.start()

Applications

Logging is useful for debugging, tracking errors, and monitoring the performance of your AsyncIO application. It helps you understand what's happening in your code and identify any issues.

Detect Never-Awaited Coroutines

Explanation:

A coroutine is a special function that can pause and resume execution. If you call a coroutine but never "wait" for it, asyncio will warn you.

Simplified Explanation:

Imagine a game where you have to wait for your turn. If you never press the "Wait" button, the other players will never know it's your turn.

Code Example:

async def my_turn():
    # This coroutine represents your turn in the game
    print("It's my turn!")

async def main():
    my_turn()  # We call the coroutine, but never wait for it

asyncio.run(main())

Output:

RuntimeWarning: coroutine 'my_turn' was never awaited

Fix:

You can either wait for the coroutine:

async def main():
    await my_turn()  # Now we wait for our turn

Or you can schedule it using asyncio's scheduler:

async def main():
    asyncio.create_task(my_turn())  # This schedules the coroutine

Real-World Applications:

Detecting and fixing bugs in asynchronous code where coroutines are not awaited properly.
Ensuring that all important tasks in your program are completed before exiting.

Detect never-retrieved exceptions

If you have a function that runs asynchronously (in the background) and raises an exception, that exception will not be shown to the user unless you specifically handle it. This is because the function is running in the background, and the main program is not waiting for it to finish.

To fix this, you can use the asyncio.create_task() function to create a task that runs the function. A task is an object that represents an asynchronous function that is running in the background. When the task finishes, the exception will be raised in the main program.

Here is an example of how to use asyncio.create_task() to handle exceptions:

import asyncio

async def bug():
    raise Exception("not consumed")

async def main():
    task = asyncio.create_task(bug())
    try:
        await task
    except Exception as e:
        print(e)

asyncio.run(main())

Output:

Exception: not consumed

This code will print the exception that was raised in the bug() function.

Here are some real-world applications of this technique:

Logging unhandled exceptions for debugging purposes
Alerting users of critical errors
Retrying failed tasks automatically

1. What is asyncio debug mode? asyncio debug mode is a feature that allows you to get more information about tasks that raise an exception. When a task raises an exception, the default behavior is to log the exception and continue running the event loop. This can make it difficult to debug your code, because you don't know where the task was created or what caused the exception.

2. How to enable debug mode You can enable debug mode by passing the debug=True argument to the asyncio.run() function.

3. What output to expect in debug mode When a task raises an exception in debug mode, you will see a message like the following:

Task exception was never retrieved
future: <Task finished coro=<bug() done, defined at test.py:3>
    exception=Exception('not consumed') created at asyncio/tasks.py:321>

source_traceback: Object created at (most recent call last):
  File "../t.py", line 9, in <module>
    asyncio.run(main(), debug=True)

< .. >

Traceback (most recent call last):
  File "../t.py", line 4, in bug
    raise Exception("not consumed")
Exception: not consumed

The first line of the message tells you that a task exception was never retrieved. This means that the exception was not handled by any of the tasks that were waiting for the result of the task that raised the exception.

The second line of the message shows you the future object for the task that raised the exception. The future object contains information about the task, such as its state and the result or exception that it returned.

The third line of the message shows you the source traceback for the task that raised the exception. The source traceback shows you where the task was created and what code was running when the exception was raised.

4. Real-world applications of asyncio debug mode asyncio debug mode can be useful in a variety of situations, such as:

Debugging unhandled exceptions in tasks
Identifying the source of task exceptions
Tracking the progress of tasks

5. Complete code implementation and example The following code shows how to use asyncio debug mode to debug an unhandled exception in a task:

import asyncio

async def main():
    try:
        await bug()
    except Exception as e:
        print(e)

async def bug():
    raise Exception("not consumed")

asyncio.run(main(), debug=True)

Output:

Task exception was never retrieved
future: <Task finished coro=<bug() done, defined at test.py:3>
    exception=Exception('not consumed') created at asyncio/tasks.py:321>

source_traceback: Object created at (most recent call last):
  File "../t.py", line 10, in <module>
    asyncio.run(main(), debug=True)

< .. >

Traceback (most recent call last):
  File "../t.py", line 5, in bug
    raise Exception("not consumed")
Exception: not consumed

In this example, the bug() function raises an unhandled exception. The main() function catches the exception and prints it. The asyncio.run() function is called with the debug=True argument, which enables debug mode. The output shows the task exception message, the future object for the task that raised the exception, and the source traceback for the task that raised the exception.

Previousarray Next_typing