20 Essential MySQL Optimization Tips for Better Database

Whether you're building a new application or maintaining an existing one, database performance is crucial for delivering a great user experience. MySQL is one of the most popular relational databases, but it requires careful optimization to perform at its best.

In this guide, I'll share 20 practical tips to help you design better schemas, create efficient indexes, and write faster queries.

Schema Design Best Practices

1. Choose the Right Data Types

Smaller data types are almost always better. They consume less disk space, memory, and CPU cycles. MySQL has to process smaller amounts of data, which naturally leads to better performance.

2. Optimize String Columns

When working with strings, use VARCHAR instead of CHAR unless your data is truly fixed length. VARCHAR only uses the space needed for the actual string plus one or two bytes to store the length, while CHAR always uses the full declared length.

3. Use INT for Large Numbers

For numbers that exceed TINYINT, SMALLINT, or MEDIUMINT, jump straight to INT. The performance difference is negligible, and you'll avoid the hassle of altering your tables later as your data grows.

4. Avoid ENUM Types

While ENUM might seem convenient, changing its members requires an expensive ALTER TABLE operation. Consider using a lookup table with a foreign key constraint instead. As a rule of thumb, lookup tables make sense when you have at least a hundred records.

5. Use Numeric Primary Keys

Prefer numeric types over strings for primary keys. Looking up records by numeric primary keys is significantly faster than string based lookups.

6. Avoid BLOBs When Possible

Binary Large Objects (BLOBs) increase database size and negatively impact performance. Store files on disk whenever you can and keep file paths in your database instead.

Table Structure Optimization

7. Consider Vertical Partitioning

If a table has many columns, especially ones that aren't frequently accessed, consider splitting it into two related tables using a one to one relationship. This is called vertical partitioning.

For example, if you have a customers table with address columns that are rarely queried, split it into customers and customer_addresses tables.

8. Strategic Denormalization

Normalization is important, but if your queries require many joins due to data fragmentation, denormalizing might help. This involves duplicating a column from one table in another to reduce the number of required joins.

Use this technique judiciously—denormalization trades data consistency for query performance.

9. Create Summary Tables

For expensive queries that run frequently, consider creating summary or cache tables. For example, if fetching forum statistics (like post counts per forum) is slow, create a forums_summary table.

You can use MySQL events to refresh this data periodically, or triggers to update counts whenever relevant changes occur.

Index Optimization Strategies

10. Identify and Fix Full Table Scans

Full table scans are a major cause of slow queries. Use the EXPLAIN statement and look for queries where type = ALL. These indicate full table scans that could benefit from proper indexing.

EXPLAIN SELECT * FROM users WHERE email = '[e-mail]';

11. Index Your WHERE Clauses First

When designing indexes, prioritize columns that appear in your WHERE clauses. These help MySQL narrow down searches. Next, consider columns in ORDER BY clauses—if they're in the index, MySQL can return sorted data without a separate sort operation (filesort).

Finally, adding SELECT clause columns creates a covering index, allowing MySQL to satisfy the entire query from the index without accessing table data.

12. Prefer Composite Indexes

Instead of creating multiple single column indexes, create composite indexes that cover multiple columns. They're more efficient for queries that filter on multiple columns.

-- Better: One composite index
CREATE INDEX idx_user_status_date ON orders(user_id, status, order_date);

-- Less optimal: Multiple single column indexes
CREATE INDEX idx_user ON orders(user_id);
CREATE INDEX idx_status ON orders(status);
CREATE INDEX idx_date ON orders(order_date);

13. Order Matters in Composite Indexes

The order of columns in a composite index is crucial. Put the most frequently used columns first, and prioritize columns with higher cardinality (more unique values). However, always analyze your actual query patterns.

14. Clean Up Your Indexes

Remove duplicate, redundant, and unused indexes:

• Duplicate indexes: Same columns in the same order
• Redundant indexes: Can be replaced by existing indexes (e.g., if you have an index on (A, B), an index on just (A) is redundant)
• Unused indexes: Track index usage and remove what's not being used

15. Analyze Before Creating

Before creating a new index, analyze your existing indexes. You might already have one that serves your needs, or you might need to modify an existing index rather than add a new one.

Query Optimization Techniques

16. Isolate Columns in Queries

Write your queries so that columns appear by themselves, not inside expressions. This allows MySQL to use indexes effectively.

-- Bad: Can't use index on created_at
SELECT * FROM orders WHERE YEAR(created_at) = 2024;

-- Good: Can use index
SELECT * FROM orders WHERE created_at >= '2024-01-01' 
  AND created_at < '2025-01-01';

17. Avoid SELECT *

SELECT * ignores your carefully designed indexes and returns unnecessary columns. Be explicit about which columns you need your database will thank you.

-- Bad
SELECT * FROM users WHERE user_id = 123;

-- Good
SELECT user_id, name, email FROM users WHERE user_id = 123;

18. Use LIMIT for Large Result Sets

Always return only the rows you actually need. Use the LIMIT clause to prevent fetching thousands of unnecessary rows.

SELECT name, email FROM users ORDER BY created_at DESC LIMIT 10;

19. Avoid Leading Wildcards in LIKE

Patterns like LIKE '%name' with a leading wildcard prevent MySQL from using indexes. If you need this functionality frequently, consider full-text search or specialized search solutions.

-- Can't use index
SELECT * FROM products WHERE name LIKE '%phone%';

-- Can use index
SELECT * FROM products WHERE name LIKE 'phone%';

20. Optimize OR Queries with UNION

If you have a slow query using the OR operator, consider breaking it into two separate queries that can utilize different indexes, then combine the results with UNION.

-- Potentially slow
SELECT * FROM products 
WHERE category_id = 5 OR featured = 1;

-- Potentially faster
SELECT * FROM products WHERE category_id = 5
UNION
SELECT * FROM products WHERE featured = 1;

Conclusion

MySQL optimization is an ongoing process that requires understanding your data, query patterns, and performance bottlenecks. Start with schema design, create strategic indexes, and write efficient queries. Monitor your database regularly using tools like EXPLAIN, slow query logs, and performance schema.

Remember that optimization is about trade-offs what works for one application might not work for another. Always test changes in a development environment before applying them to production.

Further Reading

For deeper dives into MySQL optimization, I recommend:

• High Performance MySQL: Optimization, Backups, and Replication by Baron Schwartz
• Relational Database Index Design and the Optimizers by Tapio Lahdenmaki

Happy optimizing! 🚀