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depositor tuples are stored on the disk near the customer tuple, the block containing the customer tuple contains tuples of the depositor relation needed to process the query If a customer has so many accounts that the depositor records do not t in one block, the remaining records appear on nearby blocks A clustering le organization is a le organization, such as that illustrated in Figure 1119 that stores related records of two or more relations in each block Such a le organization allows us to read records that would satisfy the join condition by using one block read Thus, we are able to process this particular query more ef ciently Our use of clustering has enhanced processing of a particular join (depositor 1 customer), but it results in slowing processing of other types of query For example, select * from customer requires more block accesses than it did in the scheme under which we stored each relation in a separate le Instead of several customer records appearing in one block, each record is located in a distinct block Indeed, simply nding all the customer records is not possible without some additional structure To locate all tuples of the customer relation in the structure of Figure 1119, we need to chain together all the records of that relation using pointers, as in Figure 1120 When clustering is to be used depends on the types of query that the database designer believes to be most frequent Careful use of clustering can produce signi cant performance gains in query processing

So far, we have considered only the representation of the relations themselves A relational-database system needs to maintain data about the relations, such as the Hayes Hayes Hayes Hayes Turner Turner Figure 1119

Main A-102 A-220 A-503 Putnam A-305

Fig 3-18 The circuit studied in Example 3-5

The McGraw Hill Companies, 2001

the text box does not have an outline with red handles, click it to select it)

Main A-102 A-220 A-503 Putnam A-305

schema of the relations This information is called the data dictionary, or system catalog Among the types of information that the system must store are these: Names of the relations Names of the attributes of each relation Domains and lengths of attributes Names of views de ned on the database, and de nitions of those views Integrity constraints (for example, key constraints) In addition, many systems keep the following data on users of the system: Names of authorized users Accounting information about users Passwords or other information used to authenticate users Further, the database may store statistical and descriptive data about the relations, such as: Number of tuples in each relation Method of storage for each relation (for example, clustered or nonclustered) The data dictionary may also note the storage organization (sequential, hash or heap) of relations, and the location where each relation is stored: If relations are stored in operating system les, the dictionary would note the names of the le (or les) containing each relation If the database stores all relations in a single le, the dictionary may note the blocks containing records of each relation in a data structure such as a linked list In 12, in which we study indices, we shall see a need to store information about each index on each of the relations:

loop The circuit for which we apply KVL looks like the simple network shown in Fig 3-20 So we have two voltage dividers The voltage across each resistor is (using the values given in the problem statement) R2 3 15 = 5 V Vs = R2 + R4 3+6 R4 6 15 = 10 V V4 = Vs = R2 + R4 3+6 V2 = A similar procedure can be applied to the resistors R1 and R3 , giving voltage dividers V1 = R1 2 Vs = 15 = 375 V R1 + R3 2+6 R3 6 Vs = 15 = 1125 V V3 = R1 + R3 2+6

The McGraw Hill Companies, 2001

11

Text Properties dialog box, as shown here:

Name of the index Name of the relation being indexed Attributes on which the index is de ned Type of index formed All this information constitutes, in effect, a miniature database Some database systems store this information by using special-purpose data structures and code It is generally preferable to store the data about the database in the database itself By using the database to store system data, we simplify the overall structure of the system and harness the full power of the database for fast access to system data The exact choice of how to represent system data by relations must be made by the system designers One possible representation, with primary keys underlined, is Relation-metadata (relation-name, number-of-attributes, storage-organization, location) Attribute-metadata (attribute-name, relation-name, domain-type, position, length) User-metadata (user-name, encrypted-password, group) Index-metadata (index-name, relation-name, index-type, index-attributes) View-metadata (view-name, de nition) In this representation, the attribute index-attributes of the relation Index-metadata is assumed to contain a list of one or more attributes, which can be represented by a character string such as branch-name, branch-city The Index-metadata relation is thus not in rst normal form; it can be normalized, but the above representation is likely to be more ef cient to access The data dictionary is often stored in a non-normalized form to achieve fast access The storage organization and location of the Relation-metadata itself must be recorded elsewhere (for example, in the database code itself), since we need this information to nd the contents of Relation-metadata

The le-organization techniques described in Section 117 the heap, sequential, hashing and clustering organizations can also be used for storing objects in an object-oriented database However, some extra features are needed to support objectoriented database features, such as set-valued elds and persistent pointers