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There are two types of performance tests that form the core of the strategy of writing highperformance applications: Testing code in isolation using utilities such as JRunstats for scientifically proving which approach out of a set of approaches is suitable for your system. You have already seen how useful this test harness is in some of the previous examples. A suite of tests for systemwide testing that enables the following: A way to load the system with typical representative data sets. The data loading logic should be maintained and enhanced as you learn more about the system later on. The code that does this should also be an inherent part of the rest of your checked-in source code. A set of tests that test out the performance of the most important business functions of your end user. A way to preserve the systemwide performance data in the test and production systems. This enables you to compare your current code with how it did in the past, and to identify new performance bottlenecks as soon as they are introduced in the system. One way to do this for an Oracle-based system is to use the statspack utility (in 10g, the feature Automatic Workload Repository has replaced statspack; see Oracle Database Performance Tuning Guide and Reference [10g Release 1] for more details).

Figure 3-42 Avoiding a race condition by reordering notifications A potential problem can occur if C sends a new notification n3 (not shown in the figure) to B while processing n11, because B might be expecting n12 before n3 The decision to rearrange the order in which A sends notifications to B and C solves one problem but creates potential others A tempting idea might be to stamp notifications with a time or sequence number, as mentioned earlier But this approach has at least two problems: 1 The burden of message ordering is put on receivers This burden might be considered excessive in many systems 2 To reorder out-of-order messages, the receiver often needs to buffer messages over a period of time.

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You have already seen this principle in action in previous sections. If approach 1 works 10% faster than approach 2 in a single-user environment, but it consumes 100% more resources (in terms of latches, etc.), then approach 2 is much more likely to be suitable for a multiuser environment. In this book, we will always consider scalability of an approach as well as the response time before coming to a conclusion on which approach is better. We will use the runstats utility and its Java version, JRunstats, for this purpose throughout the book.

With time and sequence stamps, the receiver can t always start processing a notification the moment it is received, because the receiver might not be able to instantaneously determine whether a message is in the expected order For example, consider a notification system that numbers messages incrementally Each time a receiver gets a message, it saves the message number Say the number is 100 If the next message has the number 101, the receiver can process it immediately, because it is the next message created in the entire system But what if the number is 102 The receiver might wait for a short while to see if 101 arrives late This waiting period is called the grace period and is obviously related to the system s delivery latency.

Figure 6-2. Set the Layout options for report filters. 3. Because you selected Over, Then Down, the following drop-down list is labeled Report Filter Fields per Row. Select three as the number of report filters you want across each row, before the next row of report filters starts. 4. Click OK to close the PivotTable Options dialog box.

Perhaps the only universal truth when it comes to performance guidelines is that there are no universal truths! A sound approach in one situation often turns out to be a lousy one in another. The only sane approach is to know the tools at your disposal, know how to use them, and benchmark to prove that your approach is the appropriate one in your particular case. Beware of guidelines that tell you to always take an approach, because more often than not there will be situations when that approach will not be the best one at hand (and it could easily be the worst one!). For example, take the guideline that says, Always use bind variables. It is true that in the majority of systems, this guideline holds true. However, if you are writing a data warehouse system, using bind variables may not be a good approach. This is because, in a data warehouse system, the execution time of queries is much higher than the parsing time, and the number of queries executed by the system per second is very small. In this case, it may make sense not to use bind variables so that the Oracle query optimizer gets more information to arrive at a better query plan based on the input data.

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