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In these xed indexes the anchor points always have the same source key values, while in indexed les the anchor keys were those of entries appearing at the le system partitions The lowest level index needs to contain only one entry per bucket, so that the index size is again reduced The proper allocation to buckets depends on the expected average record length, the block length, and the desired performance ratios between le density, sequential access, random access, and updating of the le An indexed bucket organization can be very useful for les consisting of program or data text The records here are small and of variable length, but the variation between typical pages of text is not very great Both fast sequential access, for processing and searching, as well as reasonable access to speci c lines to be edited are required Keeping the density low provides all these features A bucket which contains a page of text can be expected to ll several blocks These blocks are exclusively assigned to an index entry Multiple blocks for one index entry can be chained sequentially from the initial block referenced by the index entry Figure 4-3 sketches this organization This le now can be read sequentially with a low overhead Space will remain unused in the last block of a sequence of blocks containing data for one bucket Relatively fewer blocks will be lled incompletely if the buckets use chains consisting of multiple blocks Better use of space causes increased fetch processing time, since the number of accesses to locate a record increases for many of the records An insertion into a long chain can be particularly expensive Careful allocation of continuation blocks to increase locality will reduce the frequency of seeks and also the expected rotational latency incurred when fetching or inserting
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Hybrid File Organizations If we denote the chain length in terms of blocks as bch we expect that
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bch + 1 )(s + r + btt) 4-1 2 Here x is again the depth of the index and s + r + btt the components of the random access time If record sizes and record quantities per bucket are random, half a block per chain will be wasted Thus, the waste per record in the chain is TF = (x + W =
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R 1 = Bfr 2bch 1
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The index space SI is preallocated to contain m bucket pointers of size P The value of m = nmax /(bch Bfr), typically m < n The space used per record R is then R = SI R R +R+ P +R+ n 2bch 1 2bch 1 4-3
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where R is the actual record size The storage required for the index, SI, is reduced when the bucket size and hence the value of bch increases When such les are used for the storage of textual data, the line numbers can provide the basis for block address computation The index entry is found by computing the quotient of the line number and the estimate of lines that can be stored in the block or blocks expected per bucket, as shown in the example below:
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Given is B = 1000 and bch = 2 The expected bucket loading capacity is then 1500 bytes The text averages 75 characters per line; hence one bucket should be assigned for each set of 20 lines
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4-1-4 Transposed Files In some applications there is a frequent requirement to access all values of a given attribute An example is the selection of the average person, where rst the sum of all values of a given attribute has to be computed before a speci c record can be selected If the le is relatively small and storage is a ordable, the creation of a duplicate data le, in transposed form, may be the easiest solution to the problem of minimizing exhaustive search times for speci c attributes Only one record is fetched per attribute from a transposed le, and this record contains the values of this attribute for all entities, eg, persons, in the le A transposed le contains one record per transposed attribute type from the main le, and each record has a number of data values which is equal to the number of records in the main le as shown in Fig 4-4 If all attributes are transposed, the transposed le will be equal in size to the main le A transposed le is oriented to aid selection or evaluation of data according to a dimension which is orthogonal to the data entry or update dimension Use of transposition presents opportunities for the discovery of trends or functional dependencies among attributes which are otherwise not obvious Many researchoriented data processing tasks will take transposed data and group, select, and
Sec 4-1
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