python使用__slots__让你的代码更加节省内存

前言

在默认情况下,Python的新类和旧类的实例都有一个字典来存储属性值。这对于那些没有实例属性的对象来说太浪费空间了，当需要创建大量实例的时候，这个问题变得尤为突出。

因此这种默认的做法可以通过在新式类中定义了一个__slots__属性从而得到了解决。__slots__声明中包含若干实例变量，并为每个实例预留恰好足够的空间来保存每个变量，因此没有为每个实例都创建一个字典，从而节省空间。

本文主要介绍了关于python使用__slots__让你的代码更加节省内存的相关内容，分享出来供大家参考学习，下面话不多说了，来一起看看详细的介绍吧

现在来说说python中dict为什么比list浪费内存？

和list相比，dict 查找和插入的速度极快，不会随着key的增加而增加；dict需要占用大量的内存，内存浪费多。

而list查找和插入的时间随着元素的增加而增加；占用空间小，浪费的内存很少。

python解释器是Cpython，这两个数据结构应该对应C的哈希表和数组。因为哈希表需要额外内存记录映射关系，而数组只需要通过索引就能计算出下一个节点的位置，所以哈希表占用的内存比数组大，也就是dict比list占用的内存更大。

如果想更加详细了解，可以查看C的源代码。python官方链接：https://www.python.org/downloads/source/

如下代码是我从python官方截取的代码片段：

List 源码：

typedef struct {
 PyObject_VAR_HEAD
 /* Vector of pointers to list elements. list[0] is ob_item[0], etc. */
 PyObject **ob_item;
 
 /* ob_item contains space for 'allocated' elements. The number
 * currently in use is ob_size.
 * Invariants:
 * 0 <= ob_size <= allocated
 * len(list) == ob_size
 * ob_item == NULL implies ob_size == allocated == 0
 * list.sort() temporarily sets allocated to -1 to detect mutations.
 *
 * Items must normally not be NULL, except during construction when
 * the list is not yet visible outside the function that builds it.
 */
 Py_ssize_t allocated;
} PyListObject;

Dict源码：

/* PyDict_MINSIZE is the minimum size of a dictionary. This many slots are
 * allocated directly in the dict object (in the ma_smalltable member).
 * It must be a power of 2, and at least 4. 8 allows dicts with no more
 * than 5 active entries to live in ma_smalltable (and so avoid an
 * additional malloc); instrumentation suggested this suffices for the
 * majority of dicts (consisting mostly of usually-small instance dicts and
 * usually-small dicts created to pass keyword arguments).
 */
#define PyDict_MINSIZE 8
 
typedef struct {
 /* Cached hash code of me_key. Note that hash codes are C longs.
 * We have to use Py_ssize_t instead because dict_popitem() abuses
 * me_hash to hold a search finger.
 */
 Py_ssize_t me_hash;
 PyObject *me_key;
 PyObject *me_value;
} PyDictEntry;
 
/*
To ensure the lookup algorithm terminates, there must be at least one Unused
slot (NULL key) in the table.
The value ma_fill is the number of non-NULL keys (sum of Active and Dummy);
ma_used is the number of non-NULL, non-dummy keys (== the number of non-NULL
values == the number of Active items).
To avoid slowing down lookups on a near-full table, we resize the table when
it's two-thirds full.
*/
typedef struct _dictobject PyDictObject;
struct _dictobject {
 PyObject_HEAD
 Py_ssize_t ma_fill; /* # Active + # Dummy */
 Py_ssize_t ma_used; /* # Active */
 
 /* The table contains ma_mask + 1 slots, and that's a power of 2.
 * We store the mask instead of the size because the mask is more
 * frequently needed.
 */
 Py_ssize_t ma_mask;
 
 /* ma_table points to ma_smalltable for small tables, else to
 * additional malloc'ed memory. ma_table is never NULL! This rule
 * saves repeated runtime null-tests in the workhorse getitem and
 * setitem calls.
 */
 PyDictEntry *ma_table;
 PyDictEntry *(*ma_lookup)(PyDictObject *mp, PyObject *key, long hash);
 PyDictEntry ma_smalltable[PyDict_MINSIZE];
};

PyObject_HEAD 源码:

#ifdef Py_TRACE_REFS
/* Define pointers to support a doubly-linked list of all live heap objects. */
#define _PyObject_HEAD_EXTRA  \
 struct _object *_ob_next;  \
 struct _object *_ob_prev;
 
#define _PyObject_EXTRA_INIT 0, 0,
 
#else
#define _PyObject_HEAD_EXTRA
#define _PyObject_EXTRA_INIT
#endif
 
/* PyObject_HEAD defines the initial segment of every PyObject. */
#define PyObject_HEAD   \
 _PyObject_HEAD_EXTRA  \
 Py_ssize_t ob_refcnt;  \
 struct _typeobject *ob_type;

PyObject_VAR_HEAD 源码:

/* PyObject_VAR_HEAD defines the initial segment of all variable-size
 * container objects. These end with a declaration of an array with 1
 * element, but enough space is malloc'ed so that the array actually
 * has room for ob_size elements. Note that ob_size is an element count,
 * not necessarily a byte count.
 */
#define PyObject_VAR_HEAD  \
 PyObject_HEAD   \
 Py_ssize_t ob_size; /* Number of items in variable part */

现在知道了dict为什么比list 占用的内存空间更大。接下来如何让你的类更加的节省内存。

其实有两种解决方案：

第一种是使用__slots__ ；另外一种是使用Collection.namedtuple 实现。

首先用标准的方式写一个类：

#!/usr/bin/env python

class Foobar(object):
 def __init__(self, x):
 self.x = x

@profile
def main():
 f = [Foobar(42) for i in range(1000000)]

if __name__ == "__main__":
 main()

然后，创建一个类Foobar()，然后实例化100W次。通过@profile查看内存使用情况。

运行结果：

该代码共使用了372M内存。

接下来通过__slots__代码实现该代码：

#!/usr/bin/env python

class Foobar(object):
 __slots__ = 'x'
 def __init__(self, x):
 self.x = x
@profile
def main():
 f = [Foobar(42) for i in range(1000000)]

if __name__ == "__main__":
 main()

运行结果：

使用__slots__使用了91M内存，比使用__dict__存储属性值节省了4倍。

其实使用collection模块的namedtuple也可以实现__slots__相同的功能。namedtuple其实就是继承自tuple，同时也因为__slots__的值被设置成了一个空tuple以避免创建__dict__。

看看collection是如何实现的：

collection 和普通创建类方式相比，也节省了不少的内存。所在在确定类的属性值固定的情况下，可以使用__slots__方式对内存进行优化。但是这项技术不应该被滥用于静态类或者其他类似场合，那不是python程序的精神所在。

总结

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