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Guidelines for Good Benchmarks
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Many good practices are associated with benchmarking. In this section, I ll take you through a few that I ve found to be helpful in getting the most out of the benchmarking experience. First, you should always consider the concept of before-and-after snapshots. Don t wait until after you ve made a change to the server to see how it compares to the baseline you took six months ago. A lot can happen in six months! Instead, measure the system before the change, make the change, and then measure the system again. This will give you three metrics to compare: how the system is expected to perform, how it performs before the change, and how it performs after the change. You may find that something has taken place that makes your change more or less significant. For example, let s say your benchmarks include a metric for query time. Your baseline established six months ago for a given test query was set at 4.25 seconds. You decide to modify the index of the table being tested. You run your before benchmark and get a value of 15.50, and your after benchmark produces a value of 4.5 seconds. If you had not taken the before picture, you wouldn t have known that your change increased performance dramatically. Instead, you might have concluded that the change caused the query to perform a bit slower which might have led you to undo that change, thus resulting in a return to slower queries. This fictional example exposes several aspects that I want to warn you about. If you are conducting benchmarks on the performance of data retrieval on systems that are expected to
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CHAPTER 4 TEST-DRIVEN MYSQL DEVELOPMENT
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grow in the amount of data stored, you need to run your benchmarks more frequently so that you can map the effects of the growth of data with the performance of the system. In the previous example, you would have considered the before value to be normal for the conditions of the system, such as data load. You should also be careful to ensure your tests are valid for what you are measuring. If you are benchmarking the performance of a query for a table, your benchmarks are targeted at the application level and are not useful for predicting the performance of the system in the general sense. Be sure to segregate application-level benchmarks from the more general metrics to be sure you don t skew your conclusions. Another good practice that is related to the before and after concept is to run your benchmarks several times over a constrained period of activity (under a consistent load) to ensure your benchmarks are not affected by localized activity such as a rogue process or a resource-intensive task. I find running the benchmark up to several dozen times permits me to determine mean values for the results. You can create these aggregates using many techniques. You could use a statistic package to create the basic statistics or use your favorite statistical friendly spreadsheet application.4
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Note Some benchmark tools provide this feature for you. Alas, the MySQL Benchmark Suite does not.
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Perhaps the most useful practice to adopt is the idea of changing one thing at a time. Don t go through your server with a wide brush of changes and expect to conclude anything meaningful from the results. What often happens in this case is one of the six or so changes negatively affects the gains of several others and the remaining ones have little or no effect on performance. Unless you made one change at a time, you would have no idea which affected the system in a negative, positive, or neutral way. You should also use real data whenever possible. Sometimes manufactured data contains data that falls neatly into the ranges of the fields specified and therefore never test certain features of the system (domain and range checking, etc.). If your data can change frequently, you may want to snapshot the data at some point and build your tests using the same set of data each time. While this will ensure you are testing the performance using real data, it may not test performance degradation over time with respect to growth. Lastly, when interpreting the results of your benchmarks and managing your expectations, be sure to set realistic goals. If you are trying to improve the performance of the system under certain conditions, make sure you have a firm grasp of the known consequences before you set your goals. For example, if you are examining the effect of switching the network interface from a gigabit connection to an interface that performs network communication 100 times faster, your server will not perform its data transfer 100 times faster. In this case and ones similar to it, the value added by the hardware should be weighed against the cost of the hardware and the expected gains of using the new hardware. In other words, your server should perform some percentage faster, thereby saving you money (or increasing income).
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4. Some statisticians consider the statistical engine in Microsoft Excel to be inaccurate. However, for the values you are likely to see, the inaccuracies are not a problem.
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