Perform Incremental Map-Reduce

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Data Setup

Initial Map-Reduce of Current Collection
Subsequent Incremental Map-Reduce
Aggregation Alternative

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Note

Aggregation Pipeline as Alternative

Starting in MongoDB 5.0, map-reduce is deprecated:

Instead of map-reduce, you should use an aggregation pipeline. Aggregation pipelines provide better performance and usability than map-reduce.
You can rewrite map-reduce operations using aggregation pipeline stages, such as $group, $merge, and others.
For map-reduce operations that require custom functionality, you can use the $accumulator and $function aggregation operators. You can use those operators to define custom aggregation expressions in JavaScript.

For examples of aggregation pipeline alternatives to map-reduce, see:

This section has an example aggregation pipeline alternative to map-reduce that does not use a custom function. For an example that uses a custom function, see Map-Reduce to Aggregation Pipeline.

To perform map-reduce operations, MongoDB provides the mapReduce command and, in mongosh, the db.collection.mapReduce() wrapper method.

If the map-reduce data set is constantly growing, you may want to perform an incremental map-reduce rather than performing the map-reduce operation over the entire data set each time.

To perform incremental map-reduce:

Run a map-reduce job over the current collection and output the result to a separate collection.
When you have more data to process, run subsequent map-reduce jobs with:
- the query parameter that specifies conditions that match only the new documents.
- the out parameter that specifies the reduce action to merge the new results into the existing output collection.

Consider the following example where you schedule a map-reduce operation on a usersessions collection to run at the end of each day.

Data Setup

The usersessions collection contains documents that log users' sessions each day, for example:

db.usersessions.insertMany([
   { userid: "a", start: ISODate('2020-03-03 14:17:00'), length: 95 },
   { userid: "b", start: ISODate('2020-03-03 14:23:00'), length: 110 },
   { userid: "c", start: ISODate('2020-03-03 15:02:00'), length: 120 },
   { userid: "d", start: ISODate('2020-03-03 16:45:00'), length: 45 },
   { userid: "a", start: ISODate('2020-03-04 11:05:00'), length: 105 },
   { userid: "b", start: ISODate('2020-03-04 13:14:00'), length: 120 },
   { userid: "c", start: ISODate('2020-03-04 17:00:00'), length: 130 },
   { userid: "d", start: ISODate('2020-03-04 15:37:00'), length: 65 }
])

Initial Map-Reduce of Current Collection

Run the first map-reduce operation as follows:

Define the map function that maps the userid to an object that contains the fields total_time, count, and avg_time:

var mapFunction = function() {
    var key = this.userid;
    var value = { total_time: this.length, count: 1, avg_time: 0 };
    emit( key, value );
};

Define the corresponding reduce function with two arguments key and values to calculate the total time and the count. The key corresponds to the userid, and the values is an array whose elements corresponds to the individual objects mapped to the userid in the mapFunction.

var reduceFunction = function(key, values) {
   var reducedObject = { total_time: 0, count:0, avg_time:0 };
   values.forEach(function(value) {
      reducedObject.total_time += value.total_time;
      reducedObject.count += value.count;
   });
   return reducedObject;
};

Define the finalize function with two arguments key and reducedValue. The function modifies the reducedValue document to add another field average and returns the modified document.

var finalizeFunction = function(key, reducedValue) {
   if (reducedValue.count > 0)
      reducedValue.avg_time = reducedValue.total_time / reducedValue.count;
   return reducedValue;
};

Perform map-reduce on the usersessions collection using the mapFunction, the reduceFunction, and the finalizeFunction functions. Output the results to a collection session_stats. If the session_stats collection already exists, the operation will replace the contents:
```
db.usersessions.mapReduce(
   mapFunction,
   reduceFunction,
   {
     out: "session_stats",
     finalize: finalizeFunction
   }
)
```

Query the session_stats collection to verify the results:

db.session_stats.find().sort( { _id: 1 } )

The operation returns the following documents:

{ "_id" : "a", "value" : { "total_time" : 200, "count" : 2, "avg_time" : 100 } }
{ "_id" : "b", "value" : { "total_time" : 230, "count" : 2, "avg_time" : 115 } }
{ "_id" : "c", "value" : { "total_time" : 250, "count" : 2, "avg_time" : 125 } }
{ "_id" : "d", "value" : { "total_time" : 110, "count" : 2, "avg_time" : 55 } }

Subsequent Incremental Map-Reduce

Later, as the usersessions collection grows, you can run additional map-reduce operations. For example, add new documents to the usersessions collection:

db.usersessions.insertMany([
   { userid: "a", ts: ISODate('2020-03-05 14:17:00'), length: 130 },
   { userid: "b", ts: ISODate('2020-03-05 14:23:00'), length: 40 },
   { userid: "c", ts: ISODate('2020-03-05 15:02:00'), length: 110 },
   { userid: "d", ts: ISODate('2020-03-05 16:45:00'), length: 100 }
])

At the end of the day, perform incremental map-reduce on the usersessions collection, but use the query field to select only the new documents. Output the results to the collection session_stats, but reduce the contents with the results of the incremental map-reduce:

db.usersessions.mapReduce(
   mapFunction,
   reduceFunction,
   {
     query: { ts: { $gte: ISODate('2020-03-05 00:00:00') } },
     out: { reduce: "session_stats" },
     finalize: finalizeFunction
   }
);

Query the session_stats collection to verify the results:

db.session_stats.find().sort( { _id: 1 } )

The operation returns the following documents:

{ "_id" : "a", "value" : { "total_time" : 330, "count" : 3, "avg_time" : 110 } }
{ "_id" : "b", "value" : { "total_time" : 270, "count" : 3, "avg_time" : 90 } }
{ "_id" : "c", "value" : { "total_time" : 360, "count" : 3, "avg_time" : 120 } }
{ "_id" : "d", "value" : { "total_time" : 210, "count" : 3, "avg_time" : 70 } }

Aggregation Alternative

As an alternative to map-reduce, you can use an aggregation pipeline that combines $group and $merge stages to achieve the same result in a more flexible operation.

Recreate the usersessions collection:

db.usersessions.drop();
db.usersessions.insertMany([
   { userid: "a", start: ISODate('2020-03-03 14:17:00'), length: 95 },
   { userid: "b", start: ISODate('2020-03-03 14:23:00'), length: 110 },
   { userid: "c", start: ISODate('2020-03-03 15:02:00'), length: 120 },
   { userid: "d", start: ISODate('2020-03-03 16:45:00'), length: 45 },
   { userid: "a", start: ISODate('2020-03-04 11:05:00'), length: 105 },
   { userid: "b", start: ISODate('2020-03-04 13:14:00'), length: 120 },
   { userid: "c", start: ISODate('2020-03-04 17:00:00'), length: 130 },
   { userid: "d", start: ISODate('2020-03-04 15:37:00'), length: 65 }
])

Using the available aggregation pipeline operators, you can rewrite the map-reduce example without defining custom functions:

db.usersessions.aggregate([
   { $group: { _id: "$userid", total_time: { $sum: "$length" }, count: { $sum: 1 }, avg_time: { $avg: "$length" } } },
   { $project: { value: { total_time: "$total_time", count: "$count", avg_time: "$avg_time" } } },
   { $merge: {
      into: "session_stats_agg",
      whenMatched: [ { $set: {
         "value.total_time": { $add: [ "$value.total_time", "$$new.value.total_time" ] },
         "value.count": { $add: [ "$value.count", "$$new.value.count" ] },
         "value.avg": { $divide: [ { $add: [ "$value.total_time", "$$new.value.total_time" ] },  { $add: [ "$value.count", "$$new.value.count" ] } ] }
      } } ],
      whenNotMatched: "insert"
   }}
])

The $group groups by the userid and calculates:

The total_time using the $sum operator
The count using the $sum operator
The avg_time using the $avg operator

The operation returns the following documents:

{ "_id" : "c", "total_time" : 250, "count" : 2, "avg_time" : 125 }
{ "_id" : "d", "total_time" : 110, "count" : 2, "avg_time" : 55 }
{ "_id" : "a", "total_time" : 200, "count" : 2, "avg_time" : 100 }
{ "_id" : "b", "total_time" : 230, "count" : 2, "avg_time" : 115 }

The $project stage reshapes the output document to mirror the map-reduce's output to have two fields _id and value. The stage is optional if you do not need to mirror the _id and value structure.

{ "_id" : "a", "value" : { "total_time" : 200, "count" : 2, "avg_time" : 100 } }
{ "_id" : "d", "value" : { "total_time" : 110, "count" : 2, "avg_time" : 55 } }
{ "_id" : "b", "value" : { "total_time" : 230, "count" : 2, "avg_time" : 115 } }
{ "_id" : "c", "value" : { "total_time" : 250, "count" : 2, "avg_time" : 125 } }

The $merge stage outputs the results to a session_stats_agg collection. If an existing document has the same _id as the new result, the operation applies the specified pipeline to calculate the total_time, count, and avg_time from the result and the existing document. If there is no existing document with the same _id in the session_stats_agg, the operation inserts the document.

Query the session_stats_agg collection to verify the results:

db.session_stats_agg.find().sort( { _id: 1 } )

The operation returns the following documents:

{ "_id" : "a", "value" : { "total_time" : 200, "count" : 2, "avg_time" : 100 } }
{ "_id" : "b", "value" : { "total_time" : 230, "count" : 2, "avg_time" : 115 } }
{ "_id" : "c", "value" : { "total_time" : 250, "count" : 2, "avg_time" : 125 } }
{ "_id" : "d", "value" : { "total_time" : 110, "count" : 2, "avg_time" : 55 } }

Add new documents to the usersessions collection:

db.usersessions.insertMany([
   { userid: "a", ts: ISODate('2020-03-05 14:17:00'), length: 130 },
   { userid: "b", ts: ISODate('2020-03-05 14:23:00'), length: 40 },
   { userid: "c", ts: ISODate('2020-03-05 15:02:00'), length: 110 },
   { userid: "d", ts: ISODate('2020-03-05 16:45:00'), length: 100 }
])

Add a $match stage at the start of the pipeline to specify the date filter:

db.usersessions.aggregate([
   { $match: { ts: { $gte: ISODate('2020-03-05 00:00:00') } } },
   { $group: { _id: "$userid", total_time: { $sum: "$length" }, count: { $sum: 1 }, avg_time: { $avg: "$length" } } },
   { $project: { value: { total_time: "$total_time", count: "$count", avg_time: "$avg_time" } } },
   { $merge: {
      into: "session_stats_agg",
      whenMatched: [ { $set: {
         "value.total_time": { $add: [ "$value.total_time", "$$new.value.total_time" ] },
         "value.count": { $add: [ "$value.count", "$$new.value.count" ] },
         "value.avg_time": { $divide: [ { $add: [ "$value.total_time", "$$new.value.total_time" ] },  { $add: [ "$value.count", "$$new.value.count" ] } ] }
      } } ],
      whenNotMatched: "insert"
   }}
])

Query the session_stats_agg collection to verify the results:

db.session_stats_agg.find().sort( { _id: 1 } )

The operation returns the following documents:

{ "_id" : "a", "value" : { "total_time" : 330, "count" : 3, "avg_time" : 110 } }
{ "_id" : "b", "value" : { "total_time" : 270, "count" : 3, "avg_time" : 90 } }
{ "_id" : "c", "value" : { "total_time" : 360, "count" : 3, "avg_time" : 120 } }
{ "_id" : "d", "value" : { "total_time" : 210, "count" : 3, "avg_time" : 70 } }

Optional. To avoid having to modify the aggregation pipeline's $match date condition each time you run, you can define wrap the aggregation in a helper function:

updateSessionStats = function(startDate) {
   db.usersessions.aggregate([
      { $match: { ts: { $gte: startDate } } },
      { $group: { _id: "$userid", total_time: { $sum: "$length" }, count: { $sum: 1 }, avg_time: { $avg: "$length" } } },
      { $project: { value: { total_time: "$total_time", count: "$count", avg_time: "$avg_time" } } },
      { $merge: {
         into: "session_stats_agg",
         whenMatched: [ { $set: {
            "value.total_time": { $add: [ "$value.total_time", "$$new.value.total_time" ] },
            "value.count": { $add: [ "$value.count", "$$new.value.count" ] },
            "value.avg_time": { $divide: [ { $add: [ "$value.total_time", "$$new.value.total_time" ] },  { $add: [ "$value.count", "$$new.value.count" ] } ] }
         } } ],
         whenNotMatched: "insert"
      }}
   ]);
};

Then, to run, you would just pass in the start date to the updateSessionStats() function:

updateSessionStats(ISODate('2020-03-05 00:00:00'))

Perform Incremental Map-Reduce

Note

Aggregation Pipeline as Alternative

Data Setup

Initial Map-Reduce of Current Collection

Subsequent Incremental Map-Reduce

Aggregation Alternative

Tip

See also:

Perform Incremental Map-Reduce.leafygreen-ui-m0pgrr{-webkit-align-self:center;-ms-flex-item-align:center;align-self:center;padding:0 10px;visibility:hidden;}.leafygreen-ui-a30zj9{color:#889397;vertical-align:middle;margin-top:-2px;}.css-fmznk8{margin-top:-85px;position:absolute;padding-bottom:2px;}

Note

Aggregation Pipeline as Alternative

Data Setup

Initial Map-Reduce of Current Collection

Subsequent Incremental Map-Reduce

Aggregation Alternative

Tip

See also:

Perform Incremental Map-Reduce