"type":"string"
时间: 2024-06-03 09:04:58 浏览: 11
"type":"string"是JSON Schema中用于描述某个属性的数据类型的关键字,用于表示该属性的值必须为字符串类型。JSON Schema是一种用于验证JSON数据的规范,用于规定JSON数据的结构和内容。在许多编程语言中,也可以使用类似的方法对数据进行验证。
在Python中,可以使用第三方库jsonschema来验证JSON数据是否符合指定的Schema。下面是一个使用jsonschema验证数据类型的例子:
```python
import jsonschema
from jsonschema import validate
# 定义Schema
schema = {
"type": "object",
"properties": {
"name": {"type": "string"},
"age": {"type": "integer"}
}
}
# 要验证的数据
data = {
"name": "John",
"age": 30
}
# 验证数据是否符合Schema
validate(instance=data, schema=schema)
# 如果验证通过,则不会有任何输出,否则会抛出异常
```
相关问题
props: { id: { type: Number, }, title: { default: '', type: String }, suggest: { default: '', type: String }, zhiyin: { default: '', type: String }, time: { default: '', type: String }, url: { default: '', type: String }, location: { [ latitude: Number, longitude: Number, name: String, address: String, scale: Number,] } },代码纠错
这段代码中存在一个语法错误,需要在 location 属性的方括号中添加一个逗号,如下所示:
```
props: {
id: {
type: Number,
},
title: {
default: '',
type: String
},
suggest: {
default: '',
type: String
},
zhiyin: {
default: '',
type: String
},
time: {
default: '',
type: String
},
url: {
default: '',
type: String
},
location: {
[
latitude: Number,
longitude: Number,
name: String,
address: String,
scale: Number,
],
}
},
```
[ERROR] Could not execute SQL statement. Reason: org.apache.flink.table.api.ValidationException: Column types of query result and sink for 'default_catalog.default_database.enriched_orders1' do not match. Cause: Incompatible types for sink column 'email' at position 4. Query schema: [order_id: INT NOT NULL, user_id: STRING, product_id: STRING, address_id: STRING, number: INT, oder_time: TIMESTAMP(6), order_status: STRING, user_name: STRING, gender: STRING, telephone: STRING, email: STRING, code: STRING, province: STRING, city: STRING, product_name: STRING, price: STRING, type: STRING, factory: STRING] Sink schema: [user_id: INT, user_name: STRING, gender: STRING, telephone: STRING, email: STRING, address_id: INT, code: STRING, province: STRING, city: STRING, product_id: INT, product_name: STRING, price: STRING, type: STRING, factory: STRING, order_id: INT, number: INT, order_time: TIMESTAMP(6), order_status: STRING]
这是一个错误信息,看起来是数据表的列类型不匹配导致的。具体来说,查询结果的列类型与目标表不一致,其中 'email' 列的类型不兼容。查询结果的列类型包括 [order_id: INT NOT NULL, user_id: STRING, product_id: STRING, address_id: STRING, number: INT, oder_time: TIMESTAMP(6), order_status: STRING, user_name: STRING, gender: STRING, telephone: STRING, email: STRING, code: STRING, province: STRING, city: STRING, product_name: STRING, price: STRING, type: STRING, factory: STRING],而目标表的列类型包括[user_id: INT, user_name: STRING, gender: STRING, telephone: STRING, email: STRING, address_id: INT, code: STRING, province: STRING, city: STRING, product_id: INT, product_name: STRING, price: STRING, type: STRING, factory: STRING, order_id: INT, number: INT, order_time: TIMESTAMP(6), order_status: STRING]。需要确认这两个表的列类型是否一致,并作出相应的调整。
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