Performing case-statement in mongodb aggregation framework

what corresponds to the "case" SQL statement in the aggregation framework, is the $cond operator (see manual). $cond statements can be nested to simulate "when-then" and "else", but I have chosen another approach, because it is easier to read (and to generate, see below): I'll use the $concat operator to write the range string, which then serves as grouping key.

So for the given collection:

db.xx.find()
{ "_id" : ObjectId("514919fb23700b41723f94dc"), "name" : "A", "timespent" : 100 }
{ "_id" : ObjectId("514919fb23700b41723f94dd"), "name" : "B", "timespent" : 200 }
{ "_id" : ObjectId("514919fb23700b41723f94de"), "name" : "C", "timespent" : 300 }
{ "_id" : ObjectId("514919fb23700b41723f94df"), "name" : "D", "timespent" : 400 }
{ "_id" : ObjectId("514919fb23700b41723f94e0"), "name" : "E", "timespent" : 500 }

the aggregate (hardcoded) looks like this:

db.xx.aggregate([
  { $project: {
    "_id": 0,
    "range": {
      $concat: [{
        $cond: [ { $lte: ["$timespent", 250] }, "range 0-250", "" ]
      }, {
        $cond: [ { $and: [
          { $gte: ["$timespent", 251] }, 
          { $lt:  ["$timespent", 450] } 
        ] }, "range 251-450", "" ]
      }, {
        $cond: [ { $and: [
          { $gte: ["$timespent", 451] }, 
          { $lt:  ["$timespent", 650] } 
        ] }, "range 450-650", "" ]
      }]
    }
  }},
  { $group: { _id: "$range", count: { $sum: 1 } } },
  { $sort: { "_id": 1 } },
]);

and the result is:

{
    "result" : [
        {
            "_id" : "range 0-250",
            "count" : 2
        },
        {
            "_id" : "range 251-450",
            "count" : 2
        },
        {
            "_id" : "range 450-650",
            "count" : 1
        }
    ],
    "ok" : 1
}

In order to generate the aggregate command, you have to build the "range" projection as a JSON object ( or you could generate a string and then use JSON.parse(string) )

The generator looks like this:

var ranges = [ 0, 250, 450, 650 ];
var rangeProj = {
  "$concat": []
};

for (i = 1; i < ranges.length; i++) {
  rangeProj.$concat.push({
    $cond: {
      if: {
        $and: [{
          $gte: [ "$timespent", ranges[i-1] ]
        }, {
          $lt: [ "$timespent", ranges[i] ]
        }]
      },
      then: "range " + ranges[i-1] + "-" + ranges[i],
      else: ""
    }
  })
}

db.xx.aggregate([{
  $project: { "_id": 0, "range": rangeProj }
}, {
  $group: { _id: "$range", count: { $sum: 1 } }
}, {
  $sort: { "_id": 1 }
}]);

which will return the same result as above.

Starting from MongoDB 3.4 we can use the $switch operator to perform a multi-switch statement in the $project stage.

The $group pipeline operator group the documents by "range" and return the "count" for each group using the $sum accumulator operator.

db.collection.aggregate(
    [  
        { "$project": { 
            "range": { 
                "$switch": { 
                    "branches": [ 
                        { 
                            "case": { "$lte": [ "$timespent", 250 ] }, 
                            "then": "0-250" 
                        }, 
                        { 
                            "case": { 
                                "$and": [ 
                                    { "$gt": [ "$timespent", 250 ] }, 
                                    { "$lte": [ "$timespent", 450 ] } 
                                ] 
                            }, 
                            "then": "251-450" 
                        }, 
                        { 
                            "case": { 
                                "$and": [ 
                                    { "$gt": [ "$timespent", 450 ] }, 
                                    { "$lte": [ "$timespent", 650 ] } 
                                ] 
                            }, 
                            "then": "451-650" 
                        } 
                    ], 
                    "default": "650+" 
                } 
            } 
        }}, 
        { "$group": { 
            "_id": "$range", 
            "count": { "$sum": 1 } 
        }}
    ]
)

With the following documents in our collection,

{ "_id" : ObjectId("514919fb23700b41723f94dc"), "name" : "A", "timespent" : 100 },
{ "_id" : ObjectId("514919fb23700b41723f94dd"), "name" : "B", "timespent" : 200 },
{ "_id" : ObjectId("514919fb23700b41723f94de"), "name" : "C", "timespent" : 300 },
{ "_id" : ObjectId("514919fb23700b41723f94df"), "name" : "D", "timespent" : 400 },
{ "_id" : ObjectId("514919fb23700b41723f94e0"), "name" : "E", "timespent" : 500 }

our query yields:

{ "_id" : "451-650", "count" : 1 }
{ "_id" : "251-450", "count" : 2 }
{ "_id" : "0-250", "count" : 2 }

We may want to add a $sort stage to the pipeline sort our document by range but this will only sort the documents in lexicographic order because of the type of "range".

Performing case-statement in mongodb aggregation framework

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