Python Multi-Threading usage

In the actual programming process often need to wrap tasks into multi-process or multi-threading, multi-process and multi-threading is the difference between multiple threads is memory sharing, variables and other shared, multi-process process is independent operation, so the creation of multi-threaded or multi-process depends on the different requirements.

Python because of the mechanism of global locking, so in Python in the multi-threaded run is actually only used a CPU, however, multi-threaded run is much faster than a single-threaded run.

to threading. Thread, there are roughly two ways of creating multithreading in Python.

Mode 1

Call threading in the subclass. Thread class

Import Threading. Thread

Class Metric_collector (threading. Thread):

def __init__ (self):

Threading. Thread.__init__ (self)

def run (self):

Print "Testing ..."

Time.sleep (10)

For I in range (10):

t = Metric_collector ()

T.start ()

Threading. The thread class also has the run () method, which overrides the run () method in the Metric_collector class so that the write code reads more structurally.

Mode 2

In threading. Call method in thread instance

Import Threading. Thread

Class Metric_collector (object):

def __init__ (self):

Pass

def move (self, var1, var2):

Print "testing.....%s%s"% (var1, var2)

Collector = Metric_collector ()

For I in range (10):

T=threading. Thread (target = colector.move, args = (var1, var2))

T.start ()

This method is relatively free in use.

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Python Multi-Threading usage