# slots魔法

在 Python 中，每个类都有实例属性。默认情况下 Python 用一个字典来保存一个对象的实例属性。这非常有用，因为它允许我们在运行时去设置任意的新属性。

然而，对于有着已知属性的小类来说，它可能是个瓶颈。这个字典浪费了很多内存。Python 不能在对象创建时直接分配一个固定量的内存来保存所有的属性。因此如果你创建许多对象（我指的是成千上万个），它会消耗掉很多内存。 不过还是有一个方法来规避这个问题。这个方法需要使用 `__slots__` 来告诉 Python 不要使用字典，而且只给一个固定集合的属性分配空间。

这里是一个使用与不使用 `__slots__` 的例子：

* 不使用 `__slots__`:

```python
class MyClass(object):
    def __init__(self, name, identifier):
        self.name = name
        self.identifier = identifier
        self.set_up()
    # ...
```

* 使用 `__slots__`:

```python
class MyClass(object):
    __slots__ = ['name', 'identifier']
    def __init__(self, name, identifier):
        self.name = name
        self.identifier = identifier
        self.set_up()
    # ...
```

第二段代码会为你的内存减轻负担。通过这个技巧，有些人已经看到内存占用率几乎40%\~50%的减少。

稍微备注一下，你也许需要试一下 PyPy。它已经默认地做了所有这些优化。

以下你可以看到一个例子，它用 IPython 来展示在有与没有 `__slots__` 情况下的精确内存占用，感谢 <https://github.com/ianozsvald/ipython_memory_usage>

```python
Python 3.4.3 (default, Jun  6 2015, 13:32:34)
Type "copyright", "credits" or "license" for more information.

IPython 4.0.0 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.

In [1]: import ipython_memory_usage.ipython_memory_usage as imu

In [2]: imu.start_watching_memory()
In [2] used 0.0000 MiB RAM in 5.31s, peaked 0.00 MiB above current, total RAM usage 15.57 MiB

In [3]: %cat slots.py
class MyClass(object):
        __slots__ = ['name', 'identifier']
        def __init__(self, name, identifier):
                self.name = name
                self.identifier = identifier

num = 1024*256
x = [MyClass(1,1) for i in range(num)]
In [3] used 0.2305 MiB RAM in 0.12s, peaked 0.00 MiB above current, total RAM usage 15.80 MiB

In [4]: from slots import *
In [4] used 9.3008 MiB RAM in 0.72s, peaked 0.00 MiB above current, total RAM usage 25.10 MiB

In [5]: %cat noslots.py
class MyClass(object):
        def __init__(self, name, identifier):
                self.name = name
                self.identifier = identifier

num = 1024*256
x = [MyClass(1,1) for i in range(num)]
In [5] used 0.1758 MiB RAM in 0.12s, peaked 0.00 MiB above current, total RAM usage 25.28 MiB

In [6]: from noslots import *
In [6] used 22.6680 MiB RAM in 0.80s, peaked 0.00 MiB above current, total RAM usage 47.95 MiB
```


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