How would you optimize a complex SQL query that uses CASE statements?

Optimizing a SQL query with many CASE expressions involves several tactics. First, ensure that the columns used in the CASE conditions are indexed; this speeds up the predicate evaluation. Second, consider rewriting nested CASEs into simpler expressions or using derived tables to pre‑compute intermediate results, reducing the number of times the CASE logic is executed. Third, if the CASE is used for conditional aggregation, replace it with conditional SUMs or FILTER clauses (in PostgreSQL) to let the engine push down predicates. Fourth, evaluate whether the CASE can be replaced by a JOIN to a lookup table, which can be more efficient if the mapping is large. Fifth, analyze the execution plan to identify full table scans or hash joins that could be avoided by adding appropriate indexes or rewriting the query to use EXISTS or IN. Finally, test the query with realistic data volumes, as the optimizer’s cost estimates may differ from actual runtime, and adjust hints or rewrite the query accordingly.