Complex optimization strategies in MySQL involve advanced techniques to fine-tune query performance for complex and resource-intensive queries. These strategies go beyond basic indexing and query rewriting and delve into advanced database optimization methods to achieve the best possible execution plans. Here are some complex optimization strategies:

1. Query Rewrite and Transformation:

• Materialized Views: Creating materialized views involves storing the results of a complex query in a precomputed form to speed up query execution. This is particularly useful for queries with aggregations or joins that are resource-intensive.
• Subquery to Join Transformation: Transforming subqueries into joins can improve performance in some cases by allowing the optimizer to optimize the entire query more effectively.

2. Index Optimization:

• Covering Indexes: Creating indexes that cover all the columns needed for a query can avoid the need for additional table lookups, leading to faster query execution.
• Prefix Indexes: Using prefix indexes can be beneficial for columns with long values, reducing the size of the index and improving performance.

3. Partitioning and Sharding:

• Table Partitioning: Dividing a large table into smaller partitions based on certain criteria (e.g., range, list, hash) can improve query performance and maintenance tasks.
• Database Sharding: Sharding involves splitting a large database into smaller databases or instances to distribute the workload and achieve better scalability.

4. Query Optimization Hints:

• USE INDEX / FORCE INDEX: Specifying an index to use in a query using hints can sometimes lead to better query plans.
• IGNORE INDEX: In some cases, it might be beneficial to instruct the optimizer to avoid using a specific index.

5. Optimizer Statistics and Configuration:

• Histograms and Extended Statistics: Creating histograms or extended statistics on columns can help the optimizer make more accurate decisions about query execution plans.
• Optimizer Settings: Tweaking optimizer-related configuration parameters can impact the optimizer’s decisions about query execution strategies.

6. Query Cache Management:

• Query Cache Control: The query cache stores results of SELECT queries, but it can negatively impact performance for frequently updated tables. Managing the query cache and invalidating cache entries as needed is important.

7. Parallel Query Execution:

• Parallel Execution: Some queries can be divided into subtasks that can be executed concurrently. Configuring MySQL to use parallel execution can improve performance on multi-core systems.

8. In-Memory Tables and Caching:

• Memory Storage Engine: Storing frequently accessed data in memory tables can significantly reduce disk I/O and improve query performance.
• Query Cache and Key-Value Caches: Using query caches and key-value caches like Memcached or Redis can help reduce the load on the database server.

9. Profiling and Monitoring:

• Query Profiling: Profiling queries can help identify bottlenecks and areas for optimization.
• Database Monitoring: Using monitoring tools can help track performance metrics and detect performance degradation.

10. Materialized Query Result Storage:

• Temporary Tables: Storing intermediate query results in temporary tables can help reduce the complexity of the main query and improve performance.

Complex optimization strategies involve a deep understanding of MySQL’s query execution engine, query optimization, indexing, and database architecture. Implementing these strategies requires careful analysis, testing, and consideration of the specific workload and database structure. It’s important to thoroughly test optimizations in a controlled environment before applying them to production systems.