API中缺少JSON字段时的防御条件

Question

我正在开发一个小的Python，以便从forecast.io获得天气数据，一旦我得到JSON文档，我就调用一个类，以便创建一个要保存在数据库中的新记录。问题是有些字段(也是include中的属性)在API中并不总是被告知，所以我必须包含某种防御代码，否则在找不到字段时脚本就会中断。

我找到了@Alex的答案，它的接缝非常好：如果键可能不存在，则从Python读取

如果您想做一些与使用默认值不同的事情(例如，在缺少键时完全跳过打印)，那么您需要更多的结构，即： 对于结果中的r:如果r中的'key_name‘：打印r’‘key_name’ 或 对于结果中的r:除了KeyError: pass之外，尝试:打印r‘’key_name‘

但是，我想知道我是否必须在我想要保存的每一个领域中都包括一个" if“或"try”，还是有一个更漂亮的方法来做到这一点？(我想拯救27个领域和27个“如果”似乎丑陋)

这是我到目前为止掌握的代码：

from datetime import datetime

import tornado.web
import tornado.httpclient
from tornado import gen

from src.db.city import list_cities
from src.db.weather import Weather

from motorengine import *


@gen.coroutine
def forecastio_api():
    http_client = tornado.httpclient.AsyncHTTPClient()
    base_url = "https://api.forecast.io/forecast/APIKEY"
    city yield list_cities()
    for city in city:
        url = base_url + "/%s,%s" %(str(city.loc[0]), str(city.loc[1]))
        response = yield http_client.fetch(url)
        json = tornado.escape.json_decode(response.body)
        for day in json['daily']['data']:
            weather = Weather(city=city,
                              time = datetime.fromtimestamp(day['time']),
                              summary = day.get('summary'),
                              icon = day.get('icon'),
                              sunrise_time = datetime.fromtimestamp(day.get('sunriseTime')),
                              sunset_time = datetime.fromtimestamp(day.get('sunsetTime')),
                              moon_phase = day.get('moonPhase'),
                              precip_intensity = day.get('precipIntensity'),
                              precip_intensity_max = day.get('precipIntensityMax'),
                              precip_intensity_max_time = datetime.fromtimestamp(day.get('precipIntensityMaxTime')),
                              precip_probability = day.get('precipProbability'),
                              precip_type = day.get('precipType'),
                              temperature_min = day.get('temperatureMin'),
                              temperature_min_time = datetime.fromtimestamp(day.get('temperatureMinTime')),
                              temperature_max = day.get('temperatureMax'),
                              temperature_max_time = datetime.fromtimestamp(day.get('temperatureMaxTime')),
                              apparent_temperature_min = day.get('apparentTemperatureMin'),
                              apparent_temperature_min_time = datetime.fromtimestamp(day.get('apparentTemperatureMinTime')),
                              apparent_temperature_max = day.get('apparentTemperatureMax'),
                              apparent_temperature_max_time = datetime.fromtimestamp(day.get('apparentTemperatureMaxTime')),
                              dew_point = day.get('dewPoint'),
                              humidity = day.get('humidity'),
                              wind_speed = day.get('windSpeed'),
                              wind_bearing = day.get('windBearing'),
                              visibility = day.get('visibility'),
                              cloud_cover = day.get('cloudCover'),
                              pressure = day.get('pressure'),
                              ozone = day.get('ozone')
            )
            weather.create()


if __name__ == '__main__':
    io_loop = tornado.ioloop.IOLoop.instance()
    connect("DATABASE", host="localhost", port=27017, io_loop=io_loop)
    forecastio_api()
    io_loop.start()

这是使用Motornegine的天气类：

from tornado import gen
from motorengine import Document
from motorengine.fields import DateTimeField, DecimalField, ReferenceField, StringField

from src.db.city import City


class Weather(Document):
    __collection__ = 'weather'
    __lazy__ = False
    city = ReferenceField(reference_document_type=City)
    time = DateTimeField(required=True)
    summary = StringField()
    icon = StringField()
    sunrise_time = DateTimeField()
    sunset_time = DateTimeField()
    moon_phase = DecimalField(precision=2)
    precip_intensity = DecimalField(precision=4)
    precip_intensity_max = DecimalField(precision=4)
    precip_intensity_max_time = DateTimeField()
    precip_probability = DecimalField(precision=2)
    precip_type = StringField()
    temperature_min = DecimalField(precision=2)
    temperature_min_time = DateTimeField()
    temperature_max = DecimalField(precision=2)
    temperature_max_time = DateTimeField()
    apparent_temperature_min = DecimalField(precision=2)
    apparent_temperature_min_time = DateTimeField()
    apparent_temperature_max = DecimalField(precision=2)
    apparent_temperature_max_time = DateTimeField()
    dew_point = DecimalField(precision=2)
    humidity = DecimalField(precision=2)
    wind_speed = DecimalField(precision=2)
    wind_bearing = DecimalField(precision=2)
    visibility = DecimalField(precision=2)
    cloud_cover = DecimalField(precision=2)
    pressure = DecimalField(precision=2)
    ozone = DecimalField(precision=2)
    create_time = DateTimeField(auto_now_on_insert=True)

    @gen.coroutine
    def create(self):
        yield self.save()

Answer 1

你可以检查一下示意图。这个库帮助您定义可以轻松从dicts填充的对象(您可以轻松地将json转换为python )。它允许您对每个属性定义验证规则。当某些属性丢失或格式错误时，该对象将引发ModelValidationError错误。图表允许您在定义模型时添加默认值和更好的内容。