Utilizing aggregation techniques or Map Reduce processes to generate standardized 'Distinct Paying Customers for each Vendor' data

Question

Utilizing aggregation techniques or Map Reduce processes to generate standardized 'Distinct Paying Customers for each Vendor' data

I am currently working on generating a report for the number of Unique Paying Users Per Vendor using either Map Reduce or the Aggregation Framework in MongoDB. The challenge lies in normalizing the totals so that each user contributes a total of 1 across all vendors they have purchased from. For instance,

{
   "account": "abc",
   "vendor": "amazon",
},
{
   "account": "abc",
   "vendor": "overstock",
},
{
   "account": "ccc",
   "vendor": "overstock",
}

would result in

{
   "vendor": "amazon",
   "total" : 0.5
},
{ 
   "vendor": "overstock",
   "total": 1.5
}

In this scenario, the user 'abc' made two purchases and contributed equally to both vendors. Additionally, the sum of vendor totals equals the number of unique paying users.

Initially, I approached this aggregation process with four steps:

1. Start by storing the number of purchases per vendor for each user.
2. Calculate the total purchases for each user and distribute these among the respective vendors.
3. Merge the normalized purchase data for each user into a final vendor map through addition.

While effective with smaller datasets, this method proves to be slow and memory-intensive when dealing with larger sets.

Utilizing the Aggregation framework, I managed to calculate the total users but struggled with normalizing them effectively.

agg = this.db.aggregate(
[
    {
        $group :
        {
            _id :
            {
                vendor : '$vendor',
                user : '$account'
            },
            total :
            {
                $sum : 1
            }
        }
    }
]);

var transformed = {};
for( var index in agg.result)
{
    var entry = agg.result[index];

    var vendor= entry._id.vendor;
    if(!transformed[vendor])
    {
        transformed[vendor] = 0;
    }
    transformed[vendor] += 1;
}

I'm seeking guidance on restructuring this query to properly normalize the users' totals.

javascript mongodb mapreduce aggregation-framework

Answer 1

Answer №1

When it comes to analyzing data using MongoDB's .aggregate() or .mapReduce() methods, there are multiple approaches to consider. The efficiency of these approaches will depend on the size of your dataset.

If you choose to use the aggregate method, you will need to calculate totals per "vendor" and overall totals per user to determine percentages. This involves grouping operations and possibly creating arrays using the $unwind operator:

db.collection.aggregate([
    { "$group": {
        "_id": { "account": "$account", "vendor": "$vendor" },
        "count": { "$sum": 1 }
    }},
    { "$group": {
        "_id": "$_id.account",
        "purch": { "$push": { "vendor": "$_id.vendor", "count": "$count" } },
        "total": { "$sum": "$count" },
    }},
    { "$unwind": "$purch" },
    { "$project": {
        "vendor": "$purch.vendor",
        "total": { 
            "$divide": [ "$purch.count", "$total" ]
        }
    }},
    { "$group": {
        "_id": "$vendor",
        "total": { "$sum": "$total" }
    }}
])

Alternatively, the mapReduce approach involves two steps: first reducing responses by vendor for each user, then further reducing them down to vendor totals:

db.collection.mapReduce(
    function () {
        emit(
            this.account,
            {
                "data": [{
                    "vendor": this.vendor,
                    "count": 1,
                }],
                "total": 1,
                "seen": false
            }
        );
    },
    function (key,values) {

        var reduced = { data: [], total: 0, seen: true };

        values.forEach(function(value) {
            value.data.forEach(function(data) {
                var index = -1;
                for (var i = 0; i <=reduced.data.length-1; i++) {

                    if ( reduced.data[i].vendor == data.vendor ) {
                        index = i;
                        break;
                    }
                }

                if ( index == -1 ) {
                    reduced.data.push(data);
                } else {
                    if (!value.seen)
                        reduced.data[index].count += data.count;
                }
            });
        });

        reduced.data.map(function(x) {
            reduced.total += x.count;
        });

        return reduced;
    },
    { 
        "out": { "replace": "output" },
        "finalize": function (key,value) {

            var result = {
                data: []
            };

            result.data = value.data.map(function(x) {
                var res = { };
                res["vendor"] = x.vendor;
                res["total"] = x.count / value.total;
                return res;
            });

            return result;
        }
    }
)

The second part involves outputting the results:

db.output.mapReduce(
    function () {
        this.value.data.forEach(function(data){
            emit( data.vendor, data.total );
        });
    },
    function(key,values) {
        return Array.sum( values );
    },
    { "out": { "inline": 1 } }
)

Considering the size of your data, the mapReduce approach may be slower as it requires additional processing and output to a collection. On the other hand, the aggregation framework approach typically runs faster, but could face slowdowns depending on the size of the vendor array per user.

Answer 2

When it comes to analyzing data using MongoDB's .aggregate() or .mapReduce() methods, there are multiple approaches to consider. The efficiency of these approaches will depend on the size of your dataset.

If you choose to use the aggregate method, you will need to calculate totals per "vendor" and overall totals per user to determine percentages. This involves grouping operations and possibly creating arrays using the $unwind operator:

db.collection.aggregate([
    { "$group": {
        "_id": { "account": "$account", "vendor": "$vendor" },
        "count": { "$sum": 1 }
    }},
    { "$group": {
        "_id": "$_id.account",
        "purch": { "$push": { "vendor": "$_id.vendor", "count": "$count" } },
        "total": { "$sum": "$count" },
    }},
    { "$unwind": "$purch" },
    { "$project": {
        "vendor": "$purch.vendor",
        "total": { 
            "$divide": [ "$purch.count", "$total" ]
        }
    }},
    { "$group": {
        "_id": "$vendor",
        "total": { "$sum": "$total" }
    }}
])

Alternatively, the mapReduce approach involves two steps: first reducing responses by vendor for each user, then further reducing them down to vendor totals:

db.collection.mapReduce(
    function () {
        emit(
            this.account,
            {
                "data": [{
                    "vendor": this.vendor,
                    "count": 1,
                }],
                "total": 1,
                "seen": false
            }
        );
    },
    function (key,values) {

        var reduced = { data: [], total: 0, seen: true };

        values.forEach(function(value) {
            value.data.forEach(function(data) {
                var index = -1;
                for (var i = 0; i <=reduced.data.length-1; i++) {

                    if ( reduced.data[i].vendor == data.vendor ) {
                        index = i;
                        break;
                    }
                }

                if ( index == -1 ) {
                    reduced.data.push(data);
                } else {
                    if (!value.seen)
                        reduced.data[index].count += data.count;
                }
            });
        });

        reduced.data.map(function(x) {
            reduced.total += x.count;
        });

        return reduced;
    },
    { 
        "out": { "replace": "output" },
        "finalize": function (key,value) {

            var result = {
                data: []
            };

            result.data = value.data.map(function(x) {
                var res = { };
                res["vendor"] = x.vendor;
                res["total"] = x.count / value.total;
                return res;
            });

            return result;
        }
    }
)

The second part involves outputting the results:

db.output.mapReduce(
    function () {
        this.value.data.forEach(function(data){
            emit( data.vendor, data.total );
        });
    },
    function(key,values) {
        return Array.sum( values );
    },
    { "out": { "inline": 1 } }
)

Considering the size of your data, the mapReduce approach may be slower as it requires additional processing and output to a collection. On the other hand, the aggregation framework approach typically runs faster, but could face slowdowns depending on the size of the vendor array per user.

Answer 3

Answer №2

In response to Neil Lunn's previous answer, I had a moment of realization yesterday that the aggregation process would need to be multi-step if using map reduce. I appreciate your approach of utilizing map reduce to write to a collection, especially in dealing with larger datasets. I'm also intrigued by the potential performance boost from trying out the .aggregate() method.

After some experimentation, I settled on the following solution for our dataset:

1. Utilize the aggregation framework to calculate purchases per account.
2. Convert the results into a map for quicker access.
3. Implement map reduce on the collection, leveraging the 'scope' field to pass in the account total map created in step 2.

The code snippet resembles the following:

var agg = this.db.aggregate([
    {
        $group: {
            _id: {
                user: '$account'
            },
            total: {
                $sum: 1
            }
        }
    }
]);

var accountMap = {};
for (var index in agg.result) {
    var entry = agg.result[index];
    addToMap(accountMap, entry._id.user, entry.total);
}

// Free up memory by deleting agg?
delete agg;

var mapFunction = function() {
    var key = this.vendor;
    var value = 1 / accountMap[this.account];

    emit(key, value);
};

var reduceFunction = function(key, values) {
    return(Array.sum(values));
};

var res = this.db.mapReduce(mapFunction, reduceFunction, {
    out: {
        inline: 1
    },
    scope: {
        'accountMap': accountMap
    }
});

// Clearing up accountMap
delete accountMap;

var transformed = {};
for (var index in res.results) {
    transformed[entry._id] = entry.value;
}

Answer 4

In response to Neil Lunn's previous answer, I had a moment of realization yesterday that the aggregation process would need to be multi-step if using map reduce. I appreciate your approach of utilizing map reduce to write to a collection, especially in dealing with larger datasets. I'm also intrigued by the potential performance boost from trying out the .aggregate() method.

After some experimentation, I settled on the following solution for our dataset:

1. Utilize the aggregation framework to calculate purchases per account.
2. Convert the results into a map for quicker access.
3. Implement map reduce on the collection, leveraging the 'scope' field to pass in the account total map created in step 2.

The code snippet resembles the following:

var agg = this.db.aggregate([
    {
        $group: {
            _id: {
                user: '$account'
            },
            total: {
                $sum: 1
            }
        }
    }
]);

var accountMap = {};
for (var index in agg.result) {
    var entry = agg.result[index];
    addToMap(accountMap, entry._id.user, entry.total);
}

// Free up memory by deleting agg?
delete agg;

var mapFunction = function() {
    var key = this.vendor;
    var value = 1 / accountMap[this.account];

    emit(key, value);
};

var reduceFunction = function(key, values) {
    return(Array.sum(values));
};

var res = this.db.mapReduce(mapFunction, reduceFunction, {
    out: {
        inline: 1
    },
    scope: {
        'accountMap': accountMap
    }
});

// Clearing up accountMap
delete accountMap;

var transformed = {};
for (var index in res.results) {
    transformed[entry._id] = entry.value;
}

Utilizing aggregation techniques or Map Reduce processes to generate standardized 'Distinct Paying Customers for each Vendor' data

Answer №1

Answer №2

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