Optimizing MySQL Queries: Mastering the Art of Multi-Column Indexing

“In the intricate dance of database optimization, multiple-column indexes are a powerful tool. They transform slow, cumbersome queries into swift, agile operations. When used with precision and understanding and considering the unique requirements of your data, these indexes unlock the full performance potential of MySQL. This ensures that your queries hit the right notes every time.”

MinervaDB Performance Engineering

Using multiple-column indexes, also known as composite indexes, in MySQL can significantly enhance query performance. This allows the database engine to locate and retrieve the necessary data for a query more efficiently. Below is a guide on how to use them effectively.

Understanding Multiple Column Indexes

A multiple-column index is an index that involves two or more columns. When querying data using conditions that include these columns, MySQL can use the composite index to quickly locate the rows that meet the conditions.

Use Cases

1. Filtering on Multiple Columns

Consider an orders table with customer_idorder_date, and status columns.

If you often run queries filtering on customer_id and order_date, a composite index on (customer_id, order_date)would be beneficial.

CREATE INDEX idx_customer_date ON orders(customer_id, order_date);

2. Sorting and Filtering

If you need to filter and sort results:

SELECT * FROM orders
WHERE customer_id = 1
AND order_date > ‘2023-01-01’
ORDER BY order_date;

The composite index (customer_id, order_date) aids in both filtering and sorting results.

Best Practices

1. Leftmost Prefix:

MySQL can optimize queries using a leftmost prefix of the index. For example, an index on (col1, col2, col3) allows MySQL to optimize queries involving col1, both col1 and col2, or col1col2, and col3. However, it cannot optimize queries involving col2 alone or col2 and col3.

2. Cardinality Matters:

Consider creating indexes on high cardinality columns (i.e., columns with unique or near-unique values) as the first columns in the composite index.

3. Keep Index Selective:

Highly selective indexes can offer better performance.

4. Monitor the Index Length:

The total size of a composite index equals the sum of all included column sizes. Be aware of the index length, particularly with VARCHAR columns.


  1. DO use composite indexes for columns frequently used together in queries.
  2. DO arrange index columns based on selectivity and query patterns.
  3. DO consider column sizes when creating a composite index. Smaller columns are quicker.
  4. DO examine your queries and EXPLAIN output to understand how your indexes are utilized.


  1. DON’T create composite indexes for all column combinations without consideration. This can lead to more disk space usage and slower write operations.
  2. DON’T neglect to maintain your indexes. Over time, as data is added, removed, or updated, indexes can become fragmented.
  3. DON’T disregard single-column indexes if they are used by other queries. Replacing them with composite indexes could negatively affect those queries.
  4. DON’T overlook column cardinality when creating composite indexes. Low cardinality columns might be less effective in a composite index.


Multiple column indexes can greatly enhance query performance when used appropriately. Analyzing your queries, understanding your data distribution, and considering column cardinality can assist in designing effective composite indexes. Regular testing and monitoring are crucial to ensure your indexes provide the desired performance benefits.

About Shiv Iyer 390 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.