Python 参数校验的进化
2024-08-30 09:16:56
事情的起因是感觉目前项目中的参数校验方法写的太简单了,很多时候需要在server层再if else处理,于是就动手准备写一个好用一点的,可以自定义校验参数规则的参数校验器,考虑到要可以灵活的配置就萌生了大概的印象:
- 使用map - 参数A:ruleA,参数B-ruleB..等等,对参数进行规则绑定
- 使用装饰器
- 可扩展,可以自定义校验规则
于是第一个版本实现如下:
版本1
# -*- coding:utf-8 -*-
__author__ = "aleimu"
__date__ = "2018-12-6"
__doc__ = "一个实用的入参校验装饰器--针对目前,前端 url?&a=1&b=2或-d'a=1&b=2c=qwe'形式的非json(所有参数都是str类型)" \
"入参的校验"
import copy
import traceback
from collections import OrderedDict
from functools import wraps
from flask import Flask, json, jsonify, request
app = Flask(__name__)
def verify_args(need=None, length=None, check=None, strip=True, default=(False, None), diy_func=None, release=False):
"""
约束:
1. 简化了传参校验,使用位置传参或者关键词传参(一个参数对应一个参数),不允许使用one to list等python高级传参特性
2. 所有的参数都是str/unicode类型的,前端没有使用json带参数类型的入参方式
:param need: 必须参数,且不能为None或者""
:param length: 参数长度范围
:param check: str的常用类方法/属性如下:
isalnum 判断字符串中只能由字母和数字的组合，不能有特殊符号
isalpha 字符串里面都是字母，并且至少是一个字母，结果就为真，（汉字也可以）其他情况为假
isdigit 函数判断是否全为数字
:param strip:对字段进行前后过滤空格
:param default:将"" 装换成None
:param diy_func:自定义的对某一参数的校验函数格式: {key:func},类似check, diy_func={"a": lambda x: x + "aa"})
:param release:发生参数校验异常后是否依然让参数进入主流程函数
:return:
"""
def wraps_1(f):
@wraps(f)
def wraps_2(*args, **kwargs):
if release:
args_bak = args[:]
kwargs_bak = copy.deepcopy(kwargs) # 下面流程异常时,是否直接使用 原参数传入f todo
print ("in", args, kwargs)
args_template = f.func_code.co_varnames
print("args_template:", args_template)
args_dict = OrderedDict()
req_args_need_list = []
req_args_types_list = []
try:
for i, x in enumerate(args):
args_dict[args_template[i]] = x
sorted_kwargs = sort_by_co_varnames(args_template, kwargs)
args_dict.update(sorted_kwargs)
print("args_dict:", args_dict)
# need
if need:
for k in need:
if k not in args_dict:
req_args_need_list.append(k)
else:
if args_dict[k] == None or args_dict[k] == "":
req_args_need_list.append(k)
if req_args_need_list:
return False, "%s is in need" % req_args_need_list
# strip
if strip:
for k in args_dict:
if args_dict[k]:
args_dict[k] = args_dict[k].strip()
# length
if length:
for k in args_dict:
if k in length:
if not (len(args_dict[k]) >= length[k][0] and len(args_dict[k]) <= length[k][1]):
return False, "%s length err" % k
# default:
if default[0]:
for x in args_dict:
if args_dict[x] == "":
args_dict[x] = default[1]
# check
if check:
for k in check:
check_func = getattr(type(args_dict[k]), check[k], None)
if not (k in args_dict and check_func and check_func(args_dict[k])):
req_args_types_list.append(k)
if req_args_types_list:
return False, "%s type err" % req_args_types_list
# diy_func
if diy_func:
for k in args_dict:
if k in diy_func:
args_dict[k] = diy_func[k](args_dict[k])
except Exception as e:
print("verify_args catch err: ", traceback.format_exc())
if release:
return f(*args_bak, **kwargs_bak)
else:
return False, str(e)
return f(*args_dict.values())
return wraps_2
return wraps_1
def sort_by_co_varnames(all_args, kwargs):
new_ordered = OrderedDict()
for x in all_args:
if x in kwargs:
new_ordered[x] = kwargs[x]
return new_ordered
@app.route("/", methods=["GET", "POST", "PUT"])
def index():
a = request.values.get("a")
b = request.values.get("b")
c = request.values.get("c")
d = request.values.get("d")
e = request.values.get("e")
f = request.values.get("f")
g = request.values.get("g")
status, data = todo(a, b, c, d, e=e, f=f, g=g)
if status:
return jsonify({"code": 200, "data": data, "err": None})
else:
return jsonify({"code": 500, "data": None, "err": data})
@verify_args(need=['a', 'b', 'c'], length={"a": (6, 50)}, strip=True,
check={"b": 'isdigit', "c": "isalnum"},
default=(True, None),
diy_func={"a": lambda x: x + "aa"})
def todo(a, b, c, d, e=' 1 ', f='2 ', g=''):
return True, {"a": a, "b": b, "c": c, "d": d, "e": e, "f": f, "g": g}
if __name__ == "__main__":
app.run(host='0.0.0.0', port=6000, debug=True)
"""
# curl "http://127.0.0.1:6000/" -d "pwd=123&a=1111111&b=2&c=3&d=d&e=eeeeee&f=12345&g="
{
"code": 200,
"data": {
"a": "1111111aa",
"b": "2",
"c": "3",
"d": "d",
"e": "eeeeee",
"f": "12345",
"g": null
},
"err": null
}
# curl "http://127.0.0.1:6000/" -d "pwd=123&a=1111111&b=2&c=3346()*&d=d&e=eeeeee&f=12345&g="
{
"code": 500,
"data": null,
"err": "['c'] type err"
}
# curl "http://127.0.0.1:6000/" -d "pwd=123&a=1111111&b=2&c=&d=d&e=eeeeee&f=12345&g="
{
"code": 500,
"data": null,
"err": "['c'] is in need"
}
# curl "http://127.0.0.1:6000/" -d "pwd=123&a=1111111&b=2&c= 1 &d=d&e=eeeeee&f=12345&g="
{
"code": 200,
"data": {
"a": "1111111aa",
"b": "2",
"c": "1",
"d": "d",
"e": "eeeeee",
"f": "12345",
"g": null
},
"err": null
}
"""
第一个版本切合了当前项目中经常遇到的校验问题,实现起来较简单,基本满足要求.
想要更通用点,更多校验规则一些,就需要每次为verify_args添加参数写if else了,嗯.....有点不优雅啊,于是去看github上有啥好的实现.
找到了如下几个项目:
- https://github.com/keleshev/s... 嗯,1.6K的star,思路一致,实现的优雅,但是不好扩展啊....
- https://github.com/kvesteri/v... 额,Python Data Validation for Humans™. not for me....
- https://github.com/mansam/val... 嗯,思路一致,实现也简单,挺好扩展的,就用它了!
这里说说validator.py ,给个例子
from validator import Required, Not, Truthy, Blank, Range, Equals, In, validate
# let's say that my dictionary needs to meet the following rules...
rules = {
"foo": [Required, Equals(123)],
"bar": [Required, Truthy()],
"baz": [In(["spam", "eggs", "bacon"])],
"qux": [Not(Range(1, 100))] # by default, Range is inclusive
}
# then this following dict would pass:
passes = {
"foo": 123,
"bar": True, # or a non-empty string, or a non-zero int, etc...
"baz": "spam",
"qux": 101
}
print validate(rules, passes)
# (True, {})
# but this one would fail
fails = {
"foo": 321,
"bar": False, # or 0, or [], or an empty string, etc...
"baz": "barf",
"qux": 99
}
print validate(rules, fails)
# (False,
# {
# 'foo': ["must be equal to '123'"],
# 'bar': ['must be True-equivalent value'],
# 'baz': ["must be one of ['spam', 'eggs', 'bacon']"],
# 'qux': ['must not fall between 1 and 100']
# })
嗯,使用第一个版本封装一下validator.py就好了!考虑到需要写个dome来试试,就选了flask,嗯,对了,先去github 上搜一下 flask validator 没准已经有现成的呢,实现思路基本一致,但是......前几个star多的都不令人满意,还是自己造轮子吧.
先实现常见的在route上加装饰器版本,这样的话,就可以直接接收request收到的参数,然后直接校验了,有问题就直接返回错误给调用者,于是有了版本2
版本2
rules_example = {
"a": [Required, Equals("123")], # foo must be exactly equal to 123
"b": [Required, Truthy()], # bar must be equivalent to True
"c": [In(["spam", "eggs", "bacon"])], # baz must be one of these options
"d": [Not(Range(1, 100))], # qux must not be a number between 1 and 100 inclusive
"e": [Length(0, maximum=5)],
"f": [Required, InstanceOf(str)],
"g": [Required, Not(In(["spam", "eggs", "bacon"]))],
"h": [Required, Pattern("\d\d\%")],
"i": [Required, GreaterThan(1, reverse=True, auto=True)], # auto 自动转换成float类型来做比较
"j": [lambda x: x == "bar"],
"k": [Required, Isalnum()], # 判断字符串中只能由字母和数字的组合，不能有特殊符号
"l": [Required, Isalpha()], # 字符串里面都是字母，并且至少是一个字母，结果就为真，（汉字也可以）其他情况为假
"m": [Required, Isdigit()], # 判断字符串是否全为数字
}
def validator_wrap(rules, strip=True, diy_func=None):
"""装饰器版 - 只能检测是否符合规则,不能修改参数
:param rules:参数的校验规则,map
:param strip:对字段进行前后空格检测
:param diy_func:自定义的对某一参数的校验函数格式: {key:func},类似check, diy_func={"a": lambda x: x=="aa"})
"""
def decorator(f):
@wraps(f)
def decorated_func(*args, **kwargs):
try:
args_dict = OrderedDict()
if request.values:
args_dict.update(request.values)
if request.json:
args_dict.update(request.json)
# strip
if strip:
for k in args_dict:
if args_dict[k] and isstr(args_dict[k]):
if args_dict[k][0] == " " or args_dict[k][-1] == " ":
return jsonify({"code": 500, "data": None, "err": "%s should not contain spaces" % k})
# diy_func
if diy_func:
for k in args_dict:
if k in diy_func:
args_dict[k] = diy_func[k](args_dict[k])
# rules
if rules:
result, err = validate(rules, args_dict)
if not result:
return jsonify(
{"code": 500, "data": None, "err": err})
except Exception as e:
print("verify_args catch err: ", traceback.format_exc())
return jsonify({"code": 500, "data": None, "err": str(e)})
return f(*args, **kwargs)
return decorated_func
return decorator
@app.route("/wrap", methods=["GET", "POST", "PUT"])
@validator_wrap(rules=rules_example, strip=True) # 姿势 1:只能检测是否符合规则,不能修改参数,不符合就会直接返回json给调用者
def wrap_example():
a = request.values.get("a")
b = request.values.get("b")
c = request.values.get("c")
d = request.values.get("d")
e = request.values.get("e")
f = request.values.get("f")
g = request.values.get("g")
h = request.values.get("h")
i = request.values.get("i")
j = request.values.get("j")
k = request.values.get("k")
l = request.values.get("l")
m = request.values.get("m")
status, data = todo(a=a, b=b, c=c, d=d, e=e, f=f, g=g, h=h, i=i, j=j, k=k, l=l, m=m)
if status:
return jsonify({"code": 200, "data": data, "err": None})
else:
return jsonify({"code": 500, "data": None, "err": data})
好像挺好的,基本满足要求了,但是再route上加装饰器,那就改变不了参数的值了,虽然有些参数不一定符合要求,但是简单修补一下还是可以用的,还得继续寻找能够改变入参的方式,第一反应是在装饰器中修改request.values或者request.json的值,让进入到主函数后获取更新后的值,上下求索未得门径,request.value.update方法是被禁用的,继续看源码,后面的实现使用了dict的复杂封装,不好改啊,这样太绕了,还是直接调用函数吧,不玩装饰器了.于是又了版本3
版本3
def validator_func(rules, strip=True, default=(False, None), diy_func=None, release=False):
"""函数版-返回dict,代替request.values/request.json
:param rules:参数的校验规则,map
:param strip:对字段进行前后过滤空格
:param default:将"" 装换成None
:param diy_func:自定义的对某一参数的校验函数格式: {key:func},类似check, diy_func={"a": lambda x: x + "aa"})
:param release:发生参数校验异常后是否依然让参数进入主流程函数
"""
args_dict = OrderedDict()
try:
if request.values:
args_dict.update(request.values)
if request.json:
args_dict.update(request.json)
if release:
args_dict_copy = copy.deepcopy(args_dict) # 下面流程异常时,是否直接使用 原参数传入f # fixme
# strip
if strip:
for k in args_dict:
if isstr(args_dict[k]):
args_dict[k] = args_dict[k].strip()
# default
if default[0]:
for x in args_dict:
if args_dict[x] == "":
args_dict[x] = default[1]
# diy_func
if diy_func:
for k in args_dict:
if k in diy_func:
args_dict[k] = diy_func[k](args_dict[k])
# rules
if rules:
result, err = validate(rules, args_dict)
if not result:
return False, err
except Exception as e:
print("verify_args catch err: ", traceback.format_exc()) # TODO
if release:
return True, args_dict_copy
else:
return False, str(e)
return True, args_dict
@app.route("/func", methods=["GET", "POST", "PUT"])
def func_example():
result, request_args = validator_func(rules=rules_example, strip=True) # 姿势 2
if not result:
return jsonify({"code": 500, "data": None, "err": request_args})
a = request_args.get("a")
b = request_args.get("b")
c = request_args.get("c")
d = request_args.get("d")
e = request_args.get("e")
f = request_args.get("f")
g = request_args.get("g")
h = request_args.get("h")
i = request_args.get("i")
j = request_args.get("j")
k = request_args.get("k")
l = request_args.get("l")
m = request_args.get("m")
status, data = todo(a=a, b=b, c=c, d=d, e=e, f=f, g=g, h=h, i=i, j=j, k=k, l=l, m=m)
if status:
return jsonify({"code": 200, "data": data, "err": None})
else:
return jsonify({"code": 500, "data": None, "err": data})
嗯,还行吧,就是不怎么优雅,还是有点喜欢装饰器版本,但是苦于能力有限,不想看ImmutableMultiDict,MultiDict的实现,还是将第一个版本融合一下吧,装饰route不行,装饰todo还不行吗.于是有了版本4
版本4
def validator_args(rules, strip=True, default=(False, None), diy_func=None, release=False):
"""针对普通函数的参数校验的装饰器
:param rules:参数的校验规则,map
:param strip:对字段进行前后过滤空格
:param default:将"" 装换成None
:param diy_func:自定义的对某一参数的校验函数格式: {key:func},类似check, diy_func={"a": lambda x: x + "aa"})
:param release:发生参数校验异常后是否依然让参数进入主流程函数
"""
def decorator(f):
@wraps(f)
def decorated_func(*args, **kwargs):
if release:
args_bak = args[:]
kwargs_bak = copy.deepcopy(kwargs) # 下面流程异常时,是否直接使用 原参数传入f # fixme
try:
args_template = f.func_code.co_varnames
except:
args_template = f.__code__.co_varnames
args_dict = OrderedDict()
try:
for i, x in enumerate(args):
args_dict[args_template[i]] = x
sorted_kwargs = sort_by_co_varnames(args_template, kwargs)
args_dict.update(sorted_kwargs)
# strip
if strip:
for k in args_dict:
if isstr(args_dict[k]):
args_dict[k] = args_dict[k].strip()
# default
if default[0]:
for x in args_dict:
if args_dict[x] == "":
args_dict[x] = default[1]
# diy_func
if diy_func:
for k in args_dict:
if k in diy_func:
args_dict[k] = diy_func[k](args_dict[k])
# rules
if rules:
result, err = validate(rules, args_dict)
if not result:
return False, err
except Exception as e:
print("verify_args catch err: ", traceback.format_exc())
if release:
return f(*args_bak, **kwargs_bak)
else:
return False, str(e)
return f(*args_dict.values())
return decorated_func
return decorator
@validator_args(rules=rules_example, strip=True) # 姿势 3
def todo(a, b, c, d, e, f, g, h, i, j, k, l, m):
return True, {"a": a, "b": b, "c": c, "d": d, "e": e, "f": f, "g": g, "h": h, "i": i, "j": j, "k": k, "l": l,
"m": m}
哎,就这样吧,打包一下,随便选吧,爱用哪个用哪个,反正我都写出来了.简单说就是:
- validator_func 针对flask的request.json/requests.values的参数校验以及修改,修改的方式有限,可以自己控制
- validator_wrap 是针对flask route的装饰器,针对request.json/requests.values的参数校验,只是校验,当然校验的方式可以自己写扩展
- validator_args 针对普通函数的参数校验以及修改,注意不要使用python传参的高级特性(一个参数对应多个值),这个方法可以脱离flask使用,所以如果需要就直接copy过去吧.
嗯,最后还是分享一下到git上吧, https://github.com/aleimu/flask-validator 喜欢的点个star.
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