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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]

Henry F. Korth studies database management systems, including database transactions and database query processing in parallel and distributed settings. His Lehigh profile records these research areas and work on database storage and aggregation systems.^[ref]

With Abraham Silberschatz and S. Sudarshan, he coauthored teaching material on the relational model. A relation is a set of tuples, so mathematical projection removes repeated result tuples; a query language or implementation that retains duplicates has different semantics unless duplicate removal is requested. For example, projecting the department column of two employee records from the same department gives one tuple under set semantics. Keeping this distinction explicit helps connect a declarative query to its actual result.^[ref]