python http service flask architecture utility code detailed analysis

dependency library (computing)

flask installation, accelerated using bean source.

pip install flask -i /simple

gevent installation, accelerated using the bean source.

pip install gevent -i /simple

coding

#!/user/bin/env python
# coding=utf-8
"""
@project : TestDemo
@author  : huyi
@file   : 
@ide    : PyCharm
@time   : 2021-10-15 11:58:44
"""
from flask import Flask, request
from  import WSGIServer
from gevent import monkey
import json
 
# Replace python's standard io methods with methods of the same name in gevent, and when io blocks gevent automatically switches to a concatenation.
monkey.patch_all()
 
app = Flask(__name__)
 
 
# Request return entity
class TestResponse:
    def __init__(
            self,
            code,
            success,
            msg, data):
         = code
         = success
         = msg
         = data
 
 
@('/progress', methods=['POST'])
def progress():
    request_data = ()
    print("progress callback:{}".format(request_data))
    return (
        TestResponse(0, True, "progress callback success", None),
        default=lambda obj: obj.__dict__,
        sort_keys=True,
        indent=4)
 
 
@('/result', methods=['POST'])
def result():
    request_data = ()
    print("Results callbacks:{}".format(request_data))
    return (
        TestResponse(0, True, "result callback success", None),
        default=lambda obj: obj.__dict__,
        sort_keys=True,
        indent=4)
 
 
if __name__ == '__main__':
    print("When in doubt, ask the spring breeze.")
    WSGIServer(('0.0.0.0', 8383), app).serve_forever()

Validating results with postman

summarize

The code architecture is simple and effective, without adding too much heavy stuff. If you need asynchronous non-blocking interface calls, you can add a thread pool and throw a thread out of the execution, which is just as applicable.

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