我检查了pytest文档，但找不到任何相关内容。我知道pytest --duratiOns= 0会打印出所有测试的运行时。有没有办法让pytest也打印出函数消耗的峰值内存使用情况？否则，我可能只能使用下面的装饰功能。但我想知道是否有更好的方法可以做到这一点。
from functools import wraps def mem_time(func): @wraps(func) def wrapper(*args,**kwargs): # Start of function r0 = resource.getrusage(resource.RUSAGE_SELF) t0 = datetime.datetime.now() # Call function status = func(*args,**kwargs) # End of function r1 = resource.getrusage(resource.RUSAGE_SELF) t1 = datetime.datetime.now() sys.stderr.write('{}: utime {} stime {} wall {}\n'.format(func.__name__,datetime.timedelta(secOnds=r1.ru_utime - r0.ru_utime),datetime.timedelta(secOnds=r1.ru_stime - r0.ru_stime),t1 - t0)) sys.stderr.write('{}: mem {} MB ({} GB)\n'.format(func.__name__,(r1.ru_maxrss - r0.ru_maxrss) / 1000.0,(r1.ru_maxrss - r0.ru_maxrss) / 1000000.0)) return status return wrapper
pytest-monitor 一个新的pytest出色插件，有助于监视资源使用情况，时间安排，内存等。所有指标都存储在sqlite数据库中以进行后期分析
从pypi或conda-forge中检出 pytest-monitor ：
- https://pypi.org/project/pytest-monitor/
- https://github.com/CFMTech/pytest-monitor
示例
Calculated Table =CALCULATETABLE ( SUMMARIZE ( 'SOME_TABLE',[CATEGORY],"COUNT",DISTINCTCOUNT ( 'SOME_TABLE'[SOME_COLUMN] ) ),Dim[Color] = SELECTEDVALUE ( Slicer[SlicerValues] ))
希望这会有所帮助
,内存性能分析：
没有插件可以从pytest获取内存配置文件（据我所知）。使用以下链接进行内存分析
https://github.com/fabianp/memory_profiler
其他参考：https://stackoverflow.com/a/43772305/9595032
累计时间：
https://pypi.org/project/pytest-profiling/
但是要获取使用插件pytest-profiling的所有调用的累积时间
pip install pytest-profiling
用法：
pytest -s -v [file_name] --profile
输出看起来像这样
Profiling (from /home/work/project/analytics/testing/TestCases/cloud/prof/combined.prof):Wed Nov 20 12:09:47 2019 /home/work/project/analytics/testing/TestCases/cloud/prof/combined.prof 437977 function calls (380211 primitive calls) in 3.866 seconds Ordered by: cumulative time List reduced from 683 to 20 due to restriction ncalls tottime percall cumtime percall filename:lineno(function) 1 0.000 0.000 3.866 3.866 runner.py:107(pytest_runtest_call) 1 0.000 0.000 3.866 3.866 python.py:1457(runtest) 1 0.000 0.000 3.866 3.866 hooks.py:275(__call__) 1 0.000 0.000 3.866 3.866 manager.py:59() 1 0.000 0.000 3.866 3.866 manager.py:65(_hookexec) 1 0.000 0.000 3.866 3.866 callers.py:157(_multicall) 1 0.000 0.000 3.866 3.866 python.py:188(pytest_pyfunc_call) 1 0.001 0.001 3.866 3.866 test_abc.py:46(test_abc) 1 0.000 0.000 3.865 3.865 test_abc.py:9(run_abc_test) 1 0.001 0.001 3.854 3.854 dataAnalyzer.py:826(sanitize_data) 1 0.000 0.000 3.773 3.773 Analyzer.py:563(Traffic) 1 0.000 0.000 3.772 3.772 traffic.py:83(false_alarm_checks) 3 0.000 0.000 3.767 1.256 api.py:60(get) 3 0.000 0.000 3.765 1.255 api.py:135(_get_from_overpass) 3 0.000 0.000 3.765 1.255 api.py:101(post) 3 0.000 0.000 3.765 1.255 api.py:16(request) 3 0.000 0.000 3.762 1.254 sessions.py:445(request) 3 0.000 0.000 3.759 1.253 sessions.py:593(send) 3 0.000 0.000 3.757 1.252 adapters.py:393(send) 3 0.000 0.000 3.755 1.252 connectionpool.py:447(urlopen)