


How to use multiprocessing to implement inter-process communication in Python?
1. Why should we master inter-process communication?
The multi-threaded code efficiency of Python is restricted by GIL and cannot be accelerated by multi-core CPU, while multi-process This method can bypass the GIL, take advantage of multi-CPU acceleration, and significantly improve the performance of the program. However, inter-process communication is an issue that must be considered. A process is different from a thread. A process has its own independent memory space and cannot use global variables to transfer data between processes.
In actual project requirements, there are often intensive calculations or real-time tasks, and sometimes a large amount of data needs to be transferred between processes, such as pictures, large objects, etc.
The Python multiprocessing module itself provides various very efficient inter-process communication methods such as message mechanism, synchronization mechanism, and shared memory
.Understanding and mastering the use of various methods of Python inter-process communication, as well as security mechanisms, can help greatly improve program running performance.
2. Introduction to various communication methods between processes The main methods of inter-process communication are summarized as followsAbout inter-process Memory safety of communication
Memory safety means that shared variable exceptions may occur between multiple processes due to simultaneous grabbing, accidental destruction, etc. The Queue, Pipe, Lock, and Event objects provided by the Multiprocessing module have all implemented inter-process communication security mechanisms. Using shared memory communication, you need to track and destroy these shared memory variables yourself in the code, otherwise they may be scrambled or not destroyed normally. Cause system abnormality. Unless the developer is very clear about the usage characteristics of shared memory, it is not recommended to use this shared memory directly, but to use the shared memory through the Manager manager.
Memory Manager Manager
Multiprocessing provides the memory manager Manager class, which can uniformly solve the memory security issues of process communication. Various shared data can be added to the manager, including list , dict, Queue, Lock, Event, Shared Memory, etc., are tracked and destroyed uniformly. 3. Message mechanism communication
Pipe object construction method:
parent_conn, child_conn = Pipe(duplex=True/False)
Parameter description
- duplex=False, the pipeline is one-way communication, only child_conn can send messages, and parent_conn can only receive messages.
- Sample code:
from multiprocessing import Process, Pipe def myfunction(conn): conn.send(['hi!! I am Python']) conn.close() if __name__ == '__main__': parent_conn, child_conn = Pipe() p = Process(target=myfunction, args=(child_conn,)) p.start() print (parent_conn.recv() ) p.join()2) Message Queue Queue communication methodThe Queue class of Multiprocessing was modified on python queue 3.0 version. It can be easily implemented to transfer data between producers and messagers, and the Queue module of Multiprocessing implements the lock security mechanism.
Queue module provides a total of 3 types of queues.
(1) FIFO queue, first in first out,
class queue.Queue(maxsize=0)(2) LIFO queue, last in first out, actually a stack
class queue.LifoQueue(maxsize=0)(3) With priority queue, the lowest priority entry value is listed first
class queue.PriorityQueue(maxsize=0)
The main method of Multiprocessing.Queue class:
queue.full() | |
queue.empty() | |
queue.put(item) | |
queue.get() | |
queue.put_nowait(item), queue.get_nowait() | |
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