1. Understand the Problem First

• Know the data volume and growth rate. Estimate how fast data will grow—this guides how much optimization you’ll need.
• Identify slow queries. Use tools like EXPLAIN, MySQL’s slow query log, or performance_schema to find queries that take too long.
• Measure before changes. A baseline helps confirm whether your optimizations are working.

2. Use Indexes Wisely

Indexes are among the most powerful tools in MySQL, but misuse can hurt more than help.

• Index the columns you filter (WHERE), join on, or sort (ORDER BY). These are often the bottlenecks.
• Keep indexes compact. Avoid indexing large text or blob columns unless absolutely necessary. Smaller indexes work faster.
• Use composite (multi-column) indexes when multiple columns are used together in queries. Order matters: MySQL can only use leftmost parts of composite indexes.
• Avoid redundant indexes. Two similar indexes waste space and slow down write operations.

3. Optimize Joins and Subqueries

• Prefer joins over subqueries when possible. Joins often perform better, especially for large tables.
• Limit the data before joining. Use sub-selects or temporary tables to trim data early rather than full joins on entire tables.
• Be aware of join order. MySQL’s optimizer does a lot, but writing joins in logical order (smallest filtered table first) can help.

4. Limit Data Retrieved

• Select only necessary columns. SELECT * is convenient but inefficient.
• Paginate results. For user-facing output, use LIMIT / OFFSET smartly. For large offsets, consider cursor-based pagination.
• Filter early. Apply WHERE clauses as soon as possible to reduce dataset size.

5. Use Efficient Query Structures

• Avoid functions in WHERE clauses where possible (e.g. applying YEAR(date_column) on every row prevents index use).
• Use IN vs. multiple ORs. A well-structured IN clause is often more efficient than lots of OR.
• Use EXISTS instead of IN in certain cases. Especially when checking presence of rows in related tables.

Building efficient MySQL queries for large datasets is not about one trick—it’s about applying several best practices together. Good indexing, wise joins, careful query structure, monitoring, and appropriate use of caching or partitioning all play important roles. When done right, these optimizations improve speed, reduce load, and make your system more maintainable