In addition to the above suggestions, we may want to consider restructuring the query by leveraging advanced SQL features. For example, we could explore using window functions to perform complex calculations and aggregations without the need for multiple subqueries. Another approach is to investigate whether the query can benefit from the powerful capabilities of SQL analytic functions, which can help streamline and simplify complex queries. Additionally, exploring the use of table partitioning or indexed views may provide performance gains in certain situations. It's advisable to test and benchmark different optimization techniques to determine the most effective approach for a specific query and dataset.

One approach to optimizing a SQL query with multiple joins and complex conditions is to carefully analyze the execution plan. By examining the execution plan, we can identify any inefficiencies, such as missing or incorrect indexes, and address them appropriately. Additionally, we can consider breaking down the query into smaller, more manageable subqueries or using common table expressions (CTEs) for better readability and maintenance. Another optimization technique is to ensure that the tables involved have accurate statistics to assist the query optimizer in making efficient decisions. It is also worth exploring if any alternative approaches, such as using temporary tables or materialized views, could improve performance in specific scenarios.

I agree with the previous responses, and I'd like to add that sometimes optimizing a complex SQL query goes beyond just improving the query itself. It often requires optimizing the underlying database schema and the overall data model. By properly indexing the tables and ensuring normalization and denormalization are appropriately applied, we can enhance performance. It's also beneficial to review the hardware and infrastructure aspects, ensuring sufficient resources are allocated to support the query workload. In some cases, horizontally scaling the databases across multiple servers can provide a significant performance boost. Ultimately, optimizing complex queries involves employing a holistic approach that considers the query, schema, infrastructure, and data model.