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This article introduces the relevant knowledge of "Python multi-process knowledge collation". In the operation of actual cases, many people will encounter such a dilemma. Next, let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

1.Python multiprocess

Type CPU intensive operation IO intensive operation network request intensive operation linear operation 94.9182499622.461999957.3296 multithread operation 101.170000124.86050010.505333265 multiprocess operation 53.88999912.784000040.504500032

From the above table, we can see:

Multithreading does not seem to have a great advantage under IO-intensive operations (perhaps the heavy tasks of IO operations can show the advantage), and the performance of multithreading is obviously worse than that of single-thread linear execution under CPU-intensive operations, but for busy thread blocking operations such as network requests, the advantage of multithreading is very significant.

Multiple processes can show performance advantages in both CPU-intensive and IO-intensive, as well as network request-intensive (operations that often cause thread blocking). However, in similar network request-intensive operations, it is almost the same as multithreading, but it takes more resources such as CPU, so in this case, we can choose multithreading to execute.

Constructor description method description target execution method name process name args execution method instance method description is_alive () check whether the process is running join () blocking process start () start process run () is called by start () stop process property description name process name pid process number daemon daemon

two。 Examples

2.1 use multithreading to run functions

Import multiprocessing as mpdef count (): for i in range (1000): print (I) # run the next code only when the program is the main program # each python module (python file, that is, test.py and import_test.py here) contains the built-in # variable _ _ name__, when the running module is executed, _ _ name__ equals the file name (including the suffix .py) If # import is in another module, _ _ name__ equals the module name (excluding the suffix .py). And "_ _ main__" equals the name of the file executed before # (including the suffix .py). Then when the module is executed directly, _ _ name__ = = 'main' knot # is true. If _ _ name__ = = "_ _ main__": # Note that the function called by target here is the function name, not with (); in addition, the first letter of Process is capitalized p1=mp.Process (target=count) p2=mp.Process (target=count) p1.start () p2.start ()

We can see a fragment like this in the run result:

one hundred and fifty two

nine hundred and fifty three

one hundred and fifty three

one hundred and fifty four

one hundred and fifty five

one hundred and fifty six

one hundred and fifty seven

nine hundred and fifty four

one hundred and fifty eight

nine hundred and fifty five

one hundred and fifty nine

nine hundred and fifty six

one hundred and sixty

Indicates that in the process of executing the first loop, the second process starts to execute concurrently, and the two loop processes are carried out at the same time.

2.2 daemon

Import multiprocessing as mpdef count1 (): for i in range (10): print (I) def count2 (): for i in range (10): print (iTun10) if _ _ name__ = "_ _ main__": p1=mp.Process (target=count1) p2=mp.Process (target=count2) # the properties of the process must be set p1.daemon=Truep1.start () p2.start () print ("Done") before the process starts

The result of the output is:

Done

0

ten

twenty

thirty

forty

fifty

sixty

seventy

eighty

ninety

We can see that the main program ends first and prints "Done". Because the process of count1 is not protected, the count1 process is terminated after the end of the main program. However, due to the degree of protection set by count2, the count2 process does not terminate with the termination of the main program.

2.3 lock

Lock is used to handle access to shared resources and avoid access conflicts

2.4 Semaphore

Semaphore restricts the number of visits

2.5 Event

Event is used to realize synchronous communication between processes.

2.6 Queue

Multi-process security queue, which is used to realize data transfer between processes.

2.7 Pipe

2.8 Pool

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