Introduction
Schema design patterns help you organize data effectively, but equally important is recognizing common mistakes that undermine performance, increase costs, and create maintenance burdens. These mistakes, called anti-patterns, often emerge when developers apply relational database thinking to a document database or skip the access-pattern analysis step during design.
Suppose your e-commerce company's Azure DocumentDB application is experiencing growing pains. User profile documents are bloating because sign in history arrays grows without any limit. The development team created separate collections for each tenant in a multitenant feature. After some time, there are hundreds of collections to manage. The database has over 20 indexes per collection. Writes are becoming slow. Order pages make four separate queries to assemble data that could come from a single document. And customer support tickets keep arriving about users who can't sign in because they typed their email address in a different capitalization than what's stored in the database, such as Andre@contoso.com versus andre@contoso.com. Each of these problems stems from a schema design anti-pattern.
In this module, you learn to recognize five common anti-patterns and apply targeted fixes. You start with structural issues: unbounded arrays and collection sprawl. Next, you identify performance trade-off mistakes: unnecessary indexes and over-normalization. Finally, you address data quality problems caused by case sensitivity. For each anti-pattern, you examine the symptoms, understand why it causes problems, and apply a specific solution.
By the end of this module, you're able to identify schema design anti-patterns in your Azure DocumentDB application and apply the right fix to resolve them.