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Test Correctly - Oracle

This article, the first of three parts, focuses on the design and creation of applications that use the database. It is excerpted from chapter five of the book Oracle Database 10g DBA Handbook, written by Kevin Loney and Bob Bryla (McGraw-Hill/Osborne, 2005; ISBN: 0072231459).

  1. Developing and Implementing Applications
  2. Do As Little As Possible
  3. In Your Application Design, Strive to Avoid Trips to the Database
  4. Go Atomic
  5. Store Data Efficiently at the Block Level
  6. Test Correctly
  7. Standard Deliverables
  8. Tuning Goals for Queries and Transaction Processing
By: McGraw-Hill/Osborne
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February 09, 2006

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In most development methodologies, application testing has multiple phases, including module testing, full system testing, and performance stress testing. Many times, the full system test and performance stress test are not performed adequately due to time constraints as the application nears its delivery deadline. The result is that applications are released into production without any way to guarantee that the functionality and performance of the application as a whole will meet the needs of the users. This is a serious and significant flaw and should not be tolerated by any user of the application. Users do not need just one component of the application to function properly; they need the entire application to work properly in support of a business process. If they cannot do a day’s worth of business in a day, the application fails.

This is a key tenet regarding identifying the need for tuning: If the application slows the speed of the business process, it should be tuned. The tests you perform must be able to determine if the application will hinder the speed of the business process under the expected production load.

Test with Large Volumes of Data

As described earlier in this chapter, objects within the database function differently after they have been used for some time. For example, the pctfree and pctused settings may make it likely that blocks will be only half-used or rows will be chained. Each of these causes performance problems that will only be seen after the application has been used for some time.

A further problem with data volume concerns indexes. As a B*tree index grows in size, it may split internally—the level of entries within the index increases. As a result, you can picture the new level as being an index within the index. The additional level in the index increases the negative effect of the index on data load rates. You will not see this impact until after the index is split. Applications that work acceptably for the first week or two in production only to suddenly falter after the data volume reaches critical levels do not support the business needs. In testing, there is no substitute for production data loaded at production rates while the tables already contain a substantial amount of data.

Test with Many Concurrent Users

Testing with a single user does not reflect the expected production usage of most database applications. You must be able to determine if concurrent users will encounter deadlocks, data consistency issues, or performance problems. For example, suppose an application module uses a work table during its processing. Rows are inserted into the table, manipulated, and then queried. A separate application module does similar processing—and uses the same table. When executed at the same time, the two processes attempt to use each other’s data. Unless you are testing with multiple users executing multiple application functions simultaneously, you may not discover this problem and the business data errors it will generate.

Testing with many concurrent users will also help to identify areas in the application where users frequently use undo segments to complete their queries, thus impacting performance.

Test the Impact of Indexes on Your Load Times

Every insert, update, or delete of an indexed column may be slower than the same transaction against an unindexed table. There are some exceptions—sorted data has much less of an impact, for example—but the rule is generally true. The impact is dependent on your operating environment, the data structures involved, and the degree to which the data is sorted.

How many rows per second can you insert in your environment? Perform a series of simple tests. Create a table with no indexes and insert a large number of rows into it. Repeat the tests to reduce the impact of physical reads on the timing results. Calculate the number of rows inserted per second. In most environments you can insert tens of thousands of rows per second into the database. Perform the same test in your other database environments so you can identify any that are significantly different from the others.

Now consider your application. Are you able to insert rows into your tables via your application at anywhere near the rate you just calculated? Many applications run at less than 5 percent of the rate the environment will support. They are bogged down by unneeded indexes or the type of code design issues described earlier in this chapter. If your application’s load rate decreases—say, from 40 rows per second to 20 rows per second—your tuning focus should not be solely on how that decrease occurred but also on how the application managed to get only 40 rows per second inserted in an environment that supports thousands of rows inserted per second.

Make All Tests Repeatable

Most regulated industries have standards for tests. Their standards are so reasonable that all testing efforts should follow them. Among the standards is that all tests must be repeatable. To be compliant with the standards, you must be able to re-create the data set used, the exact action performed, the exact result expected, and the exact result seen and recorded. Pre-production tests for validation of the application must be performed on the production hardware. Moving the application to different hardware requires retesting the application. The tester and the business users must sign off on all tests.

Most people, on hearing those restrictions, would agree that they are good steps to take in any testing process. Indeed, your business users may be expecting that the people developing the application are following such standards, even if they are not required by the industry. But are they followed? And if not, then why not? The two commonly cited reasons for not following such standards are time and cost. Such tests require planning, personnel resources, business user involvement, and time for execution and documentation. Testing on production-caliber hardware may require the purchase of additional servers. Those are the most evident costs—but what is the business cost of failing to perform such tests? The testing requirements for validated systems in some health industries were implemented because those systems directly impact the integrity of critical products such as the safety of the blood supply. If your business has critical components served by your application (and if it does not, then why are you building the application?), you must consider the costs of insufficient, rushed testing and communicate those potential costs to the business users. The evaluation of the risks of incorrect data or unacceptably slow performance must involve the business users. In turn, that may lead to an extended deadline to support proper testing.

In many cases, the rushed testing cycle occurs because a testing standard was not in place at the start of the project. If there is a consistent, thorough, and well-documented testing standard in place at the enterprise level when the project starts, the testing cycle will be shorter when it is finally executed. Testers will have known long in advance that repeatable data sets will be needed. Templates for tests will be available. If there is an issue with any test result, or if the application needs to be retested following a change, the test can be repeated. Also, the application users will know that the testing is robust enough to simulate the production usage of the application. If the system fails the tests for performance reasons, the problem may be a design issue (as described in the previous sections) or a problem with an individual query.

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