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Database query optimization chooses a database query plan for a database query by comparing ways to obtain the required result. The choice includes the order of relational joins, use of a database index, and the algorithms implementing those relational joins. A cost model estimates work from quantities such as input sizes and storage accesses. The selected plan minimizes estimated cost among considered alternatives; this need not be the fastest possible plan in reality.^[ref]

Consider a relational join of one million orders with customers, followed by a condition selecting ten customers. Applying the customer condition first can greatly shrink a join input. If a suitable database index locates those customers' orders, the engine may avoid scanning all orders. If almost all customers qualify, a sequential scan can instead be cheaper than many scattered index accesses. These are alternative ways of computing the same answer, not different meanings of the query.

Join order also matters with three or more inputs: an early join that creates a huge intermediate result can dominate the total work. Exhaustive search itself becomes expensive as the number of inputs grows. PostgreSQL therefore switches to a heuristic search above a configurable threshold. Inaccurate size estimates can favor a poor plan even when the search algorithm correctly minimizes its cost model.^[ref]

Database query processing is the sequence that turns a database query into returned results. In a typical SQL system, it includes parsing the text, checking names and types, rewriting the query when appropriate, database query optimization, and executing the chosen database query plan. Planning chooses operations; execution actually performs storage accesses, relational joins, filtering and aggregation.^[ref]

For a request for each customer's total order value, the engine might scan orders, group their database tuples by customer identifier, sum the values and return one result per customer. An alternative plan can first restrict the orders using a database index. The result specification alone does not prescribe which access method performs the work.

A query against a stored database view illustrates why processing has more than a text-to-loop step. PostgreSQL can rewrite the reference into a query on the underlying tables before planning. The executor then walks the resulting plan and obtains rows from its operations. The distinction is useful when diagnosing a slow query: excessive planning work, a poor plan, and unexpectedly expensive execution are different causes even if the same statement triggered them.^[ref]

S. Sudarshan researches database management systems, particularly database query optimization and the boundary between application code and database queries. His IIT Bombay research description explains work that rewrites repeated query invocations into batches or equivalent larger queries, combining program analysis with query planning.^[ref]

For example, an application can issue one query for each employee's department. Collecting the required identifiers and issuing a suitable combined query can reduce repeated communication, provided the rewrite preserves the original transaction and result semantics. Reducing the number of database calls is useful only if it preserves what the program computes. With Abraham Silberschatz and Henry F. Korth, Sudarshan also coauthored the database text and slides cited here.^[ref]