OPTIONS

db.collection.group()

Definition

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

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 Optional. 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.

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 } ]

The method call is analogous to the SQL statement:

SELECT ord_dt, item_sku, SUM(item_qty) as total
FROM orders
WHERE ord_dt > '01/01/2012'
GROUP BY ord_dt, item_sku

Calculate Sum, Count, and Average

The following example groups by the calculated day_of_week field, those documents that have ord_dt greater than 01/01/2011 and calculates the sum, count, and average of the qty field for each grouping:

db.orders.group(
   {
     keyf: function(doc) {
               return { day_of_week: doc.ord_dt.getDay() };
           },
     cond: { ord_dt: { $gt: new Date( '01/01/2012' ) } },
    reduce: function( curr, result ) {
                result.total += curr.item.qty;
                result.count++;
            },
    initial: { total : 0, count: 0 },
    finalize: function(result) {
                  var weekdays = [
                       "Sunday", "Monday", "Tuesday",
                       "Wednesday", "Thursday",
                       "Friday", "Saturday"
                      ];
                  result.day_of_week = weekdays[result.day_of_week];
                  result.avg = Math.round(result.total / result.count);
              }
   }
)

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

[
  { "day_of_week" : "Sunday", "total" : 70, "count" : 4, "avg" : 18 },
  { "day_of_week" : "Friday", "total" : 110, "count" : 6, "avg" : 18 },
  { "day_of_week" : "Tuesday", "total" : 70, "count" : 3, "avg" : 23 }
]