group¶

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Definition
Behavior
Examples

Definition¶

group¶

Groups documents in a collection by the specified key and performs simple aggregation functions, such as computing counts and sums. The command is analogous to a SELECT <...> GROUP BY statement in SQL. The command returns a document with the grouped records as well as the command meta-data.

The group command takes the following prototype form:

{
  group:
   {
     ns: <namespace>,
     key: <key>,
     $reduce: <reduce function>,
     $keyf: <key function>,
     cond: <query>,
     finalize: <finalize function>
   }
}

The command accepts a document with the following fields:

Field	Type	Description
`ns`	string	The collection from which to perform the group by operation.
`key`	document	The field or fields to group. Returns a “key object” for use as the grouping key.
`$reduce`	function	An aggregation function that operates on the documents during the grouping operation. These functions may return a sum or a count. The function takes two arguments: the current document and an aggregation result document for that group.
`initial`	document	Initializes the aggregation result document.
`$keyf`	function	Optional. Alternative to the `key` field. Specifies a function that creates a “key object” for use as the grouping key. Use `$keyf` instead of `key` to group by calculated fields rather than existing document fields.
`cond`	document	Optional. The selection criteria to determine which documents in the collection to process. If you omit the `cond` field, `group` processes all the documents in the collection for the group operation.
`finalize`	function	Optional. A function that runs each item in the result set before `group` returns the final value. This function can either modify the result document or replace the result document as a whole. Unlike the `$keyf` and `$reduce` fields that also specify a function, this field name is `finalize`, not `$finalize`.

For the shell, MongoDB provides a wrapper method db.collection.group(). However, the db.collection.group() method takes the keyf field and the reduce field whereas the group command takes the $keyf field and the $reduce field.

Behavior¶

Limits and Restrictions¶

The group command does not work with sharded clusters. Use the aggregation framework or map-reduce in sharded environments.

The result set must fit within the maximum BSON document size.

Additionally, in version 2.2, the returned array can contain at most 20,000 elements; i.e. at most 20,000 unique groupings. For group by operations that results in more than 20,000 unique groupings, use mapReduce. Previous versions had a limit of 10,000 elements.

Prior to 2.4, the group command took the mongod instance’s JavaScript lock which blocked all other JavaScript execution.

`mongo` Shell JavaScript Functions/Properties¶

Changed in version 2.4.

In MongoDB 2.4, map-reduce operations, the group command, and $where operator expressions cannot access certain global functions or properties, such as db, that are available in the mongo shell.

When upgrading to MongoDB 2.4, you will need to refactor your code if your map-reduce operations, group commands, or $where operator expressions include any global shell functions or properties that are no longer available, such as db.

The following JavaScript functions and properties are available to map-reduce operations, the group command, and $where operator expressions in MongoDB 2.4:

Available Properties	Available Functions
`args` `MaxKey` `MinKey`	`assert()` `BinData()` `DBPointer()` `DBRef()` `doassert()` `emit()` `gc()` `HexData()` `hex_md5()` `isNumber()` `isObject()` `ISODate()` `isString()`	`Map()` `MD5()` `NumberInt()` `NumberLong()` `ObjectId()` `print()` `printjson()` `printjsononeline()` `sleep()` `Timestamp()` `tojson()` `tojsononeline()` `tojsonObject()` `UUID()` `version()`

JavaScript in MongoDB

Although group uses JavaScript, most interactions with MongoDB do not use JavaScript but use an idiomatic driver in the language of the interacting application.

Examples¶

The following are examples of the db.collection.group() method. The examples assume an orders collection with documents of the following prototype:

{
  _id: ObjectId("5085a95c8fada716c89d0021"),
  ord_dt: ISODate("2012-07-01T04:00:00Z"),
  ship_dt: ISODate("2012-07-02T04:00:00Z"),
  item:
    {
      sku: "abc123",
      price: 1.99,
      uom: "pcs",
      qty: 25
    }
}

Group by Two Fields¶

The following example groups by the ord_dt and item.sku fields those documents that have ord_dt greater than 01/01/2012:

db.runCommand(
   {
     group:
       {
         ns: 'orders',
         key: { ord_dt: 1, 'item.sku': 1 },
         cond: { ord_dt: { $gt: new Date( '01/01/2012' ) } },
         $reduce: function ( curr, result ) { },
         initial: { }
       }
   }
)

The result is a document that contain the retval field which contains the group by records, the count field which contains the total number of documents grouped, the keys field which contains the number of unique groupings (i.e. number of elements in the retval), and the ok field which contains the command status:

{ "retval" :
      [ { "ord_dt" : ISODate("2012-07-01T04:00:00Z"), "item.sku" : "abc123"},
        { "ord_dt" : ISODate("2012-07-01T04:00:00Z"), "item.sku" : "abc456"},
        { "ord_dt" : ISODate("2012-07-01T04:00:00Z"), "item.sku" : "bcd123"},
        { "ord_dt" : ISODate("2012-07-01T04:00:00Z"), "item.sku" : "efg456"},
        { "ord_dt" : ISODate("2012-06-01T04:00:00Z"), "item.sku" : "abc123"},
        { "ord_dt" : ISODate("2012-06-01T04:00:00Z"), "item.sku" : "efg456"},
        { "ord_dt" : ISODate("2012-06-01T04:00:00Z"), "item.sku" : "ijk123"},
        { "ord_dt" : ISODate("2012-05-01T04:00:00Z"), "item.sku" : "abc123"},
        { "ord_dt" : ISODate("2012-05-01T04:00:00Z"), "item.sku" : "abc456"},
        { "ord_dt" : ISODate("2012-06-08T04:00:00Z"), "item.sku" : "abc123"},
        { "ord_dt" : ISODate("2012-06-08T04:00:00Z"), "item.sku" : "abc456"}
      ],
  "count" : 13,
  "keys" : 11,
  "ok" : 1 }

The method call is analogous to the SQL statement:

SELECT ord_dt, item_sku
FROM orders
WHERE ord_dt > '01/01/2012'
GROUP BY ord_dt, item_sku

Calculate the Sum¶

The following example groups by the ord_dt and item.sku fields those documents that have ord_dt greater than 01/01/2012 and calculates the sum of the qty field for each grouping:

db.runCommand(
   { group:
       {
         ns: 'orders',
         key: { ord_dt: 1, 'item.sku': 1 },
         cond: { ord_dt: { $gt: new Date( '01/01/2012' ) } },
         $reduce: function ( curr, result ) {
                     result.total += curr.item.qty;
                  },
         initial: { total : 0 }
       }
    }
)

The retval field of the returned document is an array of documents that contain the group by fields and the calculated aggregation field:

{ "retval" :
      [ { "ord_dt" : ISODate("2012-07-01T04:00:00Z"), "item.sku" : "abc123", "total" : 25 },
        { "ord_dt" : ISODate("2012-07-01T04:00:00Z"), "item.sku" : "abc456", "total" : 25 },
        { "ord_dt" : ISODate("2012-07-01T04:00:00Z"), "item.sku" : "bcd123", "total" : 10 },
        { "ord_dt" : ISODate("2012-07-01T04:00:00Z"), "item.sku" : "efg456", "total" : 10 },
        { "ord_dt" : ISODate("2012-06-01T04:00:00Z"), "item.sku" : "abc123", "total" : 25 },
        { "ord_dt" : ISODate("2012-06-01T04:00:00Z"), "item.sku" : "efg456", "total" : 15 },
        { "ord_dt" : ISODate("2012-06-01T04:00:00Z"), "item.sku" : "ijk123", "total" : 20 },
        { "ord_dt" : ISODate("2012-05-01T04:00:00Z"), "item.sku" : "abc123", "total" : 45 },
        { "ord_dt" : ISODate("2012-05-01T04:00:00Z"), "item.sku" : "abc456", "total" : 25 },
        { "ord_dt" : ISODate("2012-06-08T04:00:00Z"), "item.sku" : "abc123", "total" : 25 },
        { "ord_dt" : ISODate("2012-06-08T04:00:00Z"), "item.sku" : "abc456", "total" : 25 }
      ],
  "count" : 13,
  "keys" : 11,
  "ok" : 1 }