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In this section, we study two approaches for improving transaction performance: Improve set orientation Reduce lock contention In the past, optimizers on many database systems were not particularly good, so how a query was written would have a big in uence on how it was executed, and therefore on the performance Today s advanced optimizers can transform even badly written queries and execute them ef ciently, so the need for tuning individual queries is less important than it used to be However, complex queries containing nested subqueries are not optimized very well by many optimizers Most systems provide a mechanism to nd out the exact execution plan for a query; this information can be used to rewrite the query in a form that the optimizer can deal with better In embedded SQL, if a query is executed frequently with different values for a parameter, it may help to combine the calls into a more set-oriented query that is
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executed only once The costs of communication of SQL queries can be high in client server systems, so combining the embedded SQL calls is particularly helpful in such systems For example, consider a program that steps through each department speci ed in a list, invoking an embedded SQL query to nd the total expenses of the department by using the group by construct on a relation expenses(date, employee, department, amount) If the expenses relation does not have a clustered index on department, each such query will result in a scan of the relation Instead, we can use a single SQL query to nd total expenses of all departments; the query can be evaluated with a single scan The relevant departments can then be looked up in this (much smaller) temporary relation containing the aggregate Even if there is an index that permits ef cient access to tuples of a given department, using multiple SQL queries can have a high communication overhead in a client server system Communication cost can be reduced by using a single SQL query, fetching its results to the client side, and then stepping through the results to nd the required tuples Another technique used widely in client server systems to reduce the cost of communication and SQL compilation is to use stored procedures, where queries are stored at the server in the form of procedures, which may be precompiled Clients can invoke these stored procedures, rather than communicate entire queries Concurrent execution of different types of transactions can sometimes lead to poor performance because of contention on locks Consider, for example, a banking database During the day, numerous small update transactions are executed almost continuously Suppose that a large query that computes statistics on branches is run at the same time If the query performs a scan on a relation, it may block out all updates on the relation while it runs, and that can have a disastrous effect on the performance of the system Some database systems Oracle, for example permit multiversion concurrency control, whereby queries are executed on a snapshot of the data, and updates can go on concurrently This feature should be used if available If it is not available, an alternative option is to execute large queries at times when updates are few or nonexistent For databases supporting Web sites, there may be no such quiet period for updates Another alternative is to use weaker levels of consistency, whereby evaluation of the query has a minimal impact on concurrent updates, but the query results are not guaranteed to be consistent The application semantics determine whether approximate (inconsistent) answers are acceptable Long update transactions can cause performance problems with system logs, and can increase the time taken to recover from system crashes If a transaction performs many updates, the system log may become full even before the transaction completes, in which case the transaction will have to be rolled back If an update transaction runs for a long time (even with few updates), it may block deletion of old parts of the log, if the logging system is not well designed Again, this blocking could lead to the log getting lled up To avoid such problems, many database systems impose strict limits on the number of updates that a single transaction can carry out Even if the system does not impose such limits, it is often helpful to break up a large update transaction into a set
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