Detailed introduction to collecting station B live broadcast barrages based on asyncio asynchronous coroutine framework

Concurrent and Asynchronous Programming Concurrent programming deals with multiple tasks executing simultaneously, asynchronous programming is a type of concurrent programming in which tasks do not block threads. asyncio is a library for asynchronous programming in python, which allows programs to perform I/O operations without blocking the main thread. Event loop The core of asyncio is the event loop, which monitors I/O events and schedules corresponding tasks. When a coroutine is ready, the event loop executes it until it waits for I/O operations. It then pauses the coroutine and continues executing other coroutines. Coroutines Coroutines are functions that can pause and resume execution. The asyncdef keyword is used to create coroutines. The coroutine uses the await keyword to wait for the I/O operation to complete. The following basics of asyncio

0. Basics In "Python Asyncio Scheduling Principles", Asyncio's two basic scheduling units, Handler and TimeHandler, are introduced. They can only be called by the loop.call_xx function. Developers do not know their existence on the surface. They are different from loop.call_xx. It is the basic function of the event loop, but these operations are single operations, and developers need to write their own code to connect their operations in series. In "The Role of Python's Waitable Objects in Asyncio", it is introduced that asyncio.Task, the initiator of the coroutine chain, can interact with the event loop through loop.call_soon and connect the entire coroutine in series.

1. Why use asynchronous programming? Traditional programming uses blocking I/O, which means that the program waits for an operation to complete before continuing. This may work well for a single task, but may cause the program to slow down when processing a large number of tasks. Asynchronous programming breaks the limitations of traditional blocking I/O and uses non-blocking I/O, which means that the program can distribute tasks to different threads or event loops for execution without waiting for the task to complete. This allows the program to handle multiple tasks simultaneously, improving the program's performance and efficiency. 2. The basis of Python asynchronous programming The basis of Python asynchronous programming is coroutines and event loops. Coroutines are functions that allow a function to switch between suspending and resuming. The event loop is responsible for scheduling

Golang coroutines and Pythonasyncio are both concurrent programming tools. Coroutines are lightweight threads that run concurrently on the same thread; asyncio uses an event loop to handle I/O events. Golang's coroutine syntax is concise, its performance is better than asyncio, and it is suitable for intensive calculations; asyncio's asynchronous features are suitable for processing a large number of I/O events, and its syntax is easier to use, making it suitable for Python developers. It is important to choose the most appropriate technology based on application needs and developer preferences.

Python's Global Interpreter Lock (GIL) is a synchronization mechanism that ensures that the Python interpreter can only execute one thread at a time. This helps prevent data races and guarantee thread safety, but can also limit the performance of parallel computing, especially on multi-core systems. The role of GIL The role of GIL is to prevent multiple threads from accessing shared data at the same time, leading to race conditions. It does this by acquiring a lock every time the bytecode is executed. When one thread acquires the GIL, other threads are blocked until the lock is released. Disadvantages of GIL Although GIL provides thread safety, it also negatively affects the performance of multi-threaded Python programs. Since the GIL limits parallel execution, it cannot be fully exploited on multi-core systems

What is asynchronous programming? Asynchronous programming is a programming paradigm that allows a program to perform multiple tasks concurrently without blocking. Unlike traditional synchronous programming, in asynchronous programming, when a task needs to wait for other tasks to complete, it will not be blocked, but can continue to perform other tasks. This way, the program can handle multiple tasks simultaneously, thereby improving the overall performance of the program. Asynchronous programming in Python Python 3.4 and higher supports asynchronous programming. Asynchronous programming is mainly implemented in Python through coroutines and asyncio modules. A coroutine is a special function in Python that allows a program to pause and resume execution without blocking. The asyncio module is an async in Python

The definition of coroutine requires the use of asyncdef statements. What can coroutines do: 1. Wait for a future result 2. Wait for another coroutine (produce a result or raise an exception) 3. Produce a result to the coroutine that is waiting for it 4 , throw an exception to the coroutine that is waiting for it to run and call the coroutine function. The coroutine will not start running, but just returns a coroutine object. There are two ways to make the coroutine object run: 1. When another coroutine is already running, Use await to wait for it in the running coroutine. 2. Plan its execution through the ensure_future function. Only when the loop of a certain thread is running, can the coroutine run. The following example: first get the default loop of the current thread, and then transfer the coroutine object. Leave it to loop.run

1. Basic introduction to Python. Asyncio is a large and comprehensive library that includes many functions. In addition to the three waitable objects, the logic related to core scheduling also has other functions, which are located in runners.py and base_event. py, event.py three files. The runners.py file has one main class - Runner. Its main responsibility is to complete the event loop for entering coroutine mode and wait for initialization, and to clean up coroutines, generators and other objects still in memory when exiting coroutine mode. . The coroutine mode is just for the convenience of understanding. For computers, there is no such distinction between event.py files except for storing EventLoop pairs.