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Query: Find the welder with social security number = '123-45-6789' Given the multiring le of Fig 3-30 with the following record counts: 10 000 employees with 10 000 social security numbers, and 50 professions (of equal size, 200 members each); also 20 locations with 10 departments of 50 employees each The rst search alternative is to enter the employee ring at the entry provided for ordering by seniority and search for the employee by social security number, takes 5 000 block accesses The second search alternative - by profession and then within a profession takes 25 + 100 = 125 expected block accesses The optimum for two levels is 2 1 10 000 = 100 2 A third alternative - by location, department, and then employee takes 10 + 5 + 25 = 40 block accesses but is only possible if location and department are known The optimum for three levels is 3 1 3 10 000 = 33 2 The block fetch counts seen here are much greater than the expected number when indexes are used; the retrieval of successor records, however, is more economical in this structure than it is when using indexed les Locating an entire subset is quite fast, as shown in Ex 3-13 An attribute which does not partition the le into multiple levels, such as a social security number which has a perfect selectivity, is not very useful as a ring element in a personnel le, since to nd someone in this manner would require n/2 fetches Finding a speci c welder, however, is done fairly e ciently by searching down the profession chain and then through the welder chain The le designer hopes that these two chains will partition the search space e ectively; the optimum is attained when both chains are equal in length, namely, n In practice this is of course never quite true.

Example 3-13

Move the currently selected line to the bottom of the list (all command lines below it will ripple up)

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To summarize data for all the welders, we expect 25 + 200 = 225 accesses This number of records probably cannot be obtained more e ciently by any of the preceding methods In order to gain a complementary bene t in access time, locality of rings has also to be considered in the le design Clustering of Rings Records which are frequently accessed together are obviously best stored with a high degree of locality One single ring can, in general, be placed entirely within one cylinder so that all seeks are avoided when following this particular clustered ring When frequent reference to the header record of the ring is needed, that header record may also participate in the cluster Now the ring at the next-higher level will be di cult to cluster, unless the total space needed by all member records and their

NOTE! Every Command Line must be processed and evaluated, but not every Command in a line may actually execute Conditional Commands exist, which will only execute if a specific Condition being evaluated is true These commands allow you to create If Then command constructions in your DVD, and are quite popular

ancestors is su ciently small to be kept on one or a few cylinders With success traversal according to a clustered hierarchy will take very few seeks In substantial les a given record type at a lower level will require many cylinders, and traversals of rings other than the clustered ring will require seeks

A CVSM visually reinforces the impact of complexity; the other side of the coin is putting numbers to that impact Quantitative analysis using

A possible assignment for the data of Fig 3-30 is sketched in Example 3-14 Locating an employee via department will require only a few seeks within the department ring, to nd the employee ring for the department If there are many employees in some department, there may be some cylinder breaks in the ring Finding an employee by profession may require seeks between nearly every employee record Stock in a warehouse is found with few seeks for a given location; these rings are apt to be long, and will require some seeks at cylinder break points To relate stock to the inventory data will take seeks between records, but these rings may be short if there are few locations In a dynamically changing database, optimal clustering is di cult to maintain and its bene ts should be partially discounted A reorganization may be needed to restore clusters Categorizing Real Attributes Some attributes, such as profession, can be naturally enumerated as a number of discrete categories, eg, welders, bookkeepers Such a categorization will have good selectivity and the le will be partitioned to match the natural structure of the data Attributes that represent real or continuous data, eg, weight or height, although they may have a high selectivity, do not provide e ective partitioning unless they are arti cially categorized A statement to de ne discrete categories for a continuous variable is shown in Example 3-14

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