Schema Design Patterns and Anti-patterns
Learn essential schema design patterns like inheritance and computed patterns. Learn to identify and avoid common anti-patterns, such as unbounded arrays and bloated documents, to optimize your MongoDB database for performance and efficiency.
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Upon completion of the Schema Patterns and Anti-patterns skill check, you will earn a Credly Badge that you are able to share with your network. |
Learning Objectives
Apply Schema Design Patterns
You will be able to define common schema design patterns such as the inheritance pattern, computed pattern, and extended reference pattern, and understand how and when to apply them.
Identify Anti-patterns
You will be able to identify anti-patterns such as unbounded arrays and bloated documents and understand how to avoid them.
Who is this Course Good for?
This skill is ideal for developers who want to deepen their MongoDB data modeling skills and design schemas that support both performance and long-term maintainability. If you are already comfortable with MongoDB fundamentals and basic schema design, but are looking for clearer guidance on how to apply reusable schema patterns and avoid common pitfalls, the Schema Design Patterns and Antipatterns Skill Badge is for you. It is especially valuable for engineers working on applications with evolving requirements, where data structures change over time and poor schema choices can quickly lead to performance issues or operational complexity. By focusing on practical MongoDB data modeling techniques, this skill helps you recognize when to apply specific patterns, how to combine them, and how to avoid schema designs that harm query performance and scalability.
What to Expect in this Course
In this skill badge, you will learn how to use schema design patterns and antipatterns as a toolkit for optimizing MongoDB data models. The skill begins by placing patterns within the broader MongoDB data modeling process, highlighting how they fit into the third design phase: applying schema design patterns after you have identified workloads and relationships. You explore an example bookstore application that sells audiobooks, ebooks, and printed books, and use it as a consistent context for practicing each pattern. You then define what a pattern is in the context of MongoDB — reusable, adaptable guidelines for solving recurring data modeling problems, not rigid solutions. You see how patterns can be combined like ingredients in a recipe book to transform and optimize schemas for specific application needs.
From there, the skill introduces three core schema design patterns: the Inheritance pattern, the Computed pattern, and the Extended Reference pattern. You learn when and how to use each one, and how they help you keep your data modeling in MongoDB predictable and easier to reason about. You will see how the Inheritance pattern helps you model shared and specialized fields across related document types, how the Computed pattern precomputes values to speed up frequent reads, and how the Extended Reference pattern balances embedding and referencing for flexible, performant access patterns. Understanding these patterns and their trade-offs helps you design MongoDB schemas that deliver consistent performance as your application grows.
The course then turns to schema design antipatterns — commonly used approaches that seem helpful initially but lead to poor performance over time. You learn what schema design antipatterns are and how they differ from healthy patterns, focusing on issues rooted in bad schema design practices rather than one-off mistakes. You will examine the unbounded array antipattern, where arrays inside documents can grow without limit and negatively affect updates, reads, and storage, and the bloated documents antipattern, which occurs when data that is not accessed together is stored in the same document. For each, you learn how to recognize the warning signs and what steps you can take to fix or avoid them. Hands-on labs give you opportunities to apply patterns and identify antipatterns in realistic MongoDB scenarios so you can confidently transfer these skills to your own projects and
Summary of the Course
- Understand where schema patterns fit into the three phases of MongoDB data modeling and how they guide design decisions.
- Define what a schema design pattern is and how patterns can be reused, adapted, and combined to solve recurring data modeling problems.
- Apply the Inheritance pattern to model shared and specialized document structures efficiently.
- Use the Computed pattern to precompute and store values that improve read performance in MongoDB.
- Implement the Extended Reference pattern to balance embedding and referencing for flexible, performant access patterns.
- Define schema design antipatterns and explain how they differ from healthy patterns.
- Identify the unbounded array and bloated documents antipatterns in MongoDB schemas and understand why they harm performance and scalability.
- Plan and implement remediation strategies that replace antipatterns with more effective MongoDB data modeling approaches.
Joel Lord | Lead, Curriculum Eng/Technologist
Joel Lord is a curriculum engineer at MongoDB who is committed to empowering developers through education and active community involvement. With more than twenty years of experience in software development, developer advocacy, and technical education, he combines extensive expertise with a dedication to making complex topics more understandable.
Holding a Bachelor of Science in computational astrophysics from Université Laval, Joel started his career in web development before he focused on assisting others in their learning journeys. At MongoDB, he develops educational materials designed to equip developers with the skills to build improved applications, drawing on his wide-ranging experience as a speaker at global conferences.
When he is not working, Joel enjoys stargazing in remote camping areas, experimenting with inventive brewing methods in his garage, and offering emotional support to his two cats, who often appear as guests during his Zoom meetings.
Manuel Fontan Garcia | Senior Technologist, Education
Manuel is a Senior Technologist on the Curriculum team at MongoDB. Previously he was a Senior Technical Services Engineer in the Core team at MongoDB. In between Manuel worked as a database reliability engineer at Slack for a little over 2 years and then for Cognite until he re-joined MongoDB. With over 15 years experience in software development and distributed systems, he is naturally curious and holds a Telecommunications Engineering MSc from Vigo University (Spain) and a Free and Open Source Software MSc from Rey Juan Carlos University (Spain).
Emily Pope | Senior Curriculum Designer
Emily Pope is a Senior Curriculum Designer at MongoDB. She loves learning and loves making it easy for others to learn how and when to use deeply technical products. Recently, she's been creating AI and vector search content for MongoDB University. Before that, she's created learning experiences on databases, computer science, full stack development, and even clinical trial design and analysis. Emily holds an Ed.M. in International Education Policy from Harvard Graduate School of Education and began her career as an English teacher in Turkiye with the Fulbright program.
Daniel Curran | Senior Software Engineer
Daniel is a Senior Software Engineer at MongoDB. Before joining MongoDB, he worked as an Instructional Designer and Content Developer specialising in technical content for a host of international clients. Daniel's goal is to remove obstacles so learners can feel confident on their journey to become masters of MongoDB.
Parker Faucher| Senior Software Engineer
Parker is a Senior Software Engineer on the Education team at MongoDB. Prior to joining MongoDB, he helped maintain a world class developer bootcamp that was offered in multiple universities. He is a self taught developer who loves being able to give back to the community that has helped him so much.
John McCambridge | Curriculum Engineer
John is a Curriculum Engineer on the University team at MongoDB. Before his work as a Curriculum Engineer, he was an instructor and teaching assistant for coding boot camps at UT (Austin), and UCLA. Additionally, he worked as a QA engineer for a startup called Coder and spent five years at Apple Inc. John is a passionate software engineer and educator who enjoys taking complex topics and making them digestible for the community.
In data modeling with MongoDB, there are three design phases, workload identification, relationship identification, and the application of design patterns.
Here, we'll cover phase three, applying schema design patterns. We'll apply the phase to an example application, a bookstore app that sells audiobooks, ebooks, as well as printed books.
The foundational schema design patterns that you learn here will give you a blueprint for modeling your data in MongoDB.
Before we go any further, let's define the word pattern.
Patterns are guidelines for responding to common problems.
By definition, patterns can be reused. And while they are not full solutions on their own, a pattern can be a starting point to help you reach your full solution.
You can run through different patterns that are relevant to your use case, then adapt and apply them to meet the specific needs of your app.
Patterns can be combined to form powerful solutions.
The patterns you'll learn here will serve as a recipe book for effectively transforming and optimizing your schemas with MongoDB.
In this skill badge, you'll learn how to use the inheritance pattern, computed pattern, and extended reference pattern.
Understanding when and how to use these patterns will demystify data modeling in MongoDB and make the process more predictable.
Taking the time to incorporate patterns in our designs can be one of the most critical steps to successfully deploying your database with MongoDB.
But sometimes, despite our best efforts to design our database effectively, we end up with problematic data models. When these problems are rooted in bad schema design practices, they are called schema design anti patterns.
An anti pattern is a commonly adopted solution to a recurring problem that leads to negative consequences, like poor application performance.
In other words, an anti pattern is an approach that might seem like a good idea at the time, but leads to problems in the long run.
Whether things didn't go according to plan or you simply made a mistake, There are steps you can take to mitigate these anti patterns. For this skill badge, we will cover common schema design anti patterns that developers encounter.
This will help you recognize and avoid them.
First, we'll show you how to identify the unbounded array anti pattern, which occurs when a document contains an array with the potential to grow very large.
Next, you'll learn about the bloated documents anti pattern. This can happen when we store data together that isn't accessed together by our application.
Understanding how to identify these anti patterns and what to do if you encounter one will help you improve performance, scalability, and affordability of your MongoDB database. You'll have plenty of opportunities to practice what you learned by completing labs that present real world scenarios.
This way, you'll build your knowledge and get comfortable with the software at the same time. When you're finished, you'll be ready to put your new skills to the test. You can earn a badge to demonstrate your knowledge. To earn your badge, simply complete the content in this course and then take the short test at the end. After passing the test, you'll receive an official Qudly badge via the email you provided. Be sure to share your badge on LinkedIn to show off your new skills.
