python工具——Py-Spy

Py-Spy是Python程序的抽样分析器，可视化查看Python程序在哪些地方花了更多时间

1.安装

pip  install py-spy

验证安装是否成功

py-spy -h
py-spy 0.3.3
Sampling profiler for Python programs

USAGE:
    py-spy <SUBCOMMAND>

OPTIONS:
    -h, --help       Prints help information
    -V, --version    Prints version information

SUBCOMMANDS:
    record    Records stack trace information to a flamegraph, speedscope or
              raw file
    top       Displays a top like view of functions consuming CPU
    dump      Dumps stack traces for a target program to stdout
    help      Prints this message or the help of the given subcommand(s)

从上面看到有三个子命令

　　record：生成火焰图

　　top：实时查看每个函数运行时间并统计

　　dump：显示每个python线程的当前调用堆栈

2.示例

main.py

from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()

class Item(BaseModel):
    name: str
    price: float
    is_offer: bool = None

@app.get("/")
def home():
    return {"Hello": "World"}

@app.get("/items/{item_id}")
def read_item(item_id: int, q: str = None):
    return {"item_id": item_id, "q": q}


@app.put("/items/{item_id}")
def update_item(item_id: int, item: Item):
    return {"item_name": item.name, "item_id": item_id}

启动

uvicorn main:app --host 0.0.0.0

（1）record

生成火焰图

 py-spy record -o profile.svg --pid 11004

在Ubuntu下会出现

　　Permission Denied: Try running again with elevated permissions by going 'sudo env "PATH=$PATH" !!'

执行

 sudo env "PATH=$PATH" py-spy record -o profile.svg --pid 11004

使用ab压力测试

ab -n 10000 -c 100 http://localhost:8000/

生成的火焰图

通过生成火焰图分析程序瓶颈，看图中最下方哪个峰顶是平的，程序的性能问题就可以从那入手去解决

（2）top

显示了在python程序中花费最多时间的功能的实时视图

py-spy top --pid 11004

说明：　　

　　参数

按％Own排序：当前在该函数中花费的时间的百分比
按％Total排序：函数及其子级中当前的时间百分比
按OwnTime排序：函数中花费的总时间
按TotalTime排序：该函数及其子项花费的总时间

　　通过OwnTime可以比较直接的看出程序运行中，所占比消耗时间最多的函数

https://github.com/benfred/py-spy

（3）dump

显示每个线程的调用栈

$ sudo env "PATH=$PATH" py-spy dump --pid 1615
Process 1615: /usr/bin/python /home/baby/.local/bin/uvicorn main:app --host 0.0.0.0
Python v3.7.9 (/usr/bin/python3.7)

Thread 1615 (idle): "MainThread"
    select (selectors.py:468)
    _run_once (asyncio/base_events.py:1750)
    run_forever (asyncio/base_events.py:541)
    run_until_complete (asyncio/base_events.py:574)
    run (uvicorn/server.py:48)
    run (uvicorn/main.py:386)
    main (uvicorn/main.py:362)
    invoke (click/core.py:610)
    invoke (click/core.py:1066)
    main (click/core.py:782)
    __call__ (click/core.py:829)
    <module> (uvicorn:8)
Thread 1624 (idle): "ThreadPoolExecutor-0_0"
    _worker (concurrent/futures/thread.py:78)
    run (threading.py:870)
    _bootstrap_inner (threading.py:926)
    _bootstrap (threading.py:890)
Thread 1625 (idle): "ThreadPoolExecutor-0_1"
    _worker (concurrent/futures/thread.py:78)
    run (threading.py:870)
    _bootstrap_inner (threading.py:926)
    _bootstrap (threading.py:890)
Thread 1626 (idle): "ThreadPoolExecutor-0_2"
    _worker (concurrent/futures/thread.py:78)
    run (threading.py:870)
    _bootstrap_inner (threading.py:926)
    _bootstrap (threading.py:890)
Thread 1710 (idle): "ThreadPoolExecutor-0_3"
    _worker (concurrent/futures/thread.py:78)
    run (threading.py:870)
    _bootstrap_inner (threading.py:926)
    _bootstrap (threading.py:890)
Thread 1711 (idle): "ThreadPoolExecutor-0_4"
    _worker (concurrent/futures/thread.py:78)
    run (threading.py:870)
    _bootstrap_inner (threading.py:926)
    _bootstrap (threading.py:890)