Usage mutilprocess module

mutilprocess usage

Process class introduced

Use the Process Class objects generated

Call the object's methods

start()

Open sub-process

join（）

Waiting for the child process to finish and then execute the following code

pid and ppid

View pid

• Import current_process
  • current_process().pid
• Check the child process pid property
  • p.pid
• Import os
  • os.get_pid()

View ppid

• Import os
  • os.get_ppid()

os methods can view the current process and the parent process pid in any process

Daemon

process(daemon= true)

When the last line of the parent process, daemon to execute no matter what the place will end

PS: The last line of code is the parent process, rather than when the program finishes running of the parent process

terminate（）

Instruction to the operating system sends an interrupt program

is_alived

The return value is of type bool

Determine whether the process is still running

name

Check the name of the process

Typically Process-1 [-2,] and the like, meaningless

JoinableQueue

It differs from the Queue in more than a join method

Recognize join method

• Task_done called once every come up with a data () method
• If there is data in the current queue
• will join up here until the queue is empty of data, the program will continue

Producer consumer model

from multiprocessing import  Queue,Process

def producer(q,food,name):
    for i in range(10):
        res = f"{name}生产{food}，编号：{i}"
        q.put(res)
        print(res)
    q.put('')

def consumer(q,name):
    while True:
        res = q.get()
        if not res :break
        print(f"{name}吃了{res}")


if __name__ == '__main__':
    q = Queue(5)
    p1 = Process(target=producer,args=(q,'cake','mark'))
    p2 = Process(target=producer,args=(q,'mike','nick'))
    c1 = Process(target=consumer,args=(q,'jiangheng'))
    c2 = Process(target=consumer,args=(q,'yanzhibing'))
    c3 = Process(target=consumer,args=(q,'yanzhibing'))
    p1.start()
    p2.start()
    c1.start()
    c2.start()
    q.put(None)
    c3.start()

Producers

• Production Data

Buffer

• Manufacturer data into the buffer,
  the consumer is removed from the data buffer

consumer

• Data processing

advantage

• Decoupling
• Support concurrent
• Support busy uneven
• Producer consumer model, greatly improving production efficiency producer, but also greatly enhance the efficiency of consumer spending.

queue

Pipeline + Lock

• Pipeline: Based on the underlying shared memory
• Lock: Lock process

q=Queue（n）

• Generating a queue object, set the queue size

q.put()

• Any data type into the

q.get()

• The first queue data fetch

The default parameters put / get the

• block
  • The default is true
    • When the queue is full, it will then put into a wait state
    • When the queue is empty, and then you will get into a wait state
  • When is False
    • When the queue is full, then put wrong will throw
    • When the queue is empty, and then get the wrong will throw
• timeout
  • Only when the block is True, it makes sense
  • Set the duration of the wait state,
    beyond being given the same time

Process lock

from multiprocessing import Lock

• Import Lock

lock = Lock()

• Defined lock object

lock.acquire()

• Lock for your particular part of the code
• Note that, when the lock is locked, other processes can not run the code in this section

lock.release()

• Unlock
• Only unlocked, other processes can grab the lock to continue to run their own code

Note: In multi-process is to ensure that multiple processes are using the same lock

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