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We should explain that all timing tests for this book were done with a single-CPU computer, so comparisons between DBMSs would be misleading The results wouldn't give full points to the DBMS with the best threading mechanisms Bottom line: Organize so that multi-threading is possible, but don't hope it will make things better on an overloaded system The most important thing is to minimize synchronization That entails separating parts of statements or parts of databases, so that different threads don't need to access the same thing or to communicate with one another And if your DBMS is a process-per-client rather than a thread-per-client system, it behooves you to disconnect when you're done so the server won't waste space on you
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We said earlier that there can be no parallel threading if there is no separateness Separateness usually means partitioning A partition is a part of a database that consists of its own data and indexes (and often, its own configuration files and transaction logs) Tables can be located in one or more database partitions that is, a partitioned table can have some of its rows stored in one partition and other rows stored in other partitions Because data is divided across partitions, you can use the power of multiple processors to execute SQL statements The DBMS will automatically decompose SELECT and data-change statements into subrequests, and execute the subrequests in parallel among the applicable partitions The fact that a table is split across multiple partitions is transparent to users issuing SQL statements Executing the components of a task (eg, a SELECT statement) in parallel can enhance performance dramatically The main types of parallelism are I/O and Query
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I/O parallelism refers to the process of writing to, or reading from, two or more I/O devices simultaneously and can result in significant improvement in throughput Query parallelism refers to the simultaneous processing of parts of a single SQL statement In general, the DBMS subdivides what is usually considered a single database operation (eg, creating an index or resolving a SELECT) into multiple parts, many or all of which can be run in parallel (a) within a single partition or (b) across multiple partitions on either one machine or on multiple machines The result set is thus returned more quickly than if the query were run in serial fashion
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For an example of how to ensure you're getting the most out of your DBMS's parallelism abilities, consider how it's done with IBM
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One type of parallel environment supported by IBM allows multiple processors to share access to a database so that SQL statements can be divided among the processors You can specify the degree of parallelism to implement when compiling your application by using IBM's non-standard CURRENT DEGREE niladic function, or the DEGREE bind option
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(Degree means the number of concurrently executing parts of an SQL statement you want to allow) Each degree of parallelism adds to the system memory and CPU overhead IBM provides three configuration parameters that you can use to control and manage parallelism The first is the intra_parallel parameter; it enables or disables instance parallelism support The second is the max_querydegree parameter; it sets an upper limit for the degree of parallelism that can be used for any query and overrides the CURRENT DEGREE function and/or the DEGREE bind option values The third configuration parameter is the dft_degree parameter; it sets the default value for CURRENT DEGREE and the DEGREE bind option If you run a query with DEGREE = ANY, IBM's optimizer chooses the degree of parallelism based on a number of factors, including the number of processors and the characteristics of the query The actual degree used at runtime may be lower than the number of processors, depending on these factors
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