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MySQL Indexes and Performance

📚 Lesson 4 of 15 ⏱️ 40 min

MySQL Indexes and Performance

40 min

Indexes are critical for MySQL performance, enabling fast data retrieval without scanning entire tables. An index is a data structure that stores a sorted copy of selected columns, allowing MySQL to quickly locate rows matching query conditions. Without indexes, MySQL must perform full table scans, which become prohibitively slow as tables grow. Understanding indexing is essential for building performant MySQL applications.

MySQL supports various index types including B-tree indexes (default, most common), hash indexes (MEMORY storage engine only), full-text indexes (for text search), and spatial indexes (for geographic data). B-tree indexes support equality and range queries efficiently and work with most storage engines. Understanding index types enables you to choose appropriate indexes for your use cases.

Single-column indexes are created on individual columns and are useful for queries filtering or sorting by that column. Composite indexes are created on multiple columns and support queries using the leftmost prefix of the indexed columns. For example, an index on (category, price) can be used for queries on category alone or category and price together, but not price alone. Understanding composite index column order enables optimal index design.

Indexes should be created on columns frequently used in WHERE clauses, JOIN conditions, and ORDER BY clauses. However, indexes add overhead to INSERT, UPDATE, and DELETE operations, so balance is important. Too many indexes can slow down writes, while too few indexes can slow down reads. Understanding when to create indexes enables optimal performance.

EXPLAIN analyzes query execution plans, showing which indexes are used, join types, and estimated row counts. EXPLAIN helps identify missing indexes, inefficient query plans, and optimization opportunities. Regular query analysis is essential for maintaining performance as data grows. Understanding EXPLAIN output enables you to optimize queries effectively.

Additional performance considerations include using covering indexes (indexes that contain all columns needed for a query), avoiding index fragmentation, monitoring index usage, and understanding index cardinality. Best practices include creating indexes based on query patterns, regularly analyzing slow queries, and removing unused indexes. Understanding these considerations enables maintaining optimal database performance.

Key Concepts

  • Indexes provide fast access to data, avoiding full table scans.
  • B-tree indexes are the default and support most query types.
  • Composite indexes support queries on multiple columns.
  • EXPLAIN shows query execution plans and index usage.
  • Indexes improve reads but add overhead to writes.

Learning Objectives

Master

  • Creating single-column and composite indexes
  • Understanding when to create indexes
  • Using EXPLAIN to analyze query performance
  • Optimizing queries with appropriate indexes

Develop

  • Understanding database performance optimization
  • Designing efficient database schemas
  • Analyzing and improving query performance

Tips

  • Create indexes on frequently queried columns: CREATE INDEX idx_email ON users(email).
  • Use composite indexes for multi-column queries: CREATE INDEX idx_cat_price ON products(category, price).
  • Analyze queries: EXPLAIN SELECT * FROM users WHERE email = 'test@example.com'.
  • Check index usage: SHOW INDEX FROM table_name to see all indexes.

Common Pitfalls

  • Creating too many indexes, slowing down write operations.
  • Not creating indexes on queried columns, causing slow queries.
  • Not understanding composite index column order, creating inefficient indexes.
  • Not analyzing queries, missing optimization opportunities.

Summary

  • Indexes are essential for MySQL query performance.
  • Understanding index types and when to use them is crucial.
  • EXPLAIN helps identify optimization opportunities.
  • Balancing index benefits with write performance is important.

Exercise

Create and analyze indexes for performance optimization.

-- Create indexes for better performance
CREATE INDEX idx_user_email ON users(email);
CREATE INDEX idx_order_date ON orders(order_date);
CREATE INDEX idx_product_category_price ON products(category, price);

-- Analyze table performance
ANALYZE TABLE users;
ANALYZE TABLE products;
ANALYZE TABLE orders;

-- Check index usage
SHOW INDEX FROM users;
SHOW INDEX FROM products;

-- Use EXPLAIN to analyze query performance
EXPLAIN SELECT * FROM users WHERE email = 'john@example.com';

-- Create composite index for complex queries
CREATE INDEX idx_user_orders ON orders(user_id, order_date, status);

-- Full-text search index
CREATE FULLTEXT INDEX idx_product_search ON products(name, description);

-- Full-text search query
SELECT * FROM products 
WHERE MATCH(name, description) AGAINST('laptop computer' IN NATURAL LANGUAGE MODE);

Exercise Tips

  • Use ANALYZE TABLE to update index statistics: ANALYZE TABLE users;
  • Create full-text indexes for text search: CREATE FULLTEXT INDEX idx_search ON table(column);
  • Use covering indexes: include all queried columns in index.
  • Monitor slow queries: enable slow query log to identify optimization opportunities.

Code Editor

Output