The following aggregation operation returns user names sorted by the month they joined. This kind of aggregation could help generate membership renewal notices.

db.users.aggregate(
  [
    { $project :
       {
         month_joined : { $month : "$joined" },
         name : "$_id",
         _id : 0
       }
    },
    { $sort : { month_joined : 1 } }
  ]
)

The pipeline passes all documents in the users collection through the following operations:

• The $project operator:

  • Creates two new fields: month_joined and name.

  • Suppresses the id from the results. The aggregate() method includes the _id, unless explicitly suppressed.

• The $month operator converts the values of the joined field to integer representations of the month. Then the $project operator assigns those values to the month_joined field.

• The $sort operator sorts the results by the month_joined field.

The operation returns results that resemble the following:

{
  "month_joined" : 1,
  "name" : "ruth"
},
{
  "month_joined" : 1,
  "name" : "harold"
},
{
  "month_joined" : 1,
  "name" : "kate"
}
{
  "month_joined" : 2,
  "name" : "jill"
}

The following operation shows how many people joined each month of the year. You might use this aggregated data for recruiting and marketing strategies.

db.users.aggregate(
  [
    { $project : { month_joined : { $month : "$joined" } } } ,
    { $group : { _id : {month_joined:"$month_joined"} , number : { $sum : 1 } } },
    { $sort : { "_id.month_joined" : 1 } }
  ]
)

The pipeline passes all documents in the users collection through the following operations:

• The $project operator creates a new field called month_joined.

• The $month operator converts the values of the joined field to integer representations of the month. Then the $project operator assigns the values to the month_joined field.

• The $group operator collects all documents with a given month_joined value and counts how many documents there are for that value. Specifically, for each unique value, $group creates a new "per-month" document with two fields:

  • _id, which contains a nested document with the month_joined field and its value.

  • number, which is a generated field. The $sum operator increments this field by 1 for every document containing the given month_joined value.

• The $sort operator sorts the documents created by $group according to the contents of the month_joined field.

The result of this aggregation operation would resemble the following:

{
  "_id" : {
    "month_joined" : 1
  },
  "number" : 3
},
{
  "_id" : {
    "month_joined" : 2
  },
  "number" : 9
},
{
  "_id" : {
    "month_joined" : 3
  },
  "number" : 5
}

The following aggregation collects top five most "liked" activities in the data set. This type of analysis could help inform planning and future development.

db.users.aggregate(
  [
    { $unwind : "$likes" },
    { $group : { _id : "$likes" , number : { $sum : 1 } } },
    { $sort : { number : -1 } },
    { $limit : 5 }
  ]
)

The pipeline begins with all documents in the users collection, and passes these documents through the following operations:

• The $unwind operator separates each value in the likes array, and creates a new version of the source document for every element in the array.

  Example

  Given the following document from the users collection:

  {
    _id : "jane",
    joined : ISODate("2011-03-02"),
    likes : ["golf", "racquetball"]
  }

  The $unwind operator would create the following documents:

  {
    _id : "jane",
    joined : ISODate("2011-03-02"),
    likes : "golf"
  }
  {
    _id : "jane",
    joined : ISODate("2011-03-02"),
    likes : "racquetball"
  }

• The $group operator collects all documents with the same value for the likes field and counts each grouping. With this information, $group creates a new document with two fields:

  • _id, which contains the likes value.

  • number, which is a generated field. The $sum operator increments this field by 1 for every document containing the given likes value.

• The $sort operator sorts these documents by the number field in reverse order.

• The $limit operator only includes the first 5 result documents.

The results of aggregation would resemble the following:

{
  "_id" : "golf",
  "number" : 33
},
{
  "_id" : "racquetball",
  "number" : 31
},
{
  "_id" : "swimming",
  "number" : 24
},
{
  "_id" : "handball",
  "number" : 19
},
{
  "_id" : "tennis",
  "number" : 18
}