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The query execution engine operates on an execution plan produced during query processing which typically is a physical operator graph whose edges specify the dataflow among the operators. There are two basic alternatives for evaluating the execution plan: materialization and pipelining. With materialized evaluation, the results of each operator are created (materialized) and stored in disk and then used as input for the evaluation of the next operator. With pipelining, the results of an operator are passed along as input to the next operator, before the first operator completes its execution. In general, pipelining improves the efficiency of query evaluation by reducing the number of temporary files that are produced.

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The output of query optimization is an execution plan or operator tree. The execution plan can be thought of as a dataflow graph where the nodes correspond to the physical operators and the edges represent the data flow among these physical...

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Recommended Reading

  1. Graefe G. Query evaluation techniques for large databases. ACM Comput. Surv., 25(2): 73–170, 1993.

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  2. Silberschatz A., Korth H.F., and Sudarshan S. Database System Concepts, 5th edn. McGraw Hill, New York, 2005.

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Pitoura, E. (2009). Pipelining. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA.

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