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Grokking the Fundamentals of System Design

Indexing and Query Optimization in Database Systems

Learn how indexing and query optimization improve database performance in scalable systems.

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Slow data retrieval can cripple even the most brilliantly designed application, leading to frustrated users and lost opportunities.

In any system where data is a core asset, the speed at which we can find and present that information is a critical factor for success. This is a central challenge in System Design, especially as systems scale and datasets grow exponentially.

Effective data management is built on two crucial concepts: indexing and query optimization.

Understanding how to structure data for rapid access is fundamental to building high-performance, scalable systems. This lesson examines the tools and strategies that enhance database performance and optimize query efficiency. To begin, let’s look at the foundation: index structures.

Index structures

An index in a database is a data structure that improves the speed of data retrieval from a database.

It works much like the index in the back of a book. Instead of reading the entire book to find a specific topic, we can look it up in the index, which points us directly to the correct page number. Similarly, a database index allows the query engine to find rows without scanning the entire table.

To see how indexing speeds up lookups, here’s the basic flow of a query that uses an index.

Indexes map query keys to specific rows in a data table, enabling faster lookups
Indexes map query keys to specific rows in a data table, enabling faster lookups

Analogy: Imagine a massive library without a card catalog. To find a specific book, we would have to check every shelf. An index acts as that catalog, organizing references to data so we can locate what we need quickly.

Databases employ various indexing strategies, each with its own trade-offs. The most common ones are:

  • Tree-based indexes

  • Hash indexes

  • Full-text search indexes

Let’s explore each in detail.

Tree-based indexes

In tree-based indexes, data is stored in a sorted tree structure, like a B-tree or B+ tree. These indexes keep records in order, making them great for range queries, sorting, and prefix lookups (e.g., finding all users with ages between 20 and 30). They balance fast lookups with efficient inserts and updates.

The B-trees and B+ trees are the most popular implementations of tree-based indexes, as follows:

  • B-tree: A ...