MySQL for Developers

Understanding B+ Trees: the data structure behind MySQL indexes

If you've spent any amount of time working with MySQL, you've likely heard about B+ trees. This data structure is the underlying foundation for most indexes in the database management system. Despite being a complex concept, it's important for any application developer to understand how B+ trees work to efficiently manage large amounts of data.

In this video, we'll provide a cursory overview of B+ trees and how they work with MySQL indexes. By the end, you'll have a basic understanding of how a MySQL index functions and why it's important.

Why add indexes?

So why add an index in the first place? Put simply, indexes speed up data retrieval. Without an index, MySQL would have to read through the entire table to find a specific value. That may be manageable for small tables, but as the number of rows increases, it becomes an impractical solution.

Anatomy of a B+ tree

This data structure resembles a tree structure with nodes, and it functions as a map for accessing specific data in a table. To demonstrate how B+ trees work, let's visualize an index on the people table. You can see that it looks like a tree, with the root node at the top and the leaf nodes at the bottom.

The root node isn't very relevant to us right now, so we'll shift our focus to the leaf nodes. Each name in the table has its own corresponding node in the B+ tree. The leaf nodes contain the data that we have indexed, in this case, first names.

Searching with a B+ tree

Let's assume we want to search for the name "Suzanne." We'll pretend to be the database and follow our searching algorithm.

Starting at the root node, we'll compare "Simon" to "Suzanne." As "Suzanne" is later in the alphabet than "Simon," we'll head down the right side of the tree. We'll then compare "Tyler" to "Suzanne." Since "Suzanne" comes before "Tyler," we'll head down the left side. We'll find the node with the name "Suzanne" and compare it to itself. Since the values match, we'll head down the right side of the tree and land where we want to be.

This algorithm allows us to skip over many of the leaf nodes at the bottom and only look at a few nodes. Without an index, we'd have to read through every single row to find the name "Suzanne."

How indexes make MySQL queries faster

So how do B+ trees make things faster? They build up a secondary data structure that optimizes searching for specific values. By creating an index on the first name field, we've optimized this secondary data structure for searching first names.

Final thoughts

B+ trees may seem complex, but they're vital to database management systems like MySQL. By adding an index to a specific field, developers can speed up data retrieval significantly. Now that you have a basic understanding of B+ trees, you'll be better equipped to optimize the performance of your databases.

About this lesson

B+ trees are the underlying data structure of most indexes in MySQL. We won't go into too much detail, but it is important to understand how they work.

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