# 簡(jiǎn)單示例展示內(nèi)存池效果
import sys

a = 1
b = 1
print(a is b)  # True，小整數(shù)使用了內(nèi)存池

c = 1000
d = 1000
print(c is d)  # Python 3.7+中可能為False，大整數(shù)可能不使用內(nèi)存池

# 查看對(duì)象內(nèi)存占用
import sys

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

# 小整數(shù)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+中通常為False

# 字符串駐留示例
s1 = "hello"
s2 = "hello"
print(s1 is s2)  # True

s3 = "hello world!"
s4 = "hello world!"
print(s3 is s4)  # False，長(zhǎng)度超過(guò)限制不駐留

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

# 內(nèi)存池性能測(cè)試
import time

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

test_allocation()

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

# 好的寫法 - 預(yù)分配列表
def process_data_optimized(data):
    result = [None] * len(data)  # 預(yù)分配
    for i, item in enumerate(data):
        result[i] = process_item(item)
    return result

# 不好的寫法
def concatenate_strings(words):
    result = ""
    for word in words:
        result += word  # 每次創(chuàng)建新字符串
    return result

# 好的寫法 - 使用join
def concatenate_strings_optimized(words):
    return "".join(words)

# 大量插入刪除使用deque
from collections import deque

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

# 使用tracemalloc分析內(nèi)存
import tracemalloc

tracemalloc.start()

# 執(zhí)行一些代碼
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

# 手動(dòng)觸發(fā)垃圾回收
gc.collect()

# 禁用/啟用GC
gc.disable()
# 執(zhí)行關(guān)鍵性能代碼
gc.enable()