Advanced Schema Design Patterns and Anti-patterns
Power up your MongoDB skills by learning advanced schema design patterns like approximation and schema versioning. Learn to manage schema lifecycle changes without downtime and identify performance-impacting anti-patterns, ensuring efficient and scalable database solutions.
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Upon completion of the Advanced Schema Design Patterns and Anti-patterns skill check, you will earn a Credly Badge that you are able to share with your network. |
Learning Objectives
Apply Advanced Schema Design Patterns
Implement advanced schema design patterns, such as the approximation pattern and schema versioning pattern.
Manage Database Schema Lifecycle
Update your schema and migrate your application to the new schema without any downtime.
Identify Advanced Anti-patterns
Identify and avoid anti-patterns that impact performance such as a massive number of collections, unnecessary indexes, data normalization, and case sensitivity.
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.
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.
Sarah Evans | Senior Curriculum Engineer
Sarah is a Senior Curriculum Engineer on the Curriculum team at MongoDB. Prior to MongoDB, she taught and developed curricula for developer bootcamps. Sarah has a MAT degree from Columbia University Teachers College and studied Software Engineering at Flatiron School in Chicago, IL.
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).
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.
Manuel and I are excited to be your guides to the skill badge on advanced schema design patterns and anti patterns.
Here, you will learn essential skills to harness the full power of MongoDB to design applications that are not just functional, but also highly efficient and performant.
By mastering advanced schema patterns and understanding common anti patterns, you'll be equipped to build your apps on top of a database optimized for speed, scalability, and flexibility.
Furthermore, as your application evolves, changes will occur, and you will need to update your schema.
While this can be a dreadful developer experience and cause downtime in a traditional relational database, MongoDB simplifies it, and you will learn how to do it. The knowledge you'll gain from this skill badge will make your applications resilient and future proof and equip you with the tools you need to adapt your applications as they evolve and get larger. We begin our journey by examining key advanced schema design patterns, starting with the approximation pattern.
This pattern allows you to reduce resource consumption by sacrificing minimal precision in scenarios where performance is more important than accuracy.
It helps optimize your data handling processes, making your application faster and more responsive.
Next, you'll learn about the schema versioning pattern, an essential strategy for managing changes to your MongoDB deployment without downtime.
Using MongoDB's flexible document model makes evolving your schema over time much easier, and you'll learn how. Building on these patterns, we'll explore schema evolution and schema migration, processes that are integral to managing your database schema's life cycle. Understanding these concepts will help you design a robust database schema.
However, even with the best intentions, we can sometimes fall into traps of poor design practices, leading to what we call schema design anti patterns.
An anti pattern is a commonly adopted solution to our 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 that you can take to mitigate these anti patterns.
After that, you'll learn how to identify the massive number of collections anti pattern. In this case, we may have exceeded the recommended number of collections for a cluster tier, a replica set, or a charter cluster.
Then we'll look at the unnecessary indexes anti pattern. Here, we may find indexes that are either redundant or rarely used. These indexes take up space and impact performance without any benefits.
Next, you'll learn about the data normalization and pattern or what happens when we separate data that is frequently accessed together.
Finally, you'll learn about the case sensitivity antipattern. This happens when we want results that are not case sensitive but misconfigured queries and indexes lead to unexpected results and poor performance. Understanding how to identify these antipatterns and what to do if you encounter one will help improve performance, scalability, and affordability of your MongoDB database.
Each lesson in this skill badge will introduce concepts through detailed videos and hands on labs. These exercises ensure that by the end, you're well equipped to understand the theory and apply what you've learned directly to your projects.
At this point, you'll be ready to take our short assessment and demonstrate your knowledge.
After passing the test, you will receive an official Credly badge to share on LinkedIn to show off your newly acquired knowledge and skills.
