本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

# \$1near
<a name="near"></a>

Amazon DocumentDB 中的`$near`運算子用於尋找地理位置接近指定點的文件。它會傳回依距離排序的文件，以最接近的文件為先。此運算子需要 2dsphere 地理空間索引，對於位置資料的鄰近查詢非常有用。

**參數**
+ `$geometry`：GeoJSON 點物件，定義近查詢的中心點。
+ `$maxDistance`：（選用） 從指定點開始，文件可以符合查詢的最大距離，以公尺為單位。
+ `$minDistance`：（選用） 文件可以符合查詢之指定點的最小距離，以公尺為單位。

**索引需求**
+ `2dsphere index`：GeoJSON Point 資料上的地理空間查詢需要。

## 範例 (MongoDB Shell)
<a name="near-examples"></a>

下列範例示範如何使用 `$near`運算子尋找距離華盛頓州西雅圖特定位置最近的餐廳。

**建立範例文件**

```
db.usarestaurants.insert([
  {
    "name": "Noodle House",
    "city": "Seattle",
    "state": "Washington",
    "rating": 4.8,
    "location": { "type": "Point", "coordinates": [-122.3517, 47.6159] }
  },
  {
    "name": "Pike Place Grill",
    "city": "Seattle",
    "state": "Washington",
    "rating": 4.2,
    "location": { "type": "Point", "coordinates": [-122.3403, 47.6062] }
  },
  {
    "name": "Lola",
    "city": "Seattle",
    "state": "Washington",
    "rating": 4.5,
    "location": { "type": "Point", "coordinates": [-122.3407, 47.6107] }
  }
]);
```

**建立 2dsphere 索引**

```
db.usarestaurants.createIndex({ "location": "2dsphere" });
```

**使用 GeoJSON Point 查詢範例**

```
db.usarestaurants.find({
  location: {
    $near: {
      $geometry: {
        type: "Point",
        coordinates: [-122.3516, 47.6156]
      },
      $maxDistance: 100,
      $minDistance: 10
    }
  }
});
```

**輸出**

```
{
  "_id" : ObjectId("69031ec9ea1c2922a1ce5f4a"),
  "name" : "Noodle House",
  "city" : "Seattle",
  "state" : "Washington",
  "rating" : 4.8,
  "location" : {
    "type" : "Point",
    "coordinates" : [ -122.3517, 47.6159 ]
  }
}
```

## 程式碼範例
<a name="near-code"></a>

若要檢視使用 `$near`命令的程式碼範例，請選擇您要使用的語言標籤：

------
#### [ Node.js ]

```
const { MongoClient } = require('mongodb');

async function findNearbyRestaurants() {
  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 restaurants = db.collection('usarestaurants');

  // Create 2dsphere index
  await restaurants.createIndex({ "location": "2dsphere" });

  const result = await restaurants.find({
    location: {
      $near: {
        $geometry: {
          type: "Point",
          coordinates: [-122.3516, 47.6156]
        },
        $maxDistance: 100,
        $minDistance: 10
      }
    }
  }).toArray();

  console.log(result);

  client.close();
}

findNearbyRestaurants();
```

------
#### [ Python ]

```
from pymongo import MongoClient

def find_nearby_restaurants():
    client = MongoClient('mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false')
    db = client['test']
    restaurants = db['usarestaurants']

    # Create 2dsphere index
    restaurants.create_index([("location", "2dsphere")])

    result = list(restaurants.find({
        'location': {
            '$near': {
                '$geometry': {
                    'type': 'Point',
                    'coordinates': [-122.3516, 47.6156]
                },
                '$maxDistance': 100,
                '$minDistance': 10
            }
        }
    }))

    print(result)

    client.close()

find_nearby_restaurants()
```

------