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204 Intraquery Parallelism
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Intraquery parallelism refers to the execution of a single query in parallel on multiple processors and disks Using intraquery parallelism is important for speeding up long-running queries Interquery parallelism does not help in this task, since each query is run sequentially To illustrate the parallel evaluation of a query, consider a query that requires a relation to be sorted Suppose that the relation has been partitioned across multiple disks by range partitioning on some attribute, and the sort is requested on the partitioning attribute The sort operation can be implemented by sorting each partition in parallel, then concatenating the sorted partitions to get the nal sorted relation Thus, we can parallelize a query by parallelizing individual operations There is another source of parallelism in evaluating a query: The operator tree for a query can contain multiple operations We can parallelize the evaluation of the operator tree by evaluating in parallel some of the operations that do not depend on one another Further, as 13 mentions, we may be able to pipeline the output of one operation to another operation The two operations can be executed in parallel on separate processors, one generating output that is consumed by the other, even as it is generated In summary, the execution of a single query can be parallelized in two ways: Intraoperation parallelism We can speed up processing of a query by parallelizing the execution of each individual operation, such as sort, select, project, and join We consider intraoperation parallelism in Section 205 Interoperation parallelism We can speed up processing of a query by executing in parallel the different operations in a query expression We consider this form of parallelism in Section 206 The two forms of parallelism are complementary, and can be used simultaneously on a query Since the number of operations in a typical query is small, compared to the number of tuples processed by each operation, the rst form of parallelism can
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scale better with increasing parallelism However, with the relatively small number of processors in typical parallel systems today, both forms of parallelism are important In the following discussion of parallelization of queries, we assume that the queries are read only The choice of algorithms for parallelizing query evaluation depends on the machine architecture Rather than presenting algorithms for each architecture separately, we use a shared-nothing architecture model in our description Thus, we explicitly describe when data have to be transferred from one processor to another We can simulate this model easily by using the other architectures, since transfer of data can be done via shared memory in a shared-memory architecture, and via shared disks in a shared-disk architecture Hence, algorithms for shared-nothing architectures can be used on the other architectures too We mention occasionally how the algorithms can be further optimized for shared-memory or shared-disk systems To simplify the presentation of the algorithms, assume that there are n processors, P0 , P1 , , Pn 1 , and n disks D0 , D1 , , Dn 1 , where disk Di is associated with processor Pi A real system may have multiple disks per processor It is not hard to extend the algorithms to allow multiple disks per processor: We simply allow Di to be a set of disks However, for simplicity, we assume here that Di is a single disk
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205 Intraoperation Parallelism
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Since relational operations work on relations containing large sets of tuples, we can parallelize the operations by executing them in parallel on different subsets of the relations Since the number of tuples in a relation can be large, the degree of parallelism is potentially enormous Thus, intraoperation parallelism is natural in a database system We shall study parallel versions of some common relational operations in Sections 2051 through 2053
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