Rust 给 Python Web 带来的性能提升

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推荐学习书目

› Learn Python the Hard Way

Python Sites

› PyPI - Python Package Index

› http://diveintopython.org/toc/index.html

› Pocoo

值得关注的项目

› PyPy

› Celery

› Jinja2

› Read the Docs

› gevent

› pyenv

› virtualenv

› Stackless Python

› Beautiful Soup

› 结巴中文分词

› Green Unicorn

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› Pyflakes

› pytest

Python 编程

› pep8 Checker

Styles

› PEP 8

› Google Python Style Guide

› Code Style from The Hitchhiker's Guide

这是一个创建于 450 天前的主题，其中的信息可能已经有所发展或是发生改变。

对比 FastAPI Robyn Gin json 序列化返回性能大概的性能对比如下

wrk -t10 -c 10 -d 20s http://127.0.0.1:xxxx/json

FastAPI (async)

Running 20s test @ http://127.0.0.1:8080/json
  10 threads and 10 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency   835.68us  798.76us  29.00ms   94.21%
    Req/Sec     1.32k   513.33     2.51k    49.45%
  262867 requests in 20.10s, 38.10MB read
Requests/sec:  13078.90
Transfer/sec:      1.90MB

Robyn (sync)

Running 20s test @ http://127.0.0.1:8111/json
  10 threads and 10 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency   358.51us  690.23us  24.51ms   96.09%
    Req/Sec     3.91k     1.70k    9.80k    76.79%
  781679 requests in 20.10s, 75.29MB read
Requests/sec:  38889.89
Transfer/sec:      3.75MB

Robyn (async)

Running 20s test @ http://127.0.0.1:8111/json
  10 threads and 10 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency   517.35us  795.62us  32.63ms   95.72%
    Req/Sec     2.42k     0.87k    6.79k    71.78%
  481576 requests in 20.10s, 46.39MB read
Requests/sec:  23959.48
Transfer/sec:      2.31MB

Gin

Running 20s test @ http://127.0.0.1:8888/json
  10 threads and 10 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency   293.92us  688.32us  27.60ms   94.13%
    Req/Sec     6.87k     2.66k   13.32k    58.32%
  1368505 requests in 20.10s, 195.77MB read
Requests/sec:  68090.01
Transfer/sec:      9.74MB

robyn 基准参考 https://github.com/sansyrox/robyn-comparison-benchmarks

Rust+Python Python 的下一波春天?

第 1 条附言 · 2023-09-07 12:33:36 +08:00

重新修改了返回 json 的方式
```
from robyn import Robyn, jsonify

app = Robyn(__file__)

@app.get("/json")
async def h(request):
return jsonify({
"message": "Hello, world!"
})

app.start(port=8111)

```
测得
```
Running 20s test @ http://127.0.0.1:8111/query
10 threads and 10 connections
Thread Stats Avg Stdev Max +/- Stdev
Latency 562.74us 1.79ms 53.50ms 93.07%
Req/Sec 7.45k 3.76k 16.32k 59.89%
1484168 requests in 20.10s, 159.94MB read
Non-2xx or 3xx responses: 1484168
Requests/sec: 73841.00
Transfer/sec: 7.96MB

```

20 条回复 • 2023-09-08 07:56:28 +08:00

wuwukai007

2023-09-07 11:57:26 +08:00

官方文档就是个简单的 demo ，很怀疑持久性，对比之前的 litestar 文档就很齐全

rrfeng

2023-09-07 12:05:40 +08:00 via Android

不是，我看着数不是被 gin 秒杀了？

Baloneo

2023-09-07 12:36:01 +08:00

@rrfeng 是的

xgdgsc

2023-09-07 12:36:22 +08:00 via Android

https://discourse.julialang.org/t/julia-can-be-better-at-doing-web-a-benchmark/103300 不如优化下 julia 创造 julia 加 rust 的春天

TArysiyehua

2023-09-07 13:20:55 +08:00

这么说吧，我用 python 的那一刻就没考虑性能的问题

so1n

2023-09-07 13:54:05 +08:00

没加数据库调用的测试都是耍流氓...而这个例子的接口连 IO 处理都没...

vicalloy

2023-09-07 13:57:01 +08:00

如果是 Json 序列化性能，可以做 FastAPI 里使用 orjson 进行加速。
https://fastapi.tiangolo.com/advanced/custom-response/#orjsonresponse
如果不想改动现有代码，可以简单的做个 monkey patch 。

import orjson

json.dumps = lambda obj, *args, **kwargs: orjson.dumps(
obj, option=orjson.OPT_NON_STR_KEYS
).decode("utf-8")
json.loads = lambda obj, *args, **kwargs: orjson.loads(obj)

Baloneo

2023-09-07 14:16:41 +08:00

@vicalloy 已经是 orjson 了