Lesson 30 of the SQL Optimization Course: Optimizing MySQL Performance with Table Denormalization Strategies

For relational databases, the design of tables and SQL is written are particularly crucial. It wouldn’t be an exaggeration to say that they account for 90% of performance. So this time, specifically targeting these two major knowledge areas, we’ll conduct a detailed analysis for you, peeling back the layers.
This Series uses plain and understandable language and selects a large number of examples to elaborate on the subtleties for you.
🧑💻 Target audience:
We will use MySQL as the demonstration database.
In the previous article, we explored normalized database design in MySQL. While normalization eliminates redundancy and enhances data integrity, it introduces challenges like increased table joins and reduced query efficiency. This article introduces denormalization strategies to balance these trade-offs.
Normalization reduces redundancy but increases table count (e.g., 3NF may require more tables than 2NF). Excessive joins degrade performance, especially for complex queries. Denormalization intentionally introduces controlled redundancy to simplify queries and improve speed. Examples include:
Consider five normalized tables: employees
, departments
, salaries
, employee_salary_links
, and employee_dept_links
. Four common query patterns were identified:
Each query required multiple joins, leading to suboptimal performance.
To optimize these queries, we added redundant fields to the employees
table:
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After denormalization:
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While normalization remains essential for data integrity, strategic denormalization can significantly improve query efficiency. By balancing normalization principles with denormalization techniques, developers can achieve optimal performance for real-world applications.
👋 See you in the next lesson.
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