Python内存池机制的实现

# 简单示例展示内存池效果
import sys

a = 1
b = 1
print(a is b)  # True，小整数使用了内存池

c = 1000
d = 1000
print(c is d)  # Python 3.7+中可能为False，大整数可能不使用内存池

# 查看对象内存占用
import sys

lst = [1, 2, 3]
print(sys.getsizeof(lst))  # 列表对象本身的大小
print(sys.getsizeof(lst) + sum(sys.getsizeof(x) for x in lst))  # 总占用

# 小整数池示例
a = 100
b = 100
print(id(a) == id(b))  # True

x = 1000
y = 1000
print(id(x) == id(y))  # Python 3.7+中通常为False

# 字符串驻留示例
s1 = "hello"
s2 = "hello"
print(s1 is s2)  # True

s3 = "hello world!"
s4 = "hello world!"
print(s3 is s4)  # False，长度超过限制不驻留

t1 = ()
t2 = ()
print(t1 is t2)  # True

# 内存池性能测试
import time

def test_allocation():
    start = time.time()
    for _ in range(1000000):
        _ = {}
    print(f"耗时: {time.time() - start:.4f}秒")

test_allocation()

# 不好的写法
def process_data(data):
    temp = []
    for item in data:
        temp.append(process_item(item))
    return temp

# 好的写法 - 预分配列表
def process_data_optimized(data):
    result = [None] * len(data)  # 预分配
    for i, item in enumerate(data):
        result[i] = process_item(item)
    return result

# 不好的写法
def concatenate_strings(words):
    result = ""
    for word in words:
        result += word  # 每次创建新字符串
    return result

# 好的写法 - 使用join
def concatenate_strings_optimized(words):
    return "".join(words)

# 大量插入删除使用deque
from collections import deque

dq = deque()
dq.append(1)  # 高效
dq.appendleft(2)  # 高效

# 使用tracemalloc分析内存
import tracemalloc

tracemalloc.start()

# 执行一些代码
data = [x for x in range(10000)]

snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('lineno')

for stat in top_stats[:5]:
    print(stat)

import gc

# 手动触发垃圾回收
gc.collect()

# 禁用/启用GC
gc.disable()
# 执行关键性能代码
gc.enable()