[mongodb] how to convert string to numerical values in mongodb

Using MongoDB 4.0 and newer

You have two options i.e. $toInt or $convert. Using $toInt, follow the example below:

filterDateStage = {
    '$match': {
        'Date': {
            '$gt': '2015-04-01', 
            '$lt': '2015-04-05'
        }
    }
};

groupStage = {
    '$group': {
        '_id': '$PartnerID',
        'total': { '$sum': { '$toInt': '$moop' } }
    }
};

db.getCollection('my_collection').aggregate([
   filterDateStage,
   groupStage
])

If the conversion operation encounters an error, the aggregation operation stops and throws an error. To override this behavior, use $convert instead.

Using $convert

groupStage = {
    '$group': {
        '_id': '$PartnerID',
        'total': { 
            '$sum': { 
                '$convert': { 'input': '$moop', 'to': 'int' }
            } 
        }
    }
};

Using Map/Reduce

With map/reduce you can use javascript functions like parseInt() to do the conversion. As an example, you could define the map function to process each input document: In the function, this refers to the document that the map-reduce operation is processing. The function maps the converted moop string value to the PartnerID for each document and emits the PartnerID and converted moop pair. This is where the javascript native function parseInt() can be applied:

var mapper = function () {
    var x = parseInt(this.moop);
    emit(this.PartnerID, x);
};

Next, define the corresponding reduce function with two arguments keyCustId and valuesMoop. valuesMoop is an array whose elements are the integer moop values emitted by the map function and grouped by keyPartnerID. The function reduces the valuesMoop array to the sum of its elements.

var reducer = function(keyPartnerID, valuesMoop) {
                  return Array.sum(valuesMoop);
              };

db.collection.mapReduce(
    mapper,
    reducer,
    {
        out : "example_results",
        query: { 
            Date: {
                $gt: "2015-04-01", 
                $lt: "2015-04-05"
            }
        }       
    }
 );

 db.example_results.find(function (err, docs) {
    if(err) console.log(err);
    console.log(JSON.stringify(docs));
 });

For example, with the following sample collection of documents:

/* 0 */
{
    "_id" : ObjectId("550c00f81bcc15211016699b"),
    "Date" : "2015-04-04",
    "PartnerID" : "123456",
    "moop" : "1234"
}

/* 1 */
{
    "_id" : ObjectId("550c00f81bcc15211016699c"),
    "Date" : "2015-04-03",
    "PartnerID" : "123456",
    "moop" : "24"
}

/* 2 */
{
    "_id" : ObjectId("550c00f81bcc15211016699d"),
    "Date" : "2015-04-02",
    "PartnerID" : "123457",
    "moop" : "21"
}

/* 3 */
{
    "_id" : ObjectId("550c00f81bcc15211016699e"),
    "Date" : "2015-04-02",
    "PartnerID" : "123457",
    "moop" : "8"
}

The above Map/Reduce operation will save the results to the example_results collection and the shell command db.example_results.find() will give:

/* 0 */
{
    "_id" : "123456",
    "value" : 1258
}

/* 1 */
{
    "_id" : "123457",
    "value" : 29
}