6-3-1 Indexing Explained

Key Concepts

• Indexing
• Types of Indexes
• B-Tree Index
• Hash Index
• Clustered Index
• Non-Clustered Index

Indexing

Indexing is a database optimization technique that improves the speed of data retrieval operations on database tables. It works by creating a data structure that allows the database to locate data quickly without scanning the entire table.

Example: Imagine you have a book with thousands of pages. Without an index, finding a specific topic would require reading through every page. With an index, you can quickly locate the topic by looking up the page number in the index.

Analogy: Think of an index in a book. Just as the index helps you find information quickly, a database index helps the database find data quickly.

Types of Indexes

There are several types of indexes, each designed for different purposes and data access patterns. The most common types include B-Tree, Hash, Clustered, and Non-Clustered indexes.

Example: A database might use different types of indexes for different tables depending on the query patterns. For example, a B-Tree index might be used for range queries, while a Hash index might be used for exact match queries.

Analogy: Think of different tools in a toolbox. Each tool is designed for a specific task, and using the right tool makes the job easier and faster.

B-Tree Index

A B-Tree (Balanced Tree) index is a self-balancing tree data structure that maintains sorted data and allows searches, sequential access, insertions, and deletions in logarithmic time. It is commonly used for range queries and sorting operations.

Example: A B-Tree index might be used in a sales database to quickly find all transactions within a specific date range.

Analogy: Think of a B-Tree as a well-organized filing cabinet. Each drawer (node) contains a sorted set of files (data), and you can quickly find the file you need by following the labels (keys).

Hash Index

A Hash index uses a hash function to compute an index into an array of buckets or slots, from which the desired value can be found. It is optimized for exact match queries but does not support range queries or sorting.

Example: A Hash index might be used in a user authentication system to quickly find a user record by their username.

Analogy: Think of a Hash index as a phonebook where each name (key) is hashed to a specific page number. You can quickly find the phone number (value) by looking up the page number.

Clustered Index

A Clustered index determines the physical order of data in the table. There can be only one clustered index per table, and it is typically created on the primary key. It speeds up data retrieval but slows down insertions and updates.

Example: A clustered index might be used in a customer database where the primary key is the customer ID. The data is physically stored in the order of customer IDs.

Analogy: Think of a clustered index as a sorted deck of cards. The cards are arranged in a specific order (physical order), and you can quickly find a card by its position.

Non-Clustered Index

A Non-Clustered index is a separate structure from the data rows. It contains a copy of the indexed columns and a pointer to the actual data rows. It speeds up data retrieval but requires additional storage.

Example: A non-clustered index might be used in an employee database to quickly find employees by their department.

Analogy: Think of a non-clustered index as an index card file. Each card contains a summary (indexed columns) and a reference (pointer) to the full document (data row).