MongoDB's aggregation is one of the most powerful tools in the database, allowing you to perform complex data analysis on large datasets. This article will show you how to use aggregation to create powerful data aggregations.

Basic aggregations

An aggregation is a powerful tool for data analysis. It allows you to perform complex queries on datasets. To use the aggregations, you must first have a MongoDB collection. In this example, we'll use a collection of blog posts.

$match stage

Once you have a collection, you can begin creating aggregations. The simplest aggregation is a match, which filters the documents in the collection to those that match the specified criteria.

For example, the following match aggregation will return all documents in the collection where the author is "John Doe":

db.posts.aggregate( [ { $match: { author: "John Doe" } } ] ) 

If you want to return only the documents where the author is "John Doe" and the title contains the word "MongoDB", you can use the following aggregation:

db.posts.aggregate( [ { $match: { author: "John Doe", title: /MongoDB/i } } ] )

The match aggregation is very powerful, but it can only be used to filter documents. If you want to perform complex data analysis, you will need to use other aggregations.

$group stage

The next aggregation is the group. The $group aggregation allows you to group documents by a specific field. For example, the following aggregation will group all documents by the author field:

db.posts.aggregate( [ { $group: { _id: "$author", count: { $sum: 1 } } } ] )

The group aggregation returns a document for each author in the collection. The document contains the author's name and the number of documents written by the author.

Combine multiple stages into a pipelines

If you want to know how many posts each author has written about MongoDB, you can use the following aggregation:

db.posts.aggregate( [ 
  { $match: { title: /MongoDB/i } },
  { $group: { _id: "$author", count: { $sum: 1 } } } 
] )

The match aggregation will filter the documents to only those that contain the word "MongoDB" in the title. The group aggregation will then group the documents by author and return the number of posts each author has written about MongoDB.

The group aggregation is very powerful, but it can only be used to group documents. If you want to perform complex data analysis, you will need to use other aggregations.

$project stage

The final aggregation is the project. The project aggregation allows you to transform the documents in the collection. For example, the following aggregation will return the title and author of each document in the collection:

db.posts.aggregate( [ { $project: { title: 1, author: 1 } } ] )

Aggregation pipeline

MongoDB supports a wide variety of aggregation stages, including those for arithmetic, comparison, and logical operations. See the list of MongoDB aggregation pipeline stages.

In addition to match, the aggregation framework provides a number of other operators, including:

• $project: This operator allows you to select, rename, and reshape fields in the documents in the collection.
• $group: This operator groups documents together by a specified key.
• $sort: This operator sorts the documents in the collection by a specified key.
• $limit: This operator limits the number of documents that are returned from the aggregation.
• $skip: This operator skips a specified number of documents.

To learn more about the aggregation framework, check out the MongoDB documentation.

Aggregations made easy with Mingo

Mingo is a great tool for those who are just getting started with MongoDB, and it is also great for those who want a more user-friendly interface for their database. You can easily create MongoDB aggregations with our GUI.

You can learn more about our MongoDB Aggregator Tool or you can download it right away and give it a try.