Python uses inter-process communication

In Python's multi-process programming, inter-process communication (IPC) is a very important concept. Since each process has its own independent memory space, they cannot share data directly. In order to realize data exchange between processes, PythonmultiprocessingModules provide a very useful tool—Queue. This article will introduce in detailThe basic concepts, usage methods and applicable scenarios of Python beginner programmers.

1. What is?

It's PythonmultiprocessingA class in the module for safely passing data between multiple processes. It's similar to the Python standard library, but is specifically used in multi-process environments.QueueIs a first-in-first-out (FIFO) data structure that can safely pass data between multiple processes.

2. Why is it necessary?

In multi-process programming, since each process has its own independent memory space, they cannot share data directly. In order to realize data exchange between processes, we need to use some special mechanisms, such asQueue、Pipe、Managerwait.

The function is to provide a secure, thread- and process-safe queue for passing data between multiple processes. passQueue, We can easily share data between multiple processes without worrying about data competition and synchronization issues.

3. How to use it?

useVery simple. You just need to create oneQueueobject, and pass this object between multiple processes, and then useput()andget()Method to send and receive data.

3.1 Basic usage

Here is a simple example showing how to use itPass data between two processes:

import multiprocessing

# Define a producer processdef producer(queue):
    for i in range(5):
        item = f"data {i}"
        print(f"Producer puts it in: {item}")
        (item)

# Define a consumer processdef consumer(queue):
    while True:
        item = ()
        if item is None:
            break
        print(f"Consumers take it out: {item}")

if __name__ == "__main__":
    # Create a Queue object    queue = ()

    # Create producer and consumer processes    producer_process = (target=producer, args=(queue,))
    consumer_process = (target=consumer, args=(queue,))

    # Start the process    producer_process.start()
    consumer_process.start()

    # Wait for the producer process to complete    producer_process.join()

    # Put in a None to notify the consumer process to end    (None)

    # Wait for the consumer process to complete    consumer_process.join()

In this example, we define a producer processproducerand a consumer processconsumer. Producer processQueuePut data into the consumer process fromQueueExtract data from. To notify the consumer process to end, we will follow the producer process toQueuePut one inNone。

3.2 Other operations of queues

Other common queue operations are also supported, such as:

• qsize(): Returns the number of items in the queue (note: this method may be inaccurate in a multi-process environment).
• empty(): Determine whether the queue is empty.
• full(): Determine whether the queue is full.

For example, determine whether the queue is empty:

if ():
    print("Quote is empty")

3.3 Queue blocking and timeout

Queueofget()andput()The method is blocking by default, that is, if the queue is empty,get()Will block until data is available; if the queue is full,put()Will block until space is available. You can also usetimeoutParameters to set the timeout:

try:
    item = (timeout=1)  # Wait for 1 secondexcept :
    print("Quote is empty, timeout")

4. Applicable scenarios

Applicable to the following scenarios:

• Task distribution: in a multi-process environment, it can be usedQueueDistribute tasks to multiple worker processes.
• Data Sharing: Share data between multiple processes, such as logging, result collection, etc.
• Inter-process communication: pass messages between multiple processes to achieve coordination and synchronization between processes.

5. Summary

It is a very useful tool in Python multi-process programming, which can help us safely pass data between multiple processes. passQueue, We can easily share data between multiple processes without worrying about data competition and synchronization issues.

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