Revealing the secrets of Python GIL: the roadblock to multi-threaded concurrent programming

python GIL (Global Interpreter Lock) is a mutex lock, which ensures that there is only one thread## at the same time #Ability to execute Python bytecode. This limits Python's multithreadingconcurrent programming performance, but it also ensures the stability and security of the Python interpreter.

Principle of GIL

GIL is a global lock, which is located in the Python interpreter. When a thread wants to execute Python bytecode, it must first obtain the GIL. If the GIL is already held by another thread, then this thread must wait until the GIL is released.

The acquisition and release of GIL are atomic operations, which means that it can only be executed by one thread at the same time. This ensures that the GIL is not held by multiple threads at the same time, thus avoiding data races and deadlocks.

Advantages and Disadvantages of GIL

The advantage of GIL is that it ensures the stability and security of the Python interpreter. Since only one thread can execute Python bytecode at the same time, data races and deadlocks will not occur. This is very important for some applications that need to ensure data consistency.

The disadvantage of GIL is that it limits Python's multi-threaded

Concurrency Programming performance. Since only one thread can execute Python bytecode at a time, when multiple threads are running at the same time, these threads must compete for the GIL, which will cause performance degradation.

Alternatives to GIL

For some applications that require

high concurrency performance, the GIL is a bottleneck. To solve this problem, the Python community has proposed some GIL alternatives, such as:

• Multi-process programming: Multi-process programming is a concurrent programming method that allows multiple processes to run at the same time. In multi-process programming, each process has its own GIL, so there is no problem of GIL competition. However, multi-process programming also has some disadvantages, such as high communication overhead between processes.
• Asynchronous programming: Asynchronous programming is a concurrent programming method that allows one thread to handle multiple tasks at the same time. In asynchronous programming, when a task needs to wait, the thread will not be blocked and can continue to perform other tasks. This can improve concurrency performance, but asynchronous programming also requires programmers to write more complex code.

Summarize

GIL is an important lock in Python, which ensures the stability and security of the Python interpreter. However, the GIL also limits Python's multi-threaded concurrent programming performance. For some applications that require high concurrency performance, the GIL is a bottleneck. To solve this problem, the Python community has proposed various alternatives to GIL, such as multi-process programming and asynchronous programming.

Python GIL demo code

The following code demonstrates how to use Python's GIL:

import threading

# 创建一个全局变量
global_variable = 0

# 创建一个线程函数
def increment_global_variable():
global global_variable
for i in range(1000000):
global_variable += 1

# 创建两个线程
thread1 = threading.Thread(target=increment_global_variable)
thread2 = threading.Thread(target=increment_global_variable)

# 启动两个线程
thread1.start()
thread2.start()

# 等待两个线程结束
thread1.join()
thread2.join()

# 打印全局变量的值
print(global_variable)

Run this code, you will find that the value of the global variable is not 2000000, but less than this value. This is because the existence of GIL restricts two threads from executing Python bytecode at the same time, resulting in that two threads cannot operate global variables at the same time.

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