Skip to content
Studio 3T - The professional GUI, IDE and client for MongoDB
  • Tools
    • Aggregation Editor
    • IntelliShell
    • Visual Query Builder
    • Export Wizard
    • Import Wizard
    • Query Code
    • SQL Query
    • Connect
    • Schema Explorer
    • Compare
    • SQL ⇔ MongoDB Migration
    • Data Masking
    • Task Scheduler
    • Reschema
    • More Tools and Features
  • Solutions
  • Resources
    • Knowledge Base
    • MongoDB Tutorials & Courses
    • Tool/Feature Documentation
    • Blog
    • Community
    • Testimonials
    • Whitepapers
    • Reports
  • Contact us
    • Contact
    • Sales Support
    • Feedback and Support
    • Careers
    • About Us
  • Store
    • Buy Now
    • Preferred Resellers
    • Team Pricing
  • Download
  • My 3T
search

Academy 3T

  • Explore our courses
    • MongoDB 101: Getting Started
    • MongoDB 201: Querying MongoDB Data
    • MongoDB 301: Aggregation
  • Get certified

Lesson 3, Exercise 2: Grouping the documents in the aggregation pipeline

MongoDB 201: Querying MongoDB Data Working with the MongoDB Aggregation Pipeline Lesson 3, Exercise 2: Grouping the documents in the aggregation pipeline

In this exercise, you will add the second stage to the pipeline. This stage is based on the $group aggregate operator, which lets you group the documents in the pipeline based on a specific field.

To group the documents in the aggregation pipeline

1. On the Pipeline tab of the Aggregation Editor, ensure that the Stage 1 row is selected (the $match row), click the small down arrow to the right of the Add button, and click Add New Stage After Selected Stage. 

The Aggregation Editor adds the Stage 2 tab to the right of the Stage 1 tab and makes the new tab active. The editor again selects the $match operator for the new stage.

Aggregation Editor

2. On the Stage 2 tab, select the $group option from the Filter drop-down list in the upper left corner.

The $group operator groups the pipeline documents by a specified expression—typically the name of a field.

When you select the $group option, the Aggregation Editor adds a set of curly braces to the editor to enclose the expression, along with the _id field and several placeholders, which are a mix of plain text and comments.

3. In the editor window, delete the _id field and all the placeholders and type the following expression:

_id: "$address.city", 
total: { $sum: "$transactions" }

The expression includes two arguments, separated by a comma. Each argument is its own expression. 

  • The first argument defines the _id field, which determines how the documents should be grouped.

In this case, the grouping is based on the values in the address.city field, which is represented by the field name, preceded by a dollar sign ($address.city).

  • The second argument defines how the grouped documents should be aggregated.

The first part of the argument species the name of the outputted column (total), which will contain the aggregated totals. 

The second part of this argument—enclosed in curly braces—specifies an accumulator operator ($sum), followed by the field on which the aggregation will be based ($transactions).

Together these elements specify that the query should return the total number of transactions per city.

4. In the Stage Input pane in the bottom section, click the Execute button. This will execute the pipeline up to but not including the second stage.

5. In the Stage Output pane in the bottom section, click the Execute button. This will execute the pipeline up to and including the second stage. The following figure shows both sets of results, as they appear in Table View. 

output view

The figure shows only part of the Stage 2 results (which total 37 documents), but you can see how they include the number of transactions for each city.

6. Go to the Query Code tab. Verify that the code includes the $group operator and its expression.

7. Go to the Pipeline tab. Verify that the pipeline includes the Stage 2 row and that the row contains the $group operator and its expression.

Leave the Aggregation tab and aggregate statement in place for the next exercise.

Previous Topic
Back to Lesson
Next Topic
  • Course Home Expand All
    Performing MongoDB CRUD Operations
    4 Topics | 1 Quiz
    Lesson 1, Exercise 1: Adding a document to a collection
    Lesson 1, Exercise 2: Viewing a document in a collection
    Lesson 1, Exercise 3: Updating a document in a collection
    Lesson 1, Exercise 4: Deleting a document from a collection
    Test your skills: Performing CRUD Operations
    Building MongoDB find() Queries
    4 Topics | 1 Quiz
    Lesson 2: The MongoDB find method
    Lesson 2, Exercise 1: Using IntelliShell to build and run find statements
    Lesson 2, Exercise 2: Using Visual Query Builder to build and run find statements
    Lesson 2, Exercise 3: Using Query Code and IntelliShell to modify and run a find statement
    Test your skills: Building MongoDB find() Queries
    Working with the MongoDB Aggregation Pipeline
    6 Topics | 1 Quiz
    Lesson 3: Introducing the MongoDB aggregate method
    Lesson 3, Exercise 1: Filtering the documents in the aggregation pipeline
    Lesson 3, Exercise 2: Grouping the documents in the aggregation pipeline
    Lesson 3, Exercise 3: Adding and removing fields in the aggregation pipeline
    Lesson 3, Exercise 4: Changing the field order in the aggregation pipeline
    Lesson 3, Exercise 5: Sorting the documents in the aggregation pipeline
    Test your skills: Working with the MongoDB Aggregation Pipeline
    Querying Arrays Using MongoDB $elemMatch
    4 Topics | 1 Quiz
    Lesson 4, Exercise 1: Using IntelliShell to query single and multiple values in an array
    Lesson 4, Exercise 2: Using Visual Query Builder to query a single array value
    Lesson 4, Exercise 3: Using Visual Query Builder to query multiple array values
    Test your skills: Querying Arrays Using MongoDB $elemMatch
    MongoDB 201 Mid-Course Feedback
    Querying Embedded Documents in MongoDB Arrays
    3 Topics | 1 Quiz
    Lesson 5, Exercise 1: Using the $elemMatch operator to query embedded documents
    Lesson 5, Exercise 2: Using conditional operators to query embedded documents
    Lesson 5, Exercise 3: Using Visual Query Builder to query embedded documents
    Test your skills: Querying Embedded Documents in Arrays
    Querying MongoDB with SQL SELECT Statements
    2 Topics | 1 Quiz
    Lesson 6, Exercise 1: Using the SQL Query tool to run SQL statements
    Lesson 6, Exercise 2: Using the SQL Query tool to aggregate collection data
    Test your skills: Querying MongoDB with SQL
    Working with MongoDB Views
    3 Topics | 1 Quiz
    Lesson 7, Exercise 1: Creating a MongoDB view
    Lesson 7, Exercise 2: Querying a MongoDB view
    Lesson 7, Exercise 3: Modifying and deleting a MongoDB view
    Test your skills: Working with MongoDB Views
    Course Extras
    Return to MongoDB 201: Querying MongoDB Data
  • Studio 3T

    MongoDB Enterprise Certified Technology PartnerSince 2014, 3T has been helping thousands of MongoDB developers and administrators with their everyday jobs by providing the finest MongoDB tools on the market. We guarantee the best compatibility with current and legacy releases of MongoDB, continue to deliver new features with every new software release, and provide high quality support.

    Find us on FacebookFind us on TwitterFind us on YouTubeFind us on LinkedIn

    Education

    • Free MongoDB Tutorials
    • Connect to MongoDB
    • Connect to MongoDB Atlas
    • Import Data to MongoDB
    • Export MongoDB Data
    • Build Aggregation Queries
    • Query MongoDB with SQL
    • Migrate from SQL to MongoDB

    Resources

    • Feedback and Support
    • Sales Support
    • Knowledge Base
    • FAQ
    • Reports
    • White Papers
    • Testimonials
    • Discounts

    Company

    • About Us
    • Blog
    • Careers
    • Legal
    • Press
    • Privacy Policy
    • EULA

    © 2023 3T Software Labs Ltd. All rights reserved.

    • Privacy Policy
    • Cookie settings
    • Impressum

    We value your privacy

    With your consent, we and third-party providers use cookies and similar technologies on our website to analyse your use of our site for market research or advertising purposes ("analytics and marketing") and to provide you with additional functions (“functional”). This may result in the creation of pseudonymous usage profiles and the transfer of personal data to third countries, including the USA, which may have no adequate level of protection for the processing of personal data.

    By clicking “Accept all”, you consent to the storage of cookies and the processing of personal data for these purposes, including any transfers to third countries. By clicking on “Decline all”, you do not give your consent and we will only store cookies that are necessary for our website. You can customize the cookies we store on your device or change your selection at any time - thus also revoking your consent with effect for the future - under “Manage Cookies”, or “Cookie Settings” at the bottom of the page. You can find further information in our Privacy Policy.
    Accept all
    Decline all
    Manage cookies
    ✕

    Privacy Preference Center

    With your consent, we and third-party providers use cookies and similar technologies on our website to analyse your use of our site for market research or advertising purposes ("analytics and marketing") and to provide you with additional functions (“functional”). This may result in the creation of pseudonymous usage profiles and the transfer of personal data to third countries, including the USA, which may have no adequate level of protection for the processing of personal data. Please choose for which purposes you wish to give us your consent and store your preferences by clicking on “Accept selected”. You can find further information in our Privacy Policy.

    Accept all cookies

    Manage consent preferences

    Essential cookies are strictly necessary to provide an online service such as our website or a service on our website which you have requested. The website or service will not work without them.

    Performance cookies allow us to collect information such as number of visits and sources of traffic. This information is used in aggregate form to help us understand how our websites are being used, allowing us to improve both our website’s performance and your experience.

    Google Analytics

    Google Ads

    Bing Ads

    Facebook

    LinkedIn

    Quora

    Hotjar

    Reddit

    Functional cookies collect information about your preferences and choices and make using the website a lot easier and more relevant. Without these cookies, some of the site functionality may not work as intended.

    HubSpot

    Social media cookies are cookies used to share user behaviour information with a third-party social media platform. They may consequently effect how social media sites present you with information in the future.

    Accept selected