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le We can locate the rst page containing the desired record or records, should any qualifying records exist, with a binary search in log2 B steps (This analysis assumes that the pages in the sorted le are stored sequentially, and we can retrieve the ith page on the le directly in one disk I/O This assumption is not valid if, for example, the sorted le is implemented as a heap le using the linked-list organization, with pages in the appropriate sorted order) Each step requires a disk I/O and two comparisons Once the page is known, the rst qualifying record can again be located by a binary search of the page at a cost of Clog2 R The cost is Dlog2 B + Clog2 R, which is a signi cant improvement over searching heap les If there are several qualifying records (eg, Find all students aged 18 ), they are guaranteed to be adjacent to each other due to the sorting on age, and so the cost of retrieving all such records is the cost of locating the rst such record (Dlog2 B+Clog2 R) plus the cost of reading all the qualifying records in sequential order Typically, all qualifying records t on a single page If there are no qualifying records, this is established by the search for the rst qualifying record, which nds the page that would have contained a qualifying record, had one existed, and searches that page Search with range selection: Again assuming that the range selection is on the sort eld, the rst record that satis es the selection is located as it is for search with equality Subsequently, data pages are sequentially retrieved until a record is found that does not satisfy the range selection; this is similar to an equality search with many qualifying records The cost is the cost of search plus the cost of retrieving the set of records that satisfy the search The cost of the search includes the cost of fetching the rst page containing qualifying, or matching, records For small range selections, all qualifying records appear on this page For larger range selections, we have to fetch additional pages containing matching records Insert: To insert a record while preserving the sort order, we must rst nd the correct position in the le, add the record, and then fetch and rewrite all subsequent pages (because all the old records will be shifted by one slot, assuming that the le has no empty slots) On average, we can assume that the inserted record belongs in the middle of the le Thus, we must read the latter half of the le and then write it back after adding the new record The cost is therefore the cost of searching to nd the position of the new record plus 2 (05B(D + RC)), that is, search cost plus B(D + RC) Delete: We must search for the record, remove the record from the page, and write the modi ed page back We must also read and write all subsequent pages because all.

Console.WriteLine("s2: Console.WriteLine("s3: Console.WriteLine("s4: Console.WriteLine("s5: Console.WriteLine("s6: {0} {0} {0} {0} {0} [{1}]", [{1}]", [{1}]", [{1}]", [{1}]", s2.Length, s3.Length, s4.Length, s5.Length, s6.Length, s2); s3); s4); s5); s6);

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Many more implementation alternatives are possible Which alternative is chosen will have a major e ect on the structure permitted by the schema The user and system developer perceive the data organization through the schema presented to them The use of the database will depend on the concepts supported by the schema, since these concepts are needed for the user to manipulate the database The debate which concepts are best will continue forever Tabular models have served models of reasonable size well When the models become large a schema which supports a hierarchical decomposition of the model space becomes desirable An integrated database will probably require a schema which can support more than one hierachical view 8-4-2 Evaluating the Alterntives for Translation of the Schema The approach shown for IDS is typical of compilation methods, while the approach shown for SOCRATE is typical for interpretation We will discuss the arguments for and against these two approaches, keeping in mind that compromises between the two methods exist as well The bene ts of compiling the schema information are the following: 1 High process time e ciency due to use of actual machine code It is generally conceded that interpretation of simple arithmetic statements is anywhere from 10 to 200 times slower than the execution of compiled code, and this experience may lead us to reject the interpretive approach 2 Special programs can be written and compiled to deal with special situations The entire processing repertory of the computer is available, including multiple compiler language and their libraries 3 No explicit space is required at process time to hold the schema 4 Facilities exist within programming languages to support this approach One of these is the capability to include source modules from a schema le, and another is the tailoring of programs provided by the use of macro expansion capabilities.

// returning the third character in each string Console.WriteLine("\nHere's the third character in each string..."); Console.WriteLine("s1: {0} [{1}]", s1[2], s1); Console.WriteLine("s2: {0} [{1}]", s2[2], s2); Console.WriteLine("s3: {0} [{1}]", s3[2], s3); Console.WriteLine("s4: {0} [{1}]", s4[2], s4); Console.WriteLine("s5: {0} [{1}]", s5[2], s5); Console.WriteLine("s6: {0} [{1}]", s6[2], s6); // testing for the character H in each string Console.WriteLine("\nIs there an h in the string "); Console.WriteLine("s1: {0} [{1}]", s1.ToUpper( ).IndexOf('H') >= 0 "yes" : "nope", Console.WriteLine("s2: {0} [{1}]", s2.ToUpper( ).IndexOf('H') >= 0 "yes" : "nope", Console.WriteLine("s3: {0} [{1}]", s3.ToUpper( ).IndexOf('H') >= 0 "yes" : "nope", Console.WriteLine("s4: {0} [{1}]", s4.ToUpper( ).IndexOf('H') >= 0 "yes" : "nope", Console.WriteLine("s5: {0} [{1}]", s5.ToUpper( ).IndexOf('H') >= 0 "yes" : "nope", Console.WriteLine("s6: {0} [{1}]", s6.ToUpper( ).IndexOf('H') >= 0 "yes" : "nope", // testing for strings the same as Console.WriteLine("\nWhich strings Console.WriteLine("s1: {0} [{1}]", String.Compare(s1, s2) == Console.WriteLine("s2: {0} [{1}]", String.Compare(s2, s2) == Console.WriteLine("s3: {0} [{1}]", String.Compare(s3, s2) == Console.WriteLine("s4: {0} [{1}]", String.Compare(s4, s2) == Console.WriteLine("s5: {0} [{1}]", String.Compare(s5, s2) == Console.WriteLine("s6: {0} [{1}]", String.Compare(s6, s2) ==

Sec 8-4

s1); s2); s3); s4); s5); s6);

The disadvantages of the compiled approach are the following: 1 All programs have to be regenerated when the schema changes, and new programs have to be written when new data and relationships have been speci ed 2 It is di cult to control whether all programs use the schema completely and are not dependent on speci c internal knowledge of the data organization This can leead to unpleasant suprises when changes to the schema are made 3 The apparent bene t of not requiring storage for the schema at processing time is largely illusory, since all the required information is stored somehow within the processing programs and might well appear multiple times in independent programs 4 Most compilers and higher level languages do not have the capability of de ning database structures as complex as we would like to be able to handle Conditional, composite, and iterative elds, for example, are rarely available on the language level 5 It can be di cult to adapt an existing compiler to generate code for some new hardware features which are being developed to aid databases

String 2 are the same as s2 [{0}] ", s2); 0 "Same!" : "Different", s1); 0 "Same!" : "Different", s2); 0 "Same!" : "Different", s3); 0 "Same!" : "Different", s4); 0 "Same!" : "Different", s5); 0 "Same!" : "Different", s6);

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