Python 多处理映射和应用不能并行运行？

Question

I am confused about the python multiprocessing module. Suppose we write the code like this:

pool = Pool()
for i in len(tasks) :
    pool.apply(task_function, (tasks[i],))

Firstly i = 0, and the first subprocessor will created and execute the first task. Since we are using the apply instead of apply_async, the main processor is blocked, so there is no chance that i get increment, and execute the second task. So by doing this way, we are actually write a serial code, not run in multiprocessing? So the same is true when we use map instead of map_async? No wonder the result of these tasks comes in order. If this is the truth, we don't even bother to use multiprocessing's map and apply function. Correct me, if I am wrong

Answer 1

According to the documentation :

apply(func[, args[, kwds]])

Equivalent of the apply() built-in function. It blocks until the result is ready, so apply_async() is better suited for performing work in parallel. Additionally, func is only executed in one of the workers of the pool.

So yes, if you want to delegate work to another process and return control to your main process, you have to use apply_async .

Regarding your statement:

If this is the truth, we don't even bother to use multiprocessing's map and apply function

Depends on what you want to do. For example map will split the arguments into chunks and apply the function for each chunk in the different processes of the pool, so you are achieving parallelism. This would work for your example:

pool.map(task_funcion, tasks)

It will split tasks into pieces, and then call task_function on each process from the pool with the different pieces of tasks . So for example you could have Process1 running task_function(task1) , Process2 running task_function(task2) all at the same time.