在Java中,IoC / DI是一种非常常见的实践,广泛应用于web应用程序、几乎所有可用的框架和Java EE中。另一方面,也有很多大型的Python web应用程序,但除了Zope(我听说它的编码真的很糟糕)之外,IoC在Python世界中似乎并不常见。(如果你认为我是错的,请举一些例子)。
当然,有一些流行的Java IoC框架的克隆可用于Python,例如springpython。但它们似乎都没有被实际使用。至少,我从来没有碰到过Django或sqlalchemy+<插入您最喜欢的wsgi工具箱在这里>的基于web应用程序使用类似的东西。
在我看来,IoC有合理的优势,可以很容易地取代django-default-user-model,但在Python中广泛使用接口类和IoC看起来有点奇怪,而且不»pythonic«。但是也许有人有更好的解释,为什么IoC在Python中没有被广泛使用。
Django很好地利用了反转控制。例如,由配置文件选择数据库服务器,然后框架向数据库客户机提供适当的数据库包装器实例。
区别在于Python有第一类类型。数据类型(包括类)本身就是对象。如果您想要使用特定的类,只需命名类即可。例如:
if config_dbms_name == 'postgresql':
import psycopg
self.database_interface = psycopg
elif config_dbms_name == 'mysql':
...
之后的代码可以通过以下方式创建数据库接口:
my_db_connection = self.database_interface()
# Do stuff with database.
与Java和c++需要的样板工厂函数不同,Python只需要一两行普通代码就可以完成。这就是函数式编程与命令式编程的优势所在。
Django很好地利用了反转控制。例如,由配置文件选择数据库服务器,然后框架向数据库客户机提供适当的数据库包装器实例。
区别在于Python有第一类类型。数据类型(包括类)本身就是对象。如果您想要使用特定的类,只需命名类即可。例如:
if config_dbms_name == 'postgresql':
import psycopg
self.database_interface = psycopg
elif config_dbms_name == 'mysql':
...
之后的代码可以通过以下方式创建数据库接口:
my_db_connection = self.database_interface()
# Do stuff with database.
与Java和c++需要的样板工厂函数不同,Python只需要一两行普通代码就可以完成。这就是函数式编程与命令式编程的优势所在。
Haven't used Python in several years, but I would say that it has more to do with it being a dynamically typed language than anything else. For a simple example, in Java, if I wanted to test that something wrote to standard out appropriately I could use DI and pass in any PrintStream to capture the text being written and verify it. When I'm working in Ruby, however, I can dynamically replace the 'puts' method on STDOUT to do the verify, leaving DI completely out of the picture. If the only reason I'm creating an abstraction is to test the class that's using it (think File system operations or the clock in Java) then DI/IoC creates unnecessary complexity in the solution.
似乎人们真的不明白依赖注入和控制反转意味着什么了。
使用控制反转的实践是让类或函数依赖于其他类或函数,但是与其在类或函数代码中创建实例,不如将它们作为参数接收,这样就可以实现松耦合。这有很多好处,如更多的可测试性和实现利斯科夫替换原理。
You see, by working with interfaces and injections, your code gets more maintainable, since you can change the behavior easily, because you won't have to rewrite a single line of code (maybe a line or two on the DI configuration) of your class to change its behavior, since the classes that implement the interface your class is waiting for can vary independently as long as they follow the interface. One of the best strategies to keep code decoupled and easy to maintain is to follow at least the single responsibility, substitution and dependency inversion principles.
What's a DI library good for if you can instantiate an object yourself inside a package and import it to inject it yourself? The chosen answer is right, since java has no procedural sections (code outside of classes), all that goes into boring configuration xml's, hence the need of a class to instantiate and inject dependencies on a lazy load fashion so you don't blow away your performance, while on python you just code the injections in the "procedural" (code outside classes) sections of your code.