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A-888
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Sequential le after an insertion
insertion or deletion We can manage deletion by using pointer chains, as we saw previously For insertion, we apply the following rules: 1 Locate the record in the le that comes before the record to be inserted in search-key order 2 If there is a free record (that is, space left after a deletion) within the same block as this record, insert the new record there Otherwise, insert the new record in an over ow block In either case, adjust the pointers so as to chain together the records in search-key order Figure 1116 shows the le of Figure 1115 after the insertion of the record (North Town, A-888, 800) The structure in Figure 1116 allows fast insertion of new records, but forces sequential le-processing applications to process records in an order that does not match the physical order of the records If relatively few records need to be stored in over ow blocks, this approach works well Eventually, however, the correspondence between search-key order and physical order may be totally lost, in which case sequential processing will become much less ef cient At this point, the le should be reorganized so that it is once again physically in sequential order Such reorganizations are costly, and must be done during times when the system load is low The frequency with which reorganizations are needed depends on the frequency of insertion of new records In the extreme case in which insertions rarely occur, it is possible always to keep the le in physically sorted order In such a case, the pointer eld in Figure 1115 is not needed
1172 Clustering File Organization
Many relational-database systems store each relation in a separate le, so that they can take full advantage of the le system that the operating system provides Usually, tuples of a relation can be represented as xed-length records Thus, relations
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can be mapped to a simple le structure This simple implementation of a relational database system is well suited to low-cost database implementations as in, for example, embedded systems or portable devices In such systems, the size of the database is small, so little is gained from a sophisticated le structure Furthermore, in such environments, it is essential that the overall size of the object code for the database system be small A simple le structure reduces the amount of code needed to implement the system This simple approach to relational-database implementation becomes less satisfactory as the size of the database increases We have seen that there are performance advantages to be gained from careful assignment of records to blocks, and from careful organization of the blocks themselves Clearly, a more complicated le structure may be bene cial, even if we retain the strategy of storing each relation in a separate le However, many large-scale database systems do not rely directly on the underlying operating system for le management Instead, one large operating-system le is allocated to the database system The database system stores all relations in this one le, and manages the le itself To see the advantage of storing many relations in one le, consider the following SQL query for the bank database: select account-number, customer-name, customer-street, customer-city from depositor, customer where depositorcustomer-name = customercustomer-name This query computes a join of the depositor and customer relations Thus, for each tuple of depositor, the system must locate the customer tuples with the same value for customer-name Ideally, these records will be located with the help of indices, which we shall discuss in 12 Regardless of how these records are located, however, they need to be transferred from disk into main memory In the worst case, each record will reside on a different block, forcing us to do one block read for each record required by the query As a concrete example, consider the depositor and customer relations of Figures 1117 and 1118, respectively In Figure 1119, we show a le structure designed for ef cient execution of queries involving depositor 1 customer The depositor tuples for each customer-name are stored near the customer tuple for the corresponding customername This structure mixes together tuples of two relations, but allows for ef cient processing of the join When a tuple of the customer relation is read, the entire block containing that tuple is copied from disk into main memory Since the corresponding customer-name Hayes Hayes Hayes Turner Figure 1117 account-number A-102 A-220 A-503 A-305
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