Optimizing MySQL Performance: Best Practices for Effective Indexing and Function Usage


Implementing functions on MySQL indexes, often referred to as using functional or expression-based indexes, is a topic with some nuanced considerations. Here's why this practice can be problematic and some tips and tricks for optimizing indexing in MySQL for better performance.

Why Functions on Indexes Can Be Problematic

  1. Index Ineffectiveness: When you apply a function to a column in a query condition (e.g., WHERE YEAR(date_column) = 2021), MySQL cannot use a standard index on date_column efficiently. This is because the index is on the actual date value, not on the year extracted from the date.
  2. Full Table Scans: Such queries often result in full table scans, leading to increased query execution times, especially for large tables.
  3. Complex Query Optimization: The MySQL query optimizer might struggle to accurately assess the cardinality and selectivity of function-based expressions, leading to suboptimal execution plans.

Tips and Tricks for Indexing MySQL Database

  1. Use Indexes on Base Columns: Instead of applying functions in your queries, try to index and query base columns directly. For example, instead of using YEAR(date_column), consider storing the year in a separate indexed column if it’s frequently queried.
  2. Covering Indexes: Use covering indexes, which include all the fields used in a query (SELECT, JOIN, WHERE, ORDER BY, GROUP BY). This allows the query to be satisfied entirely using the index, reducing disk I/O.
  3. Index Selectivity: Index highly selective columns where the index entries have unique or near-unique values. This makes the index more effective in query filtering.
  4. Composite Indexes: Create composite (multi-column) indexes when queries frequently filter or sort on multiple columns. The order of columns in the index should match the order of columns in the query.
  5. Avoid Redundant Indexes: Redundant indexes increase disk space usage and can slow down write operations. Regularly review and remove unnecessary indexes.
  6. Partial Indexes: If you have VARCHAR columns with long text, consider using a prefix index to index only the first few characters.
  7. Monitor and Analyze Index Usage: Use tools like Performance Schema or EXPLAIN to analyze index usage and effectiveness. This helps in identifying unused or inefficient indexes.
  8. Partitioning Large Tables: For very large tables, consider partitioning. This can improve performance, especially for range queries and in scenarios where only a subset of data is frequently accessed.
  9. Index Maintenance: Regularly run ANALYZE TABLE to update index statistics, ensuring the optimizer has accurate information for query planning.
  10. Avoid Indexing Every Column: Don’t over-index. Having an index on every column can be counterproductive, as it increases the cost of write operations (INSERT, UPDATE, DELETE).


While implementing functions on indexes is generally not effective in MySQL, focusing on indexing strategies that align with your query patterns and data characteristics is key to achieving optimal performance. Understanding how the MySQL optimizer utilizes indexes, and regularly monitoring and adjusting your indexing strategy, are fundamental to maintaining a high-performance MySQL database.

About Shiv Iyer 456 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.