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Silberschatz Korth Sudarshan: Database System Concepts, Fourth Edition
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IV Data Storage and Querying
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14 Query Optimization
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The McGraw Hill Companies, 2001
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Instead, to handle the case of avg, we maintain the sum and count aggregate values as described earlier, and compute the average as the sum divided by the count min, max: Consider a materialized view v = A Gmin(B) (r) (The case of max is exactly equivalent) Handling insertions on r is straightforward Maintaining the aggregate values min and max on deletions may be more expensive For example, if the tuple corresponding to the minimum value for a group is deleted from r, we have to look at the other tuples of r that are in the same group to nd the new minimum value
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The set operation intersection is maintained as follows Given materialized view v = r s, when a tuple is inserted in r we check if it is present in s, and if so we add it to v If a tuple is deleted from r, we delete it from the intersection if it is present The other set operations, union and set difference, are handled in a similar fashion; we leave details to you Outer joins are handled in much the same way as joins, but with some extra work In the case of deletion from r we have to handle tuples in s that no longer match any tuple in r In the case of insertion to r, we have to handle tuples in s that did not match any tuple in r Again we leave details to you
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So far we have seen how to update incrementally the result of a single operation To handle an entire expression, we can derive expressions for computing the incremental change to the result of each subexpression, starting from the smallest subexpressions For example, suppose we wish to incrementally update a materialized view E1 1 E2 when a set of tuples ir is inserted into relation r Let us assume r is used in E1 alone Suppose the set of tuples to be inserted into E1 is given by expression D1 Then the expression D1 1 E2 gives the set of tuples to be inserted into E1 1 E2 See the bibliographical notes for further details on incremental view maintenance with expressions
1453 Query Optimization and Materialized Views
Query optimization can be performed by treating materialized views just like regular relations However, materialized views offer further opportunities for optimization: Rewriting queries to use materialized views: Suppose a materialized view v = r 1 s is available, and a user submits a query r 1 s 1 t Rewriting the query as v 1 t may provide a more ef cient query plan than optimizing the query as submitted Thus, it is the job of the
Silberschatz Korth Sudarshan: Database System Concepts, Fourth Edition
IV Data Storage and Querying
14 Query Optimization
The McGraw Hill Companies, 2001
Summary
query optimizer to recognize when a materialized view can be used to speed up a query Replacing a use of a materialized view by the view de nition: Suppose a materialized view v = r 1 s is available, but without any index on it, and a user submits a query A=10 (v) Suppose also that s has an index on the common attribute B, and r has an index on attribute A The best plan for this query may be to replace v by r 1 s, which can lead to the query plan A=10 (r) 1 s; the selection and join can be performed ef ciently by using the indices on rA and sB, respectively In contrast, evaluating the selection directly on v may require a full scan of v, which may be more expensive The bibliographical notes give pointers to research showing how to ef ciently perform query optimization with materialized views Another related optimization problem is that of materialized view selection, namely, What is the best set of views to materialize This decision must be made on the basis of the system workload, which is a sequence of queries and updates that re ects the typical load on the system One simple criterion would be to select a set of materialized views that minimizes the overall execution time of the workload of queries and updates, including the time taken to maintain the materialized views Database administrators usually modify this criterion to take into account the importance of different queries and updates: Fast response may be required for some queries and updates, but a slow response may be acceptable for others Indices are just like materialized views, in that they too are derived data, can speed up queries, and may slow down updates Thus, the problem of index selection is closely related, to that of materialized view selection, although it is simpler We examine these issues in more detail in Sections 2125 and 2126 Some database systems, such as Microsoft SQL Server 75, and the RedBrick Data Warehouse from Informix, provide tools to help the database administrator with index and materialized view selection These tools examine the history of queries and updates, and suggest indices and views to be materialized
146 Summary
Given a query, there are generally a variety of methods for computing the answer It is the responsibility of the system to transform the query as entered by the user into an equivalent query that can be computed more ef ciently The process of nding a good strategy for processing a query, is called query optimization The evaluation of complex queries involves many accesses to disk Since the transfer of data from disk is slow relative to the speed of main memory and the CPU of the computer system, it is worthwhile to allocate a considerable amount of processing to choose a method that minimizes disk accesses The strategy that the database system chooses for evaluating an operation depends on the size of each relation and on the distribution of values within
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