MongoDB Aggregation

Summary: in this tutorial, you’ll learn how to use MongoDB aggregation operations to group documents and apply one or more aggregation functions to the groups.

Introduction to the MongoDB aggregation operations

MongoDB aggregation operations allow you to process multiple documents and return the calculated results.

Typically, you use aggregation operations to group documents by specific field values and perform aggregations on the grouped documents to return computed results.

For example, you can use aggregation operations to take a list of sales orders and calculate the total sales amounts grouped by the products.

To perform aggregation operations, you use aggregation pipelines. An aggregation pipeline contains one or more stages that process the input documents:

MongoDB Aggregation

Each stage in the aggregation pipeline performs an operation on the input documents and returns the output documents. The output documents are then passed to the next stage. The final stage returns the calculated result.

The operations on each stage can be one of the following:

  • $project – select fields for the output documents.
  • $match – select documents to be processed.
  • $limit – limit the number of documents to be passed to the next stage.
  • $skip – skip a specified number of documents.
  • $sort – sort documents.
  • $group – group documents by a specified key.

The following shows the syntax for defining an aggregation pipeline:

db.collection.aggregate([{ $match:...},{$group:...},{$sort:...}]);
Code language: CSS (css)

In this syntax:

  • First, call the aggregate() method on the collection.
  • Second, pass an array of documents, where each document describes a stage in the pipeline.

MongoDB 4.2 or later allows you to use an aggregation pipeline to update documents.

MongoDB aggregation example

First, switch to the coffeeshop database that stores the coffee sales:

use coffeeshop
Code language: PHP (php)

Second, insert documents into the sales collection:

db.sales.insertMany([ { "_id" : 1, "item" : "Americanos", "price" : 5, "size": "Short", "quantity" : 22, "date" : ISODate("2022-01-15T08:00:00Z") }, { "_id" : 2, "item" : "Cappuccino", "price" : 6, "size": "Short","quantity" : 12, "date" : ISODate("2022-01-16T09:00:00Z") }, { "_id" : 3, "item" : "Lattes", "price" : 15, "size": "Grande","quantity" : 25, "date" : ISODate("2022-01-16T09:05:00Z") }, { "_id" : 4, "item" : "Mochas", "price" : 25,"size": "Tall", "quantity" : 11, "date" : ISODate("2022-02-17T08:00:00Z") }, { "_id" : 5, "item" : "Americanos", "price" : 10, "size": "Grande","quantity" : 12, "date" : ISODate("2022-02-18T21:06:00Z") }, { "_id" : 6, "item" : "Cappuccino", "price" : 7, "size": "Tall","quantity" : 20, "date" : ISODate("2022-02-20T10:07:00Z") }, { "_id" : 7, "item" : "Lattes", "price" : 25,"size": "Tall", "quantity" : 30, "date" : ISODate("2022-02-21T10:08:00Z") }, { "_id" : 8, "item" : "Americanos", "price" : 10, "size": "Grande","quantity" : 21, "date" : ISODate("2022-02-22T14:09:00Z") }, { "_id" : 9, "item" : "Cappuccino", "price" : 10, "size": "Grande","quantity" : 17, "date" : ISODate("2022-02-23T14:09:00Z") }, { "_id" : 10, "item" : "Americanos", "price" : 8, "size": "Tall","quantity" : 15, "date" : ISODate("2022-02-25T14:09:00Z")} ]);
Code language: JavaScript (javascript)

Third, use an aggregation pipeline to filter the sales by the Americanos, calculate the sum of quantity grouped by sizes, and sort the result document by the total quantity in descending order.

db.sales.aggregate([ { $match: { item: "Americanos" } }, { $group: { _id: "$size", totalQty: {$sum: "$quantity"} } }, { $sort: { totalQty : -1} } ]);
Code language: PHP (php)


[ { _id: 'Grande', totalQty: 33 }, { _id: 'Short', totalQty: 22 }, { _id: 'Tall', totalQty: 15 } ]
Code language: JavaScript (javascript)

This aggregation pipeline contains three stages:

MongoDB Aggregation Example
  • Stage 1: the $match stage filters the orders by Americanos coffee and passes the filtered documents to the $group stage.
  • Stage 2: the $group stage groups the filtered documents by coffee size and uses the $sum to calculate the total quantity. The $group stage creates a new collection of documents where each document contains two fields _id and totalQty, and passed these documents to the $sort stage.
  • Stage 3: the $sort stage sorts the documents by the totalQty field in the descending order and returns the result documents.

SQL equivalent to MongoDB aggregation

If you’re familiar with SQL, the above aggregation pipeline is equivalent to the following SQL statement:

select name as _id, sum(quantity) as totalQty from sales where name = 'Americanos' group by name order by totalQty desc;
Code language: SQL (Structured Query Language) (sql)

The following table shows the comparison between SQL and MongoDB aggregation:

SQL clauseMongoDB Aggregation
$group functions: $avg, $count, $sum, $max, $min
group by$group

In this next tutorial, you’ll learn how to use the aggregation pipeline to perform more complex operations.

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