Denormalization and Performance
Learn what denormalization is and its purpose in databases.
We'll cover the following...
Imagine our OnlineStore is hosting a massive flash sale. Millions of users are browsing product pages simultaneously. For each page view, the database needs to fetch the product’s name, its price, its stock level, and the category it belongs to. In our current, nicely normalized database, this means the system has to quickly join the Products table with the Categories table for every single request. When this happens millions of times a minute, even a simple join can slow the entire website to a crawl, leading to frustrated customers and lost sales. This is where a technique that seems to break the rules we've just learned can actually save the day.
In this lesson, we’ll explore the concept of denormalization. It’s a powerful technique used to optimize database performance, but it comes with its own set of trade-offs.
By the end of this lesson, we will be able to:
Understand what denormalization is and its primary purpose.
Recognize the benefits and risks associated with denormalization.
Identify common denormalization techniques.
Determine when it is appropriate to denormalize a database schema.
Let’s dive in and learn how to strategically bend the rules of normalization for a faster, more responsive database.
Denormalization
We’ve worked hard to normalize our databases, carefully organizing data into separate tables to reduce redundancy and ensure data integrity.
Normalization forms the foundation of good relational database design. However, this ideal structure sometimes comes with a trade-off: performance. Highly normalized schemas often require complex queries with multiple joins ...