Here are ten ways to design a Postgres database for performance:

1. Use appropriate data types: Choose the most suitable data types for your columns based on the nature of the data. Using appropriate data types can significantly improve query performance and reduce storage space.

2. Normalize your database: Normalize your database schema to reduce data redundancy and improve data integrity. This helps in maintaining a clean and efficient database structure.

3. Create indexes strategically: Create indexes on columns that are frequently used in WHERE clauses, JOIN conditions, and ORDER BY clauses. Indexes can dramatically improve query performance by allowing Postgres to quickly locate the required data. However, be cautious not to overuse indexes as they can negatively impact write performance.

4. Partition large tables: If you have large tables with millions of rows, consider partitioning them based on a suitable criteria, such as date ranges or categories. Partitioning allows Postgres to efficiently scan only the relevant partitions instead of the entire table, thereby improving query performance.

5. Use materialized views: Materialized views are precomputed result sets that can be refreshed periodically. They are useful for complex queries that are executed frequently. By storing the result set in a materialized view, subsequent queries can retrieve the data directly from the view, avoiding expensive computations.

6. Optimize queries with EXPLAIN and ANALYZE: Use the EXPLAIN and ANALYZE commands to analyze the execution plan and performance of your queries. These commands provide insights into how Postgres is executing the query, including the estimated costs and actual execution time. Use this information to identify and optimize slow queries.

7. Tune Postgres configuration parameters: Postgres has various configuration parameters that can be tuned to optimize performance based on your hardware and workload. Some important parameters include shared_buffers, work_mem, effective_cache_size, and max_connections. Experiment with different values and monitor the impact on performance.

8. Use proper transaction management: Minimize the duration of transactions and avoid holding locks for longer than necessary. Use appropriate isolation levels based on your application requirements. Properly managing transactions can help prevent contention and improve overall performance.

9. Denormalize selectively: While normalization is generally recommended, there are cases where selective denormalization can improve query performance. Denormalization involves duplicating data across tables to avoid expensive joins. However, be cautious when denormalizing as it can introduce data redundancy and make updates more complex.

10. Utilize Postgres extensions and plugins: Postgres has a rich ecosystem of extensions and plugins that can enhance performance for specific use cases. For example, the pg_trgm extension provides efficient similarity searching, while the pg_partman extension simplifies partition management. Explore and leverage extensions that are relevant to your application's needs.

Bonus tip: Use stored procedures and prepared statements: Stored procedures allow you to encapsulate complex logic and reuse it across multiple queries. Prepared statements, on the other hand, can improve performance by allowing Postgres to parse and optimize the query only once, even if it is executed multiple times with different parameters.

Remember, optimizing database performance is an iterative process. Continuously monitor and analyze your database performance, and make adjustments based on your specific workload and requirements.