Query optimization is a function of many relational database management systems in which multiple query plans for satisfying a query are examined and a good query plan is identified. This may or not be the absolute best strategy because there are many ways of doing plans. There is a trade-off between the amount of time spent figuring out the best plan and the amount running the plan. Different qualities of database management systems have different ways of balancing these two. Cost based query optimizers evaluate the resource footprint of various query plans and use this as the basis for plan selection.
Typically the resources which are costed are CPU path length, amount of disk buffer space, disk storage service time, and interconnect usage between units of parallelism. The set of query plans examined is formed by examining possible access paths (e.g., primary index access, secondary index access, full file scan) and various relational table join techniques (e.g., merge join, hash join, product join). The search space can become quite large depending on the complexity of the SQL query. There are two types of optimization. These consist of logical optimization which generates a sequence of relational algebra to solve the query. In addition there is physical optimization which is used to determine the means of carrying out each operation.
Famous quotes containing the word query:
“Such condition of suspended judgment indeed, in its more genial development and under felicitous culture, is but the expectation, the receptivity, of the faithful scholar, determined not to foreclose what is still a question—the “philosophic temper,” in short, for which a survival of query will be still the salt of truth, even in the most absolutely ascertained knowledge.”
—Walter Pater (1839–1894)