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

New from version 8.0

Not supported by Elastic cluster.

The `$bucket` aggregation stage in Amazon DocumentDB allows you to group input documents into buckets based on a specified expression and bucket boundaries. This can be useful for analyzing data that falls within certain value ranges or categories.

**Parameters**
+ `groupBy` (required): The expression that specifies the value to group by.
+ `boundaries` (required): An array of double values that define the bucket boundaries. Documents are assigned to buckets based on the `groupBy` expression value falling within the specified boundaries.
+ `default` (optional): A literal value that is output for documents whose `groupBy` expression value does not fall into any of the specified boundaries.
+ `output` (optional): An object that specifies the information to output for each bucket. You can use accumulator operators like `$sum`, `$avg`, `$min`, and `$max` to compute aggregations for each bucket.

## Example (MongoDB Shell)
<a name="bucket-examples"></a>

The following example demonstrates how to use the `$bucket` stage to group sales data by price range.

**Create sample documents**

```
db.sales.insertMany([
  { item: "abc", price: 10, quantity: 2, date: new Date("2020-09-01") },
  { item: "def", price: 20, quantity: 1, date: new Date("2020-10-01") },
  { item: "ghi", price: 5, quantity: 3, date: new Date("2020-11-01") },
  { item: "jkl", price: 15, quantity: 2, date: new Date("2020-12-01") },
  { item: "mno", price: 25, quantity: 1, date: new Date("2021-01-01") }
]);
```

**Query example**

```
db.sales.aggregate([
  {
    $bucket: {
      groupBy: "$price",
      boundaries: [0, 10, 20, 30],
      default: "Other",
      output: {
        "count": { $sum: 1 },
        "totalQuantity": { $sum: "$quantity" }
      }
    }
  },
  {
    $sort: { _id: 1 }
  }
])
```

**Output**

```
[
  { _id: 0, count: 1, totalQuantity: 3 },
  { _id: 10, count: 2, totalQuantity: 4 },
  { _id: 20, count: 2, totalQuantity: 2 }
]
```

## Code examples
<a name="bucket-code"></a>

To view a code example for using the `$bucket` command, choose the tab for the language that you want to use:

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

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

async function example() {
  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 sales = db.collection('sales');

  const result = await sales.aggregate([
    {
      $bucket: {
        groupBy: "$price",
        boundaries: [0, 10, 20, 30],
        default: "Other",
        output: {
          "count": { $sum: 1 },
          "totalQuantity": { $sum: "$quantity" }
        }
      }
    },
    {
      $sort: { _id: 1 }
    }
  ]).toArray();

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

example();
```

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

```
from pymongo import MongoClient

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

    result = list(sales.aggregate([
        {
            '$bucket': {
                'groupBy': '$price',
                'boundaries': [0, 10, 20, 30],
                'default': 'Other',
                'output': {
                    'count': {'$sum': 1},
                    'totalQuantity': {'$sum': '$quantity'}
                }
            }
        },
        {
            "$sort": { "_id": 1 }
        }
    ]))

    print(result)
    client.close()

example()
```

------