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。

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

Amazon DocumentDB `$sample` 中的彙總階段用於從集合中隨機選取指定數量的文件。這適用於資料分析、測試和產生範例等任務，以供進一步處理。

**參數**
+ `size`：要隨機選取的文件數量。

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

下列範例示範如何使用 `$sample`階段從`temp`集合中隨機選取兩個文件。

**建立範例文件**

```
db.temp.insertMany([
  { "_id": 1, "temperature": 97.1, "humidity": 0.60, "timestamp": ISODate("2019-03-21T21:17:22.425Z") },
  { "_id": 2, "temperature": 98.2, "humidity": 0.59, "timestamp": ISODate("2019-03-21T21:17:22.425Z") },
  { "_id": 3, "temperature": 96.8, "humidity": 0.61, "timestamp": ISODate("2019-03-21T21:17:22.425Z") },
  { "_id": 4, "temperature": 97.9, "humidity": 0.61, "timestamp": ISODate("2019-03-21T21:17:22.425Z") },
  { "_id": 5, "temperature": 97.5, "humidity": 0.60, "timestamp": ISODate("2019-03-21T21:17:22.425Z") },
  { "_id": 6, "temperature": 98.0, "humidity": 0.59, "timestamp": ISODate("2019-03-21T21:17:22.425Z") },
  { "_id": 7, "temperature": 97.2, "humidity": 0.60, "timestamp": ISODate("2019-03-21T21:17:22.425Z") },
  { "_id": 8, "temperature": 98.1, "humidity": 0.59, "timestamp": ISODate("2019-03-21T21:17:22.425Z") },
  { "_id": 9, "temperature": 96.9, "humidity": 0.62, "timestamp": ISODate("2019-03-21T21:17:22.425Z") },
  { "_id": 10, "temperature": 97.7, "humidity": 0.60, "timestamp": ISODate("2019-03-21T21:17:22.425Z") }
]);
```

**查詢範例**

```
db.temp.aggregate([
   { $sample: { size: 2 } }
])
```

**輸出**

```
{ "_id" : 4, "temperature" : 97.9, "humidity" : 0.61, "timestamp" : ISODate("2019-03-21T21:17:22.425Z") }
{ "_id" : 9, "temperature" : 96.9, "humidity" : 0.62, "timestamp" : ISODate("2019-03-21T21:17:22.425Z") }
```

結果顯示，10 份文件中有 2 份已隨機取樣。您現在可以使用這些文件來判斷平均值或執行最小/最大計算。

## 程式碼範例
<a name="sample-code"></a>

若要檢視使用 `$sample`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 sampleDocuments() {
  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('temp');

  const result = await collection.aggregate([
    { $sample: { size: 2 } }
  ]).toArray();

  console.log(result);
  await client.close();
}

sampleDocuments();
```

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

```
from pymongo import MongoClient

def sample_documents():
    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['temp']

    result = list(collection.aggregate([
        { '$sample': { 'size': 2 } }
    ]))

    print(result)
    client.close()

sample_documents()
```

------