Indexes are essential tools in MySQL that significantly enhance query performance. By allowing the database to locate data without scanning every row, indexes can dramatically speed up data retrieval. However, they also introduce certain trade-offs. This article delves into what MySQL indexes are, their internal workings, advantages and disadvantages, and best practices for their use.

What Is an Index in MySQL?

An index in MySQL is a data structure that improves the speed of data retrieval operations on a database table. It functions similarly to an index in a book, enabling quick location of specific data without scanning the entire table. Indexes are particularly beneficial for large datasets where performance is critical.

All Types of Indexes in MySQL

Index Type Storage Engine Use Case / Purpose Limitations
Primary Key All (InnoDB, MyISAM) Unique identifier for each row. Only one allowed per table. Cannot contain NULLs.
Unique Index All Ensures all values in the indexed column(s) are unique. Allows one NULL (per MySQL version rules).
B-Tree Index InnoDB, MyISAM Default index for most queries involving equality, range, sorting. Slower on large insert/update/delete if overused.
Hash Index MEMORY Fast lookup for exact matches (=) in MEMORY engine. Not usable for range queries or sorting. Only MEMORY supports it.
Full-Text Index InnoDB, MyISAM Efficient natural language text searches. Only works on CHAR, VARCHAR, and TEXT. Requires MATCH ... AGAINST.
Spatial Index MyISAM, InnoDB (8.0+) Geospatial queries on geometry types. Only works on geometry columns. InnoDB support requires SRID.
Composite Index All Multi-column filtering and sorting. Column order matters (leftmost prefix rule).
Functional Index InnoDB (8.0.13+) Index on expressions or functions (e.g., LOWER(column)). Requires MySQL 8.0.13+.
Descending Index InnoDB (8.0+) Optimize queries with ORDER BY col DESC. Requires MySQL 8.0+. Older versions only store ascending.
Partial Index InnoDB (on prefix) Index only part of large text fields (e.g., content(100)). Only supported on string types. Not full content searchable.
Covering Index All Index that includes all columns in SELECT, enabling index-only scan. Must include all selected columns. May use more disk space.
Invisible Index InnoDB (8.0+) Exists but hidden from the optimizer. Useful for testing/removal. Ignored in query plans unless explicitly hinted.
Foreign Key Index InnoDB Enforces referential integrity. Requires index on the referenced column.

How MySQL Indexes Work Under the Hood (The Most Used)

B-Tree Indexes

The most common type of index in MySQL is the B-Tree index. This structure maintains a balanced tree where each node contains keys and pointers to child nodes, allowing efficient data retrieval. When a query is executed, MySQL traverses the B-Tree to quickly locate the desired data, reducing the number of disk reads required.

B-Tree Indexes

Composite Indexes

A composite index (also known as a multi-column index) is an index that includes two or more columns in a specific order. It’s used to speed up queries that filter or sort by multiple columns together.

Think of it like the index at the back of a textbook that sorts by topic and subtopic. You can quickly find what you need if you follow the order the index was built in.

Composite indexes are typically implemented as B-Tree indexes, where each entry in the tree stores a tuple of column values.

CREATE INDEX idx_name ON users (last_name, first_name);

Composite Index

Functional Indexes

Introduced in MySQL 8.0.13, functional indexes allow indexing based on expressions or functions applied to columns. Under the hood, MySQL creates a hidden generated column to store the result of the expression and then indexes this column.

CREATE INDEX idx_lower_email ON users ((LOWER(email)));

Functional Index

Pros and Cons of Using Indexes

Pros

  • Faster Query Performance
  • Efficient Sorting and Filtering
  • Support for Uniqueness Constraints

Cons

  • Increased Storage Requirements
  • Slower Write Operations
  • Complexity in Management

Best Practices for Using Indexes

  1. Index columns used in WHERE clauses
  2. Use composite indexes wisely and match query order
  3. Avoid over-indexing
  4. Leverage EXPLAIN for query analysis
  5. Consider indexing expressions
  6. Choose optimal data types
  7. Monitor index usage with performance schema
  8. Use covering indexes
  9. Leverage partial indexes for long strings
  10. Understand and apply cardinality

Advanced Index Optimization Techniques

Index Hint Optimization 

SELECT * FROM customers USE INDEX (idx_lastname) WHERE lastname = 'Smith';

Invisible Indexes 

ALTER TABLE users ALTER INDEX idx_email INVISIBLE;

Index Merging 

EXPLAIN SELECT * FROM table WHERE col1 = 'A' OR col2 = 'B';

In summary, indexes are powerful tools in MySQL that, when used appropriately, can greatly enhance query performance. Understanding their internal workings and adhering to best practices ensures that you reap the benefits of faster data retrieval while mitigating potential downsides.