In addition to the previous answers, another approach to optimize slow-running SQL queries is by considering the statistics and distribution of data in the underlying tables. Running the ANALYZE command can update statistics and help the query planner make more informed decisions. Moreover, using query hints or directives to influence the query optimizer's decisions can also be helpful in some cases. It's important to measure the impact of optimization efforts using tools like pg_stat_statements or benchmarking to ensure the desired improvements are achieved.

One approach to optimize a slow-running SQL query is by analyzing the query execution plan using EXPLAIN or EXPLAIN ANALYZE. The query plan provides insights into how the database engine is executing the query and helps identify potential bottlenecks or inefficient operations. By analyzing the plan, you can make informed decisions on creating or modifying indexes, rewriting the query, or partitioning large tables to improve performance.

Building on the first answer, you can consider using different techniques to optimize a slow-running query. These include optimizing data types and schema design, using appropriate join types or subqueries, leveraging caching mechanisms, and making use of advanced features such as window functions or materialized views. Additionally, monitoring and tuning database configuration parameters according to the workload can also help improve query performance.