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In a production database, the shared pool is going to command most of your attention because of its direct bearing on application performance. The shared pool is a part of the SGA that holds almost all the necessary elements for execution of the SQL statements and PL/SQL programs. In addition to caching program code, the shared pool caches the data dictionary information that Oracle needs to refer to often during the course of program execution. Proper shared pool configuration leads to dramatic improvements in performance. An improperly tuned shared pool leads to problems such as the following: Increased latch contention with the resulting demand for more CPU resources Greater I/O because executable forms of SQL aren t present in the shared pool Higher CPU usage because of unnecessary parsing of SQL code The general increase in shared pool waits and other waits observed during a severe slowdown of the production database is the result of SQL code that fails to use bind variables (I explain the important concept of bind variables in the following section). As the number of users increases, so does the demand on shared pool memory and latches, which are internal locks for memory areas. If there are excessive latches, the result might be a higher wait time and a slower response time. Sometimes the entire database seems to hang. The shared pool consists of two major areas: the library cache and the data dictionary cache. You can t allocate or decrease memory specifically for one of these components. If you increase the total shared pool memory size, both components will increase in some ratio that Oracle determines. Similarly, when you decrease the total shared pool memory, both components will decrease in size. Let s look at these two important components of the shared pool in detail.

The library cache holds the parsed and executable versions of SQL and PL/SQL code. As you may recall from 19, all SQL statements undergo the following steps during their processing: Parsing, which includes syntactic and semantic verification of SQL statements and checking of object privileges to perform the actions. Optimization, where the Oracle optimizer evaluates how to process the statement with the least cost, after it evaluates several alternatives. Execution, where Oracle uses the optimized physical execution plan to perform the action stated in the SQL statement. Fetching, which only applies to SELECT statements where Oracle has to return rows to you. This step isn t necessary in any nonquery-type statements. Parsing is a resource-intensive operation, and if your application needs to execute the same SQL statement repeatedly, having a parsed version in memory will reduce contention for latches, CPU, I/O, and memory usage. The first time Oracle parses a statement, it creates a parse tree. The optimization step is necessary only for the first execution of a SQL statement. Once the statement is optimized, the best access path is encapsulated in the access plan. Both the parse tree and the access plan are stored in the library cache before the statement is executed for the first time. Future invocation of the same statement will need to go through only the last stage, execution, which avoids the overhead of parsing and optimizing as long as Oracle can find the parse tree and access plan in the library cache. Of course, if the statement is a SQL query, the last step will be the fetch operation.

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In this chapter, you ll look at some of the issues related to locking in Oracle. In particular, you ll learn about the infamous lost update problem and various ways to address it. Along the way, you ll examine different strategies to implement two solutions to the lost update problem, namely optimistic locking and pessimistic locking. You ll also compare these two solutions and determine when to use each strategy.

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