Python使用memory_profiler诊断Flask应用内存问题的方法技巧

pip install memory_profiler

from memory_profiler import profile

@profile
def process_data():
    data = [i for i in range(10**6)]  # 分配100万个元素的列表
    result = [d*2 for d in data]     # 生成处理后的列表
    del data                         # 删除原始数据
    return result

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
     3     38.1 MiB     38.1 MiB           1   @profile
     4                                         def process_data():
     5     45.8 MiB      7.7 MiB           1       data = [i for i in range(10**6)]
     6     53.5 MiB      7.7 MiB           1       result = [d*2 for d in data]
     7     45.8 MiB     -7.7 MiB           1       del data
     8     45.8 MiB      0.0 MiB           1       return result

from flask import Flask
from memory_profiler import profile

app = Flask(__name__)

@app.route('/calculate')
@profile
def calculate():
    # 模拟复杂计算
    matrix = [[i*j for j in range(1000)] for i in range(1000)]
    # 模拟数据处理
    stats = [sum(row) for row in matrix]
    return {'stats': stats}

# 启动内存监控并运行Flask应用
mprof run --python python app.py

# 在另一个终端中生成内存使用图表
mprof plot

from memory_profiler import memory_usage

def complex_operation():
    # 记录初始内存
    start_mem = memory_usage(-1)[0]

    # 执行可能消耗内存的操作
    data = process_large_dataset()

    # 计算内存差异
    end_mem = memory_usage(-1)[0]
    print(f"内存消耗: {end_mem - start_mem:.2f} MB")

import time
import threading
from memory_profiler import memory_usage

def monitor_memory(interval=5, duration=300):
    for i in range(duration // interval):
        mem = memory_usage(-1)[0]
        print(f"[{time.ctime()}] 内存使用: {mem:.2f} MB")
        time.sleep(interval)

# 在后台线程中启动监控
threading.Thread(target=monitor_memory, daemon=True).start()

import objgraph

@app.route('/memory-leak')
def memory_leak():
    # 可疑的内存泄漏代码
    cache.setdefault('key', [])
    cache['key'].append(create_large_object())

    # 显示缓存中对象的引用图
    objgraph.show_backrefs([cache['key'][0]], filename='backrefs.png')
    return "Check memory references"