Python Thread and process Learning Notes

Multithreaded vs multiprocess program: a pile of code is stored in a document process in the form of text: a state in which the program runs includes address space, memory, data stack, etc. Each process completes an independent running environment by itself, and multi-process sharing data is an independent running segment of a problem thread and a process. A process can have multiple threads lightweight a process sharing data and context among multiple threads of a process sharing mutual exclusion global interpretation lock (GIL) the execution of python code is controlled by the python virtual machine in the main loop there is a control thread in the use of executing threading directly using threading.Thread to generate Thread examples t = threading.Thread (target=xxx, args= (xxx) ) t.start (): start multithreaded t.join (): wait for the completion of multithreaded execution case 01:

Import threading

Import time

Def loop01 (in01):

Print ("start loop01 at: {0}" .format (time.ctime ()

Print ("loop01 parameter: {0}" .format (in01))

Time.sleep (4)

Print ("end loop01 at: {0}" .format (time.ctime ()

Def loop02 (in01, in02):

Print ('start loop02 at: {0}' .format (time.ctime ()

Print ("loop02 parameter, first parameter {0}, second parameter {1}" .format (in01,in02))

Time.sleep (2)

Print ("end loop02 at: {0}" .format (time.ctime ()

If name = 'main':

Print ("Starting at: {0}" .format (time.ctime ()

T1 = threading.Thread (target=loop01,args= ("loop01 parameter",))

T2 = threading.Thread (target=loop02,args= ("loop02 parameter 1", "loop02 parameter 2"))

T1.start ()

T2.start ()

T1.join ()

T2.join ()

Print ("All done at {0}" .format (time.ctime ()

While True:

Time.sleep (10)