$minDistance - Amazon DocumentDB

本文属于机器翻译版本。若本译文内容与英语原文存在差异,则一律以英文原文为准。

$minDistance

$minDistance是与$nearSphere$geoNear结合使用的查找运算符,用于筛选距离中心点至少达到指定最小距离的文档。该运算符在 Amazon DocumentDB 中受支持,其功能与 MongoDB 中的对应运算符类似。

参数

  • $minDistance:从中心点到结果中包含文档的最小距离(以米为单位)。

示例(MongoDB 外壳)

在此示例中,我们将找到华盛顿州西雅图特定地点 2 公里半径范围内的所有餐厅。

创建示例文档

db.usarestaurants.insertMany([ { "state": "Washington", "city": "Seattle", "name": "Noodle House", "rating": 4.8, "location": { "type": "Point", "coordinates": [-122.3517, 47.6159] } }, { "state": "Washington", "city": "Seattle", "name": "Pike Place Grill", "rating": 4.5, "location": { "type": "Point", "coordinates": [-122.3412, 47.6102] } }, { "state": "Washington", "city": "Bellevue", "name": "The Burger Joint", "rating": 4.2, "location": { "type": "Point", "coordinates": [-122.2007, 47.6105] } } ]);

查询示例

db.usarestaurants.find({ "location": { "$nearSphere": { "$geometry": { "type": "Point", "coordinates": [-122.3516, 47.6156] }, "$minDistance": 1, "$maxDistance": 2000 } } }, { "name": 1 });

输出

{ "_id" : ObjectId("611f3da985009a81ad38e74b"), "name" : "Noodle House" } { "_id" : ObjectId("611f3da985009a81ad38e74c"), "name" : "Pike Place Grill" }

代码示例

要查看使用该$minDistance命令的代码示例,请选择要使用的语言的选项卡:

Node.js
const { MongoClient } = require('mongodb'); async function findRestaurantsNearby() { const client = await MongoClient.connect('mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false'); const db = client.db('test'); const collection = db.collection('usarestaurants'); const result = await collection.find({ "location": { "$nearSphere": { "$geometry": { "type": "Point", "coordinates": [-122.3516, 47.6156] }, "$minDistance": 1, "$maxDistance": 2000 } } }, { "projection": { "name": 1 } }).toArray(); console.log(result); client.close(); } findRestaurantsNearby();
Python
from pymongo import MongoClient def find_restaurants_nearby(): client = MongoClient('mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false') db = client.test collection = db.usarestaurants result = list(collection.find({ "location": { "$nearSphere": { "$geometry": { "type": "Point", "coordinates": [-122.3516, 47.6156] }, "$minDistance": 1, "$maxDistance": 2000 } } }, { "projection": {"name": 1} })) print(result) client.close() find_restaurants_nearby()