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Transform Your Data with Aggregation

On this page

  • Overview
  • Aggregation Versus Find Operations
  • Limitations
  • Aggregation Example
  • Explain an Aggregation
  • Additional Information
  • MongoDB Server Manual
  • API Documentation

Overview

In this guide, you can learn how to use the C++ driver to perform aggregation operations.

Aggregation operations process data in your MongoDB collections and return computed results. The MongoDB Aggregation framework, which is part of the Query API, is modeled on the concept of data processing pipelines. Documents enter a pipeline that contains one or more stages, and this pipeline transforms the documents into an aggregated result.

An aggregation operation is similar to a car factory. A car factory has an assembly line, which contains assembly stations with specialized tools to do specific jobs, like drills and welders. Raw parts enter the factory, and then the assembly line transforms and assembles them into a finished product.

The aggregation pipeline is the assembly line, aggregation stages are the assembly stations, and operator expressions are the specialized tools.

Aggregation Versus Find Operations

You can use find operations to perform the following actions:

  • Select which documents to return

  • Select which fields to return

  • Sort the results

You can use aggregation operations to perform the following actions:

  • Run find operations

  • Rename fields

  • Calculate fields

  • Summarize data

  • Group values

Limitations

Keep the following limitations in mind when using aggregation operations:

  • Returned documents cannot violate the BSON document size limit of 16 megabytes.

  • Pipeline stages have a memory limit of 100 megabytes by default. You can exceed this limit by setting the allow_disk_use field of a mongocxx::options::aggregate instance to true.

Important

$graphLookup Exception

The $graphLookup stage has a strict memory limit of 100 megabytes and ignores the allow_disk_use field.

Aggregation Example

Note

The examples in this guide use the restaurants collection in the sample_restaurants database from the Atlas sample datasets. To learn how to create a free MongoDB Atlas cluster and load the sample datasets, see the Get Started with Atlas guide.

To perform an aggregation, pass a mongocxx::pipeline instance containing the aggregation stages to the collection.aggregate() method.

The following code example produces a count of the number of bakeries in each borough of New York. To do so, it uses an aggregation pipeline that contains the following stages:

  • $match stage to filter for documents in which the cuisine field contains the value "Bakery"

  • $group stage to group the matching documents by the borough field, accumulating a count of documents for each distinct value

mongocxx::pipeline stages;
stages.match(make_document(kvp("cuisine", "Bakery")))
      .group(make_document(kvp("_id", "$borough"), kvp("count", make_document(kvp("$sum", 1)))));
auto cursor = collection.aggregate(stages);
for (auto&& doc : cursor) {
    std::cout << bsoncxx::to_json(doc) << std::endl;
}
{ "_id" : "Brooklyn", "count" : 173 }
{ "_id" : "Queens", "count" : 204 }
{ "_id" : "Bronx", "count" : 71 }
{ "_id" : "Staten Island", "count" : 20 }
{ "_id" : "Missing", "count" : 2 }
{ "_id" : "Manhattan", "count" : 221 }

Explain an Aggregation

To view information about how MongoDB executes your operation, you can instruct the MongoDB query planner to explain it. When MongoDB explains an operation, it returns execution plans and performance statistics. An execution plan is a potential way MongoDB can complete an operation. When you instruct MongoDB to explain an operation, it returns both the plan MongoDB executed and any rejected execution plans.

To explain an aggregation operation, run the explain database command by specifying the command in a BSON document and passing it as an argument to the run_command() method.

The following example instructs MongoDB to explain the aggregation operation from the preceding Aggregation Example:

mongocxx::pipeline stages;
stages.match(make_document(kvp("cuisine", "Bakery")))
      .group(make_document(kvp("_id", "$borough"), kvp("count", make_document(kvp("$sum", 1)))));
auto command = make_document(
    kvp("explain", make_document(
                        kvp("aggregate", "restaurants"),
                        kvp("pipeline", stages.view_array()),
                        kvp("cursor", make_document()))));
auto result = db.run_command(command.view());
std::cout << bsoncxx::to_json(result) << std::endl;
{ "explainVersion" : "2", "queryPlanner" : { "namespace" : "sample_restaurants.restaurants",
"indexFilterSet" : false, "parsedQuery" : { "cuisine" : { "$eq" : "Bakery" } }, "queryHash":
"...", "planCacheKey" : "...", "optimizedPipeline" : true, "maxIndexedOrSolutionsReached":
false, "maxIndexedAndSolutionsReached" : false, "maxScansToExplodeReached" : false,
"winningPlan" : { ... }
... }

Additional Information

MongoDB Server Manual

To view a full list of expression operators, see Aggregation Operators.

To learn about assembling an aggregation pipeline and view examples, see Aggregation Pipeline.

To learn more about creating pipeline stages, see Aggregation Stages.

To learn more about explaining MongoDB operations, see Explain Output and Query Plans.

API Documentation

For more information about executing aggregation operations with the C++ driver, see the following API documentation:

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