db.collection.group()¶

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Definition¶

db.collection.group({ key, reduce, initial [, keyf] [, cond] [, finalize] })¶

Deprecated since version 3.4: Mongodb 3.4 deprecates the db.collection.group() method. Use db.collection.aggregate() with the $group stage or db.collection.mapReduce() instead.

Note

Because db.collection.group() uses JavaScript, it is subject to a number of performance limitations. For most cases the $group operator in the aggregation pipeline provides a suitable alternative with fewer restrictions.

Groups documents in a collection by the specified keys and performs simple aggregation functions such as computing counts and sums. The method is analogous to a SELECT <...> GROUP BY statement in SQL. The group() method returns an array.

The db.collection.group() accepts a single document that contains the following:

Field	Type	Description
`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	The selection criteria to determine which documents in the collection to process. If you omit the `cond` field, `db.collection.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 `db.collection.group()` returns the final value. This function can either modify the result document or replace the result document as a whole.
`collation`	document	Optional. Specifies the collation to use for the operation. Collation allows users to specify language-specific rules for string comparison, such as rules for lettercase and accent marks. The collation option has the following syntax: collation: { locale: <string>, caseLevel: <boolean>, caseFirst: <string>, strength: <int>, numericOrdering: <boolean>, alternate: <string>, maxVariable: <string>, backwards: <boolean> } When specifying collation, the `locale` field is mandatory; all other collation fields are optional. For descriptions of the fields, see Collation Document. If the collation is unspecified but the collection has a default collation (see `db.createCollection()`), the operation uses the collation specified for the collection. If no collation is specified for the collection or for the operations, MongoDB uses the simple binary comparison used in prior versions for string comparisons. You cannot specify multiple collations for an operation. For example, you cannot specify different collations per field, or if performing a find with a sort, you cannot use one collation for the find and another for the sort. New in version 3.4.

The db.collection.group() method is a shell wrapper for the group command. 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 db.collection.group() method 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.

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 db.collection.group() method 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()`

Examples¶

The following 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 01/01/2011:

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

The result is an array of documents that contain the group by fields:

[
  { "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"}
]

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 01/01/2011 and calculates the sum of the qty field for each grouping:

db.orders.group(
   {
     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 result is an array of documents that contain the group by fields and the calculated aggregation field:

[ { "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 } ]