Aggregation with User Preference Data

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Data Model

Normalize and Sort Documents

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Data Model

Consider a hypothetical sports club with a database that contains a users collection that tracks the user's join dates, sport preferences, and stores these data in documents that resemble the following:

{
  _id : "jane",
  joined : ISODate("2011-03-02"),
  likes : ["golf", "racquetball"]
}
{
  _id : "joe",
  joined : ISODate("2012-07-02"),
  likes : ["tennis", "golf", "swimming"]
}

Normalize and Sort Documents

The following operation returns user names in upper case and in alphabetical order. The aggregation includes user names for all documents in the users collection. You might do this to normalize user names for processing.

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

All documents from the users collection pass through the pipeline, which consists of the following operations:

The $project operator:
- creates a new field called name.
- converts the value of the _id to upper case, with the $toUpper operator. Then the $project creates a new field, named name to hold this value.
- suppresses the id field. $project will pass the _id field by default, unless explicitly suppressed.
The $sort operator orders the results by the name field.

The results of the aggregation would resemble the following:

{
  "name" : "JANE"
},
{
  "name" : "JILL"
},
{
  "name" : "JOE"
}

Return Usernames Ordered by Join Month

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Data Model
Normalize and Sort Documents

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"
}

Return Total Number of Joins per Month

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Data Model
Normalize and Sort Documents

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
}

Return the Five Most Common "Likes"

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Data Model
Normalize and Sort Documents

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
}

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Example with ZIP Code Data

Map-Reduce