单例模式的实现方式
将类实例绑定到类变量上
class Singleton(object): _instance = None def __new__(cls, *args): if not isinstance(cls._instance, cls): cls._instance = super(Singleton, cls).__new__(cls, *args) return cls._instance
但是子类在继承后可以重写__new__以失去单例特性
class D(Singleton): def __new__(cls, *args): return super(D, cls).__new__(cls, *args)
使用装饰器实现
def singleton(_cls): inst = {} def getinstance(*args, **kwargs): if _cls not in inst: inst[_cls] = _cls(*args, **kwargs) return inst[_cls] return getinstance @singleton class MyClass(object): pass
问题是这样装饰以后返回的不是类而是函数,当然你可以singleton里定义一个类来解决问题,但这样就显得很麻烦了
使用__metaclass__,这个方式最推荐
class Singleton(type): _inst = {} def __call__(cls, *args, **kwargs): if cls not in cls._inst: cls._inst[cls] = super(Singleton, cls).__call__(*args) return cls._inst[cls] class MyClass(object): __metaclass__ = Singleton
Tornado中的单例模式运用
来看看tornado.IOLoop中的单例模式:
class IOLoop(object): @staticmethod def instance(): """Returns a global `IOLoop` instance. Most applications have a single, global `IOLoop` running on the main thread. Use this method to get this instance from another thread. To get the current thread's `IOLoop`, use `current()`. """ if not hasattr(IOLoop, "_instance"): with IOLoop._instance_lock: if not hasattr(IOLoop, "_instance"): # New instance after double check IOLoop._instance = IOLoop() return IOLoop._instance
为什么这里要double check?来看个这里面简单的单例模式,先来看看代码:
class Singleton(object): @staticmathod def instance(): if not hasattr(Singleton, '_instance'): Singleton._instance = Singleton() return Singleton._instance
在 Python 里,可以在真正的构造函数__new__里做文章:
class Singleton(object): def __new__(cls, *args, **kwargs): if not hasattr(cls, '_instance'): cls._instance = super(Singleton, cls).__new__(cls, *args, **kwargs) return cls._instance
这种情况看似还不错,但是不能保证在多线程的环境下仍然好用,看图:
出现了多线程之后,这明显就是行不通的。
1.上锁使线程同步
上锁后的代码:
import threading class Singleton(object): _instance_lock = threading.Lock() @staticmethod def instance(): with Singleton._instance_lock: if not hasattr(Singleton, '_instance'): Singleton._instance = Singleton() return Singleton._instance
这里确实是解决了多线程的情况,但是我们只有实例化的时候需要上锁,其它时候Singleton._instance已经存在了,不需要锁了,但是这时候其它要获得Singleton实例的线程还是必须等待,锁的存在明显降低了效率,有性能损耗。
2.全局变量
在 Java/C++ 这些语言里还可以利用全局变量的方式解决上面那种加锁(同步)带来的问题:
class Singleton { private static Singleton instance = new Singleton(); private Singleton() {} public static Singleton getInstance() { return instance; } }
在 Python 里就是这样了:
class Singleton(object): @staticmethod def instance(): return _g_singleton _g_singleton = Singleton() # def get_instance(): # return _g_singleton
但是如果这个类所占的资源较多的话,还没有用这个实例就已经存在了,是非常不划算的,Python 代码也略显丑陋……
所以出现了像tornado.IOLoop.instance()那样的double check的单例模式了。在多线程的情况下,既没有同步(加锁)带来的性能下降,也没有全局变量直接实例化带来的资源浪费。
3.装饰器
如果使用装饰器,那么将会是这样:
import functools def singleton(cls): ''' Use class as singleton. ''' cls.__new_original__ = cls.__new__ @functools.wraps(cls.__new__) def singleton_new(cls, *args, **kw): it = cls.__dict__.get('__it__') if it is not None: return it cls.__it__ = it = cls.__new_original__(cls, *args, **kw) it.__init_original__(*args, **kw) return it cls.__new__ = singleton_new cls.__init_original__ = cls.__init__ cls.__init__ = object.__init__ return cls # # Sample use: # @singleton class Foo: def __new__(cls): cls.x = 10 return object.__new__(cls) def __init__(self): assert self.x == 10 self.x = 15 assert Foo().x == 15 Foo().x = 20 assert Foo().x == 20
def singleton(cls): instance = cls() instance.__call__ = lambda: instance return instance # # Sample use # @singleton class Highlander: x = 100 # Of course you can have any attributes or methods you like. Highlander() is Highlander() is Highlander #=> True id(Highlander()) == id(Highlander) #=> True Highlander().x == Highlander.x == 100 #=> True Highlander.x = 50 Highlander().x == Highlander.x == 50 #=> True

pythonisehybridmodelofcompilationand interpretation:1)thepythoninterspretercompilesourcececodeintoplatform- interpententbybytecode.2)thepytythonvirtualmachine(pvm)thenexecuteCutestestestesteSteSteSteSteSteSthisByTecode,BelancingEaseofuseWithPerformance。

pythonisbothinterpretedAndCompiled.1)它的compiledTobyTecodeForportabilityAcrosplatforms.2)bytecodeisthenInterpreted,允许fordingfordforderynamictynamictymictymictymictyandrapiddefupment,尽管Ititmaybeslowerthananeflowerthanancompiledcompiledlanguages。

在您的知识之际,而foroopsareideal insinAdvance中,而WhileLoopSareBetterForsituations则youneedtoloopuntilaconditionismet

ForboopSareSusedwhenthentheneMberofiterationsiskNownInAdvance,而WhileLoopSareSareDestrationsDepportonAcondition.1)ForloopSareIdealForiteratingOverSequencesLikelistSorarrays.2)whileLeleLooleSuitableApeableableableableableableforscenarioscenarioswhereTheLeTheLeTheLeTeLoopContinusunuesuntilaspecificiccificcificCondond

pythonisnotpuroly interpred; itosisehybridablectofbytecodecompilationandruntimeinterpretation.1)PythonCompiLessourceceCeceDintobyTecode,whitsthenexecececected bytybytybythepythepythepythonvirtirtualmachine(pvm).2)

concateNateListsinpythonwithTheSamelements,使用:1)operatototakeepduplicates,2)asettoremavelemavphicates,or3)listCompreanspearensionforcontroloverduplicates,每个methodhasdhasdifferentperferentperferentperforentperforentperforentperfortenceandordormplications。

pythonisanterpretedlanguage,offeringosofuseandflexibilitybutfacingperformancelanceLimitationsInCricapplications.1)drightingedlanguageslikeLikeLikeLikeLikeLikeLikeLikeThonexecuteline-by-line,允许ImmediaMediaMediaMediaMediaMediateFeedBackAndBackAndRapidPrototypiD.2)compiledLanguagesLanguagesLagagesLikagesLikec/c thresst

Useforloopswhenthenumberofiterationsisknowninadvance,andwhileloopswheniterationsdependonacondition.1)Forloopsareidealforsequenceslikelistsorranges.2)Whileloopssuitscenarioswheretheloopcontinuesuntilaspecificconditionismet,usefulforuserinputsoralgorit


热AI工具

Undresser.AI Undress
人工智能驱动的应用程序,用于创建逼真的裸体照片

AI Clothes Remover
用于从照片中去除衣服的在线人工智能工具。

Undress AI Tool
免费脱衣服图片

Clothoff.io
AI脱衣机

Video Face Swap
使用我们完全免费的人工智能换脸工具轻松在任何视频中换脸!

热门文章

热工具

SublimeText3 Linux新版
SublimeText3 Linux最新版

禅工作室 13.0.1
功能强大的PHP集成开发环境

WebStorm Mac版
好用的JavaScript开发工具

MinGW - 适用于 Windows 的极简 GNU
这个项目正在迁移到osdn.net/projects/mingw的过程中,你可以继续在那里关注我们。MinGW:GNU编译器集合(GCC)的本地Windows移植版本,可自由分发的导入库和用于构建本地Windows应用程序的头文件;包括对MSVC运行时的扩展,以支持C99功能。MinGW的所有软件都可以在64位Windows平台上运行。

Dreamweaver CS6
视觉化网页开发工具