JsonSchema
使用fastjsonschema来校验数据
# 导入验证器
import json
import fastjsonschema
# 读取schema
with open('../schema/oneof-schema.json', encoding='utf8') as f:
my_schema = json.load(f)
# json数据:
with open('../data/test.json', encoding='utf8') as f:
json_data = json.load(f)
# 验证:
fastjsonschema.validate(my_schema, json_data)
使用jsonschema来校验数据
import json
# 导入验证器
from jsonschema import validate, draft7_format_checker, SchemaError, ValidationError
if __name__ == '__main__':
with open('../schema/MySchema.json', encoding='utf8') as f:
my_schema = json.load(f)
# json数据:
with open('../data/cece.json', encoding='utf8') as f:
json_data = json.load(f)
# error_list = check_type(my_schema, json_data)
# print(error_list)
# 验证:
try:
validate(instance=json_data, schema=my_schema, format_checker=draft7_format_checker)
# Draft7Validator.format_checker
except SchemaError as serr:
print("schema 错误 【%s】 \nschema错误" % str(serr))
except ValidationError as verr:
print("数据 错误 【%s】 \n数据错误" % str(verr))
MySchema
JSONSchema缺点
- 错误提示英文
- 校验数据为一步步校验,遇到错误停止
自定义JSONSchema
schema遵循 http://json-schema.org/,
"$schema": "http://json-schema.org/draft-07/schema#",
使用方法 >>>> 点击这里
代码
个人编写的校验的代码,自定义成分较多
目前仅仅扩展了 string类型的数据 format 的选型判断
CheckDataUti.py
import re
import time
# email 正则表达式
EMAIL_REGEX = "^\w+([-+.]\w+)*@\w+([-.]\w+)*\.\w+([-.]\w+)*$"
# URL 正则表达式
URL_REGEX = "^[a-zA-z]+://[^\s]*$"
# PHONE 正则表达式
PHONE_REGEX = "^([1][3,4,5,6,7,8,9])\d{9}$"
# 身份证 正则表达式
ID_CARD_REGEX = "^((\d{18})|([0-9x]{18})|([0-9X]{18}))$"
# 邮政编码 正则表达式
ZIP_CODE_REGEX = "^[1-9]\d{5}(?!\d)$"
# IP 地址 正则表达式
IP_REGEX = "^\d+\.\d+\.\d+\.\d+$"
# 正整数
INTEGER_REGEX = "^[1-9]\d*$"
ERR_LIST = []
COMMON_ERR_LIST = []
def log_error(msg, data, schema, is_common=False):
"""
打印错误日志
"""
err_log = "%s,数据:【%s】,校验规则: %s" % (str(msg), str(data) + " type of " + str(type(data).__name__), str(schema))
if not is_common:
ERR_LIST.append(err_log)
print("=================================================")
print(err_log)
print("=================================================")
else:
COMMON_ERR_LIST.append(err_log)
def check_object(data, schema, is_common):
"""
校验对象格式
【 properties、required、minProperties、maxProperties、patternProperties、additionalProperties 】
"""
if type(data) != dict:
log_error("当前校验的json不是一个对象格式", data, schema, is_common)
else:
# 获取当前校验数据的所有key
keys = dict.keys(data)
# 处理必需值
if "required" in schema:
required_schema = schema['required']
for schema_key in required_schema:
if schema_key not in keys:
log_error("字段【%s】必填" % schema_key, data, schema, is_common)
# 处理最小key和最大key
if "minProperties" in schema:
min_properties = schema['minProperties']
if len(keys) < min_properties:
log_error("校验数据的key个数小于【%s】" % str(min_properties), data, schema, is_common)
if "maxProperties" in schema:
max_properties = schema['maxProperties']
if len(keys) > max_properties:
log_error("校验数据的key个数大于【%s】" % str(max_properties), data, schema, is_common)
# 处理具体的key
if "properties" in schema:
# 处理 properties
properties_schema = schema['properties']
schema_keys = dict.keys(properties_schema)
for data_key in schema_keys:
if data_key in data:
check_data(properties_schema[data_key], data[data_key])
# 处理满足正则表达式的key
if "patternProperties" in schema:
# 处理 properties
pattern_properties = schema['patternProperties']
schema_keys = dict.keys(pattern_properties)
# 循环所有正则表达式的key
for schema_key in schema_keys:
# 循环当前待校验的数据key
for data_key in keys:
# 仅仅处理满足正则表达式的key数据
if re.match(schema_key, data_key):
check_data(pattern_properties[schema_key], data[data_key])
def check_array(data, schema, is_common):
"""
校验数组格式
【 items、additionalItems、minItems、maxItems、uniqueItems 】
"""
if type(data) != list:
log_error("当前校验的json不是数组格式", data, schema, is_common)
else:
# minItems、maxItems
# 判断最小值
if "minItems" in schema:
min_items = schema['minItems']
if len(data) < min_items:
log_error("当前校验的数据数组长度小于【%s】" % str(min_items), data, schema, is_common)
# 判断最大值
if "maxItems" in schema:
max_properties = schema['maxItems']
if len(data) > max_properties:
log_error("当前校验的数据数组长度大于【%s】" % str(max_properties), data, schema, is_common)
# uniqueItems true 数组元素不能重复
if "uniqueItems" in schema:
unique_items_schema = schema['uniqueItems']
if unique_items_schema:
# 数组元素不能重复
try:
if len(set(data)) != len(data):
log_error("当前校验的数据数组元素不能重复", data, schema, is_common)
except TypeError:
# 存在数组内部元素是dict格式
pass
# 判断每一个items
if "items" in schema:
items_schema = schema["items"]
# 判断items_schema 是数组还是对象
if type(items_schema) is list:
# 如果是数组 每一个item都是一个jsonSchema 索引对应的数组内索引的格式
index = 0
for item_sc in items_schema:
check_data(item_sc, data[index])
index += 1
# additionalItems该关键字只有在items是数组的时候才会有效
# additionalItems 除了上述规定之外的数据必需符合指定的规则
if "additionalItems" in schema:
additional_items_schema = schema['additionalItems']
for i in range(index, len(data)):
check_data(additional_items_schema, data[i])
# items如果是对象 当前schema规范了数组内所有元素的格式
elif type(items_schema) is dict:
for item_data in data:
check_data(items_schema, item_data)
def check_number(data, schema, is_common):
"""
校验数字类型
"""
if type(data) not in (int, float):
log_error("当前校验的json不是一个数字格式", data, schema, is_common)
else:
# 判断最大值 maximum 如果exclusiveMaximum该关键字是True 包含本身
if "maximum" in schema:
maximum_schema = schema['maximum']
if 'exclusiveMaximum' in schema and schema['exclusiveMaximum']:
if data >= maximum_schema:
log_error("当前校验的数据大于等于【%s】" % maximum_schema, data, schema, is_common)
else:
if data > maximum_schema:
log_error("当前校验的数据大于【%s】" % maximum_schema, data, schema, is_common)
# minimum、exclusiveMinimum
if "minimum" in schema:
minimum_schema = schema['minimum']
if 'exclusiveMinimum' in schema and schema['exclusiveMinimum']:
if data <= minimum_schema:
log_error("当前校验的数据小于等于【%s】" % minimum_schema, data, schema, is_common)
else:
if data < minimum_schema:
log_error("当前校验的数据小于【%s】" % minimum_schema, data, schema, is_common)
# multipleOf 整除
if "multipleOf" in schema:
multiple_of_schema = schema['multipleOf']
if not data % multiple_of_schema == 0:
log_error("当前校验的数据不能被%s整除" % multiple_of_schema, data, schema, is_common)
def check_str(data, schema, is_common):
"""
校验字符串类型
涉及的关键字 【maxLength、minLength、pattern、format】
"""
if type(data) != str:
log_error("当前校验的数据不是一个字符串格式", data, schema, is_common)
else:
# maxLength
if "maxLength" in schema:
max_length_schema = schema['maxLength']
if len(data) > max_length_schema:
log_error("当前校验的数据长度大于%d" % max_length_schema, data, schema, is_common)
# minLength
if "minLength" in schema:
min_length_schema = schema['minLength']
if len(data) < min_length_schema:
log_error("当前校验的数据长度小于%d" % min_length_schema, data, schema, is_common)
# pattern
if "pattern" in schema:
pattern_schema = schema['pattern']
if not re.match(pattern_schema, data):
log_error("当前校验的数据不符合正则表达式规则【%s】" % pattern_schema, data, schema, is_common)
# format
if 'format' in schema:
format_schema = schema['format']
if format_schema == 'email' and not re.match(EMAIL_REGEX, data):
log_error("当前校验的数据不是正确的邮箱格式", data, schema, is_common)
elif format_schema == 'phone' and not re.match(PHONE_REGEX, data):
log_error("当前校验的数据不是正确的手机号码格式", data, schema, is_common)
elif format_schema == 'hostname' and not re.match(IP_REGEX, data):
log_error("当前校验的数据不是正确的IP地址格式", data, schema, is_common)
elif format_schema == 'idCard' and not re.match(ID_CARD_REGEX, data):
log_error("当前校验的数据不是正确的身份证格式", data, schema, is_common)
elif format_schema == 'date':
format_patten = '%Y-%m-%d'
if 'format_patten' in schema:
format_patten = schema['format_patten']
try:
time.strptime(data, format_patten)
except ValueError:
log_error("当前校验的数据不是正确的日期格式格式【%s】" % format_patten, data, schema, is_common)
def check_common(schema, data):
"""
校验通用的
涉及到关键字:
【 enum、const、allOf、anyOf、oneOf、not、 if……then…… 】
"""
if "enum" in schema:
enum_schema = schema['enum']
if data not in enum_schema:
log_error("当前校验的数据值不存在【%s】中" % str(enum_schema), data, schema)
if "const" in schema:
const_schema = schema['const']
if data != const_schema:
log_error("当前校验数据值不等于【%s】" % str(const_schema), data, schema)
if "allOf" in schema:
all_of_schema = schema['allOf']
for item_schema in all_of_schema:
check_data(item_schema, data)
if "anyOf" in schema:
any_of_schema = schema['anyOf']
begin_len = len(COMMON_ERR_LIST)
for item_schema in any_of_schema:
check_data(item_schema, data, True)
end_len = len(COMMON_ERR_LIST)
if end_len - begin_len == len(any_of_schema):
log_error("当前校验的数据不符合当前anyof中的任一规则", data, schema)
if "oneOf" in schema:
one_of_schema = schema['oneOf']
begin_len = len(COMMON_ERR_LIST)
for item_schema in one_of_schema:
check_data(item_schema, data, True)
end_len = len(COMMON_ERR_LIST)
if end_len - begin_len != len(one_of_schema) - 1:
log_error("待校验JSON元素不能通过oneOf的校验", data, schema)
if "not" in schema:
not_schema = schema['not']
begin_len = len(COMMON_ERR_LIST)
check_data(not_schema, data, True)
end_len = len(COMMON_ERR_LIST)
if end_len == begin_len:
log_error("待校验JSON元素不能通过not规则的校验", data, schema)
# if……then……
if 'if' in schema:
if_schmea = schema['if']
begin_len = len(COMMON_ERR_LIST)
check_data(if_schmea, data, True)
end_len = len(COMMON_ERR_LIST)
if end_len == begin_len:
if "then" in schema:
then_schema = schema['then']
check_data(then_schema, data, False)
else:
if "else" in schema:
else_schema = schema['else']
check_data(else_schema, data, False)
def get_data_type(data):
"""
获取type
"""
if type(data) == dict:
return 'object'
if type(data) == list:
return 'array'
if type(data) in (int, float):
return 'number'
if type(data) == str:
return 'string'
if type(data) == bool:
return 'boolean'
def check_data(schema, data, is_common=False):
# 优先处理 通用的
check_common(schema, data)
# 没有type的情况
# type 默认为string
type_name = schema['type'] if "type" in schema else get_data_type(data)
if type_name == 'object':
check_object(data, schema, is_common)
elif type_name == 'array':
check_array(data, schema, is_common)
elif type_name in ['integer', 'number']:
check_number(data, schema, is_common)
elif type_name == 'string':
check_str(data, schema, is_common)
# type是布尔类型
elif type_name == 'boolean':
if type(data) != bool:
log_error("当前校验的数据不是一个boolean格式", data, schema, is_common)
JsonSchmea 的数据示例
{
"$schema": "http://json-schema.org/draft-07/schema#",
"type": "object",
"minProperties": 1,
"maxProperties": 200,
"properties": {
"name": {
"type": "string",
"enum": [
"shaofei",
"upuptop",
"pyfysf"
]
},
"email": {
"type": "string",
"format": "email",
"const": "[email protected]"
},
"idCard": {
"type": "string",
"format": "idCard",
"pattern": "\\d+"
},
"phone": {
"type": "string",
"format": "phone"
},
"hostname": {
"type": "string",
"format": "hostname"
},
"createTime": {
"format": "date",
"format_patten": "%Y%m%d"
},
"is": {
"type": "boolean"
},
"age": {
"type": "integer",
"maximum": 20,
"minimum": 1,
"multipleOf": 2
},
"like": {
"type": "array"
}
},
"allOf": [
{
"type": "string"
}
],
"patternProperties": {
"^\\S+123$": {
"type": "integer"
}
},
"required": [
"email"
]
}
使用方式
import json
from CheckDataUti import check_data
if __name__ == '__main__':
with open('../schema/MySchema.json', encoding='utf8') as f:
my_schema = json.load(f)
# json数据:
with open('../data/cece.json', encoding='utf8') as f:
json_data = json.load(f)
check_data(my_schema, json_data)
# print(ERR_LIST)
参考:
schema遵循 http://json-schema.org/,
"$schema": "http://json-schema.org/draft-07/schema#",
使用方法 >>>> 点击这里