背景
python 的 django 后端在刚启动时内存占用上升很快，导致对应的 pod 内存溢出，很多接口响应慢
需要找到哪行代码导致的内存泄漏，有以下方案
由于项目用的是 python 3.6 的版本，所以采用方案 1 和 4
内存占用检测
controllers/app_monitor_controller.py
import tracemalloc
import logging
from controllers.wrapper import (
get_param, get_int_param, request_wrapper)
@request_wrapper()
def get_tracemalloc(request):
key_type = get_param(request, 'key_type', default='traceback')
limit = get_int_param(request, 'limit', default='10')
snapshot = tracemalloc.take_snapshot()
snapshot = snapshot.filter_traces((
tracemalloc.Filter(False, "<unknown>"),
tracemalloc.Filter(
False, "<frozen importlib._bootstrap_external>"),
tracemalloc.Filter(
False, "<frozen importlib._bootstrap>"),
tracemalloc.Filter(False, tracemalloc.__file__),
top_stats = snapshot.statistics(key_type)
top_result = []
for index, stat in enumerate(top_stats[:limit], 1):
frame = stat.traceback[0]
desc = "#%s: %s:%s: %.1f KiB" % (
index, frame.filename, frame.lineno, stat.size / 1024)
top_result.append({
'desc': desc,
'traceback': stat.traceback.format()
other = top_stats[limit:]
if other:
size = sum(stat.size for stat in other)
total = sum(stat.size for stat in top_stats)
return {
'top_result': top_result,
'other_result': "Total %s, other: %.1f KiB" %
(len(other), size / 1024),
'total_size': "%.1f KiB" % (total / 1024)
@request_wrapper()
def get_memory(request):
limit = get_int_param(request, 'limit', default='1')
from pympler import muppy, summary
all_objects = muppy.get_objects()
sum1 = summary.summarize(all_objects)
logging.info('Summary -----')
summary.print_(sum1)
filter_all_objects = muppy.sort(all_objects)
dicts = [ao for ao in filter_all_objects if isinstance(ao, dict)]
result = []
for d in dicts[-limit:]:
item = f'{len(d)}, {str(d).encode("utf-8")}'
result.append(item)
import objgraph
logging.info('Common -----')
objgraph.show_most_common_types()
logging.info('Growth -----')
objgraph.show_growth(limit=5)
return result
这里需要在项目启动的时候去开启记录内存的功能，否则上面的
get_tracemalloc
功能不可用，当然这里的功能需要在使用结束后立即关闭，否则内存占用较大
apps.py
from django.apps import AppConfig
import tracemalloc
import os
# 减少 pypinyin 的内存占用
os.environ['PYPINYIN_NO_PHRASES'] = 'true'
os.environ['PYPINYIN_NO_DICT_COPY'] = 'true'
class Config(AppConfig):
def ready(self):
from entry.settings import START_RECORD_MEMORY
if not START_RECORD_MEMORY:
return
tracemalloc.start(25)主要用来统计函数执行过程中每行耗时及调用次数
tools/performance_tool.py
from cProfile import Profile
from pstats import Stats
def print_func_cost():
def wrapper2(func):
def wrapper1(*args, **kwargs):
profiler = Profile()
# request = args[0]
result = profiler.runcall(func, *args, **kwargs)
stats = Stats(profiler)
stats.strip_dirs()
stats.sort_stats('cumulative')
# stats.print_stats()
stats.print_callers()
return result
return wrapper1
return wrapper2由以下日志可看出，内存占用较大的部分主要在 protobuf 解码，大文件读取，pypinyin，之后的优化就可以进行下去
07:56:48 apps.py:37 INFO #1: /usr/local/lib/python3.6/site-packages/google/protobuf/text_format.py:682: 114541.4 KiB
07:56:48 apps.py:43 INFO b' File "文件路径", line 1580'
07:56:48 apps.py:43 INFO b' data = text_format.Parse(data, DataTree())'
07:00:05 apps.py:33 INFO #2: 文件路径:1571: 66375.2 KiB
07:00:05 apps.py:37 INFO b"data = data + open(file_path, 'r').read()"
07:00:05 apps.py:33 INFO #8: 文件路径:1569: 2053.4 KiB
07:00:05 apps.py:37 INFO b"data = data + open(file_path, 'r').read()"
07:56:52 apps.py:37 INFO #5: /usr/local/lib/python3.6/site-packages/pypinyin/phrases_dict.py:45476: 2560.4 KiB
07:56:52 apps.py:43 INFO b' File "文件路径", line 26'
07:56:52 apps.py:43 INFO b' from pypinyin import lazy_pinyin'