MySQL Indexing Best Practices: A Comprehensive Guide

Have you ever launched a query that felt like it was taking forever to complete? I’ve been there. When MySQL databases grow, performance can take a serious hit—and that’s when proper indexing becomes your secret weapon. After diving deep into MySQL optimization over the years, I’ve compiled these absolutely essential MySQL indexing best practices that will transform your database performance. Let me share what I’ve learned through countless hours of testing and implementation.

Why Indexing Is Critical for MySQL Performance

Think of indexes as the table of contents in a massive book. Without them, MySQL has to flip through every single page (full table scan) to find what you’re looking for. With proper indexing, it jumps straight to the right section, making everything lightning fast.

Indexes create data structures that allow MySQL to locate rows quickly without examining the entire table. This dramatically improves query execution time, especially for large datasets.

1. Don’t Index Every Column (Seriously, Don’t)

I’ve seen developers go index-crazy when they face performance issues, thinking “more indexes = better performance.” That’s completely wrong.

Here’s why you should never index everything:

• Storage Overhead: Each index requires additional disk space and memory. On large tables, this adds up fast.
• Write Performance Hit: Every time you INSERT, UPDATE, or DELETE data, MySQL must update all affected indexes. More indexes mean slower write operations.
• Optimizer Confusion: Too many indexes can confuse MySQL’s query optimizer, potentially leading to poor execution plans.

Instead, focus on indexing columns that appear in WHERE clauses, JOIN conditions, and ORDER BY statements—places where performance gains matter most.

2. Choose Between Multiple Indexes or Multi-Column Indexes

This decision makes a massive difference in query performance:

• For AND Conditions: Create a multi-column (composite) index.

/* For queries like this */ 
SELECT * FROM customers WHERE status = 'active' AND country = 'Canada'; 
/* Create a composite index */ 
CREATE INDEX idx_status_country ON customers(status, country);Code language: JavaScript (javascript)

• For OR Conditions: Create separate indexes for each column.

/* For queries like this */ 
SELECT * FROM customers WHERE status = 'active' OR country = 'Canada'; 
/* Create individual indexes */ 
CREATE INDEX idx_status ON customers(status); CREATE INDEX idx_country ON customers(country);Code language: JavaScript (javascript)

Getting this wrong can mean the difference between millisecond responses and multi-second queries.

3. Column Order Matters Tremendously in Composite Indexes

The order of columns in a composite index is absolutely critical. MySQL can only use a composite index efficiently if the query uses a leftmost prefix of the indexed columns.

For example, if you have INDEX(a,b,c):

• It works for conditions on a
• It works for conditions on a AND b
• It works for conditions on a AND b AND c
• It does NOT work efficiently for just b or just c

Best practice: Place higher cardinality columns (those with more unique values) first in your index. This dramatically improves selectivity, allowing MySQL to filter out more rows earlier in the execution process.

/* Good order (assuming user_id has higher cardinality than status) */
CREATE INDEX idx_user_status ON orders(user_id, status);

/* Less effective order */
CREATE INDEX idx_status_user ON orders(status, user_id);Code language: JavaScript (javascript)

💡: Explore more MySQL Best Practices!

4. Verify Index Usage with EXPLAIN

Never assume MySQL is using your indexes! Always check with the EXPLAIN command:

EXPLAIN SELECT * FROM customers 
WHERE last_name = 'Smith' AND city = 'New York';Code language: JavaScript (javascript)

Pay special attention to these fields in the output:

• key: Shows which index is being used (if any)
• rows: Estimated number of rows MySQL will examine
• Extra: Look for “Using index” (good) or “Using filesort”/”Using temporary” (potential problems)

If you see “NULL” in the key column, MySQL is doing a full table scan—a red flag for large tables.

5. Make Columns NOT NULL When Possible

This seemingly minor detail can have significant performance implications. MySQL treats NULL values specially in indexes, requiring additional checking during queries.

Compare these two approaches:

/* Less efficient */
CREATE TABLE customers (
    id INT PRIMARY KEY,
    email VARCHAR(100) NULL,
    INDEX (email)
);

/* More efficient */
CREATE TABLE customers (
    id INT PRIMARY KEY,
    email VARCHAR(100) NOT NULL DEFAULT '',
    INDEX (email)
);Code language: PHP (php)

The second approach avoids NULL checks during index operations, improving performance. When columns genuinely need NULL values, keep them—but don’t default to nullable columns without good reason.

6. Leverage Covering Indexes for Blazing Speed

A covering index is my absolute favorite MySQL optimization trick. It contains all columns needed for a query, allowing MySQL to satisfy the query entirely from the index without touching the table data.

/* Query that can benefit from a covering index */
SELECT user_id, created_at FROM orders WHERE status = 'completed';

/* Create a covering index */
CREATE INDEX idx_status_cover ON orders(status, user_id, created_at);Code language: JavaScript (javascript)

When MySQL uses a covering index, you’ll see “Using index” in the EXPLAIN output’s Extra column. These queries can be 2-10x faster than regular indexed queries.

7. Don’t Index Low-Selectivity Columns

Columns with few unique values (low cardinality) often don’t benefit from indexing. Examples include:

• Boolean columns (true/false)
• Status columns with few possible values
• Gender columns

MySQL will often ignore indexes on these columns anyway, as full table scans might be faster. Exception: If these low-cardinality columns are always used with other, more selective columns in queries, include them in composite indexes.

8. Force Index Usage When Needed

Sometimes MySQL’s optimizer makes wrong choices. In these rare cases, you can suggest or force index usage:

/* Suggest an index */
SELECT * FROM products USE INDEX(idx_category_price)
WHERE category_id = 5 AND price > 100;

/* Force an index */
SELECT * FROM products FORCE INDEX(idx_category_price)
WHERE category_id = 5 AND price > 100;Code language: PHP (php)

Use FORCE INDEX sparingly—it’s usually better to trust MySQL’s optimizer or redesign your indexes.

9. Consider Partial Indexes (MySQL 8.0+)

MySQL 8.0 introduced functional indexes and more powerful index filtering. This lets you index only specific rows that matter for your queries:

/* Index only active products */
CREATE INDEX idx_price_active ON products(price) WHERE status = 'active';Code language: JavaScript (javascript)

This creates smaller, more efficient indexes focused on the data you actually query.

10. Beware of Index Fragmentation

Over time, as you update and delete data, indexes become fragmented, reducing their efficiency. Regular maintenance keeps indexes optimized:

/* Analyze table to update statistics */
ANALYZE TABLE customers;

/* Optimize table to rebuild indexes */
OPTIMIZE TABLE customers;Code language: JavaScript (javascript)

For InnoDB tables, consider scheduling these operations during low-traffic periods.

11. Use Generated Columns for Complex Indexing

If you need to index expressions or function results, MySQL 8.0+ allows generated columns that can be indexed:

/* Add generated column and index it */
ALTER TABLE users 
ADD COLUMN email_domain VARCHAR(255) 
GENERATED ALWAYS AS (SUBSTRING_INDEX(email, '@', -1)) STORED,
ADD INDEX idx_email_domain (email_domain);

/* Now you can query efficiently by domain */
SELECT * FROM users WHERE email_domain = 'gmail.com';Code language: PHP (php)

This approach is much more efficient than using function-based WHERE clauses that can’t use indexes.

12. Experiment and Measure for Your Specific Workload

Every database is unique. A perfect indexing strategy depends on:

• Your actual query patterns
• Your data distribution
• Your read/write ratio
• Your hardware capabilities

Use these tools to measure real-world performance:

• MySQL’s Performance Schema
• Slow query log analysis
• Application-level metrics

Don’t blindly follow indexing rules—test different approaches with your actual workload and measure the results.

Conclusion

Proper MySQL indexing best practices aren’t just performance tricks—it’s absolutely essential for scalable applications. The difference between good and bad indexing can be the difference between a responsive application and one that frustrates users with delays.

Start implementing these tips today, and watch your database performance transform. Remember that indexing is a balance between read performance, write performance, and resource usage—find the sweet spot for your specific needs. Refer to official documentation to learn about indexing more in depth.

Have you implemented any of these MySQL indexing techniques? What kind of performance improvements did you see? Share your experiences in the comments!