In addition to the points mentioned above, it's crucial to consider the data types and sizes used in the schema. Choosing appropriate data types can minimize storage requirements and enhance query performance. Additionally, partitioning large tables can improve query execution, especially when dealing with historical or time-series data. However, it's important to regularly monitor and maintain the partitions to ensure optimal performance and prevent fragmentation. Lastly, optimizing query performance also requires analyzing and fine-tuning the SQL queries themselves, by avoiding unnecessary operations, optimizing the logical order of operations, and leveraging SQL features like subqueries and CTEs (Common Table Expressions) where applicable.

In my experience, another aspect to consider for high-performance database schema design is the careful use of constraints. While constraints ensure data consistency, they can also impact performance. For example, adding a foreign key constraint across multiple tables might require additional joins during query execution. It's important to strike a balance between enforcing data integrity and query performance. Additionally, leveraging database features like query caching and stored procedures can significantly improve performance by reducing network latency and query processing overhead. Finally, keeping up with the latest database server versions and periodically reviewing and optimizing indexes and statistics are crucial for maintaining high performance.

One important consideration for high-performance database schema design is to properly set up indexes. By identifying the commonly used columns in queries and creating indexes on those columns, you can significantly improve query performance. However, it's important to be mindful of the trade-off between read performance and write performance, as indexes can slow down write operations. Another best practice is to denormalize the schema to reduce the number of joins needed in queries, as joins can be expensive operations. This should be done carefully though, as denormalization can introduce redundancy and potential data inconsistencies. Overall, ensuring proper indexing, carefully balancing normalization and denormalization, and regularly reviewing query execution plans are some of the key steps to optimize SQL query performance.