Data Query Statement

Context:
- It can be associated to a Query Plan.
- It can be processed by a Query Optimizer.
- It can be supported by a Database System, such as a database querying system.
- It can range from being a Data Value Data Query to being an Analytical Data Query (such as an aggregation query).
- It can range from being a Declarative Query to being an Imperative Query.
- It can (often) be a Relational Data Query Statement.
- …
Example(s):
- a SQL SELECT Statement, such as a: HiveQL Query, MySQL Query.
- a Datalog Query.
- a XQuery Query.
- a Graph Query.
- …
Counter-Example(s)
- a Data Structure Definition Statement.
- a Data Manipulation Statement.
See: SQL Query, Database Querying Task.

References

Jeffrey F. Naughton. (2017). “Broadening and deepening query optimization yet still making progress: technical perspective.” In: ACM Communications.
- QUOTE: Query optimization is a fundamental problem in data management. Simply put, most database query languages are declarative rather than imperative — that is, they specify properties the answer should satisfy, rather than give an algorithm to compute the answer. The best known and most widely used database query language — SQL — is a prime example of a language for which optimization is essential. By "essential," I mean that database optimization is not a matter of shaving 10% or even a factor of 2x from a query's execution time. In database query evaluation, the difference between a good plan and a bad or even average plan can be multiple orders of magnitude — so successful query optimization makes the difference between a plan that runs quickly and one that never finishes at all.