What are the ways you can use to optimize a slow running query?

Optimizing a slow running query involves several strategies. First, examine the query execution plan using EXPLAIN or the database’s query profiler to identify bottlenecks such as full table scans, expensive joins, or unnecessary sorting. Indexing is often the most effective remedy: create B‑tree indexes on columns used in WHERE clauses, JOIN predicates, or ORDER BY statements. If the table is large, consider partitioning or clustering on frequently filtered columns to reduce the amount of data scanned. For joins, choose the appropriate join type (hash, merge, or nested loop) based on data size and distribution, and ensure that join keys are indexed. Materialized views or summary tables can pre‑aggregate data for common queries. Caching frequently accessed data in memory (e.g., using a Redis cache or the database’s in‑memory engine) can also cut latency. Finally, rewrite the query to eliminate subqueries or correlated subqueries that force repeated scans, and use query hints or optimizer directives if supported. Each of these techniques reduces I/O, CPU, and memory usage, leading to faster execution times.