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As we will be collecting data during the performance test runs, we need to have a clear understanding of the basic statistics, so let s examine them in more detail For the purposes of this book, we define response time as the length of time a client has to wait from the moment it sends a request to the moment it receives the last byte of the response from the application The sets of data that we collect from a performance test run consist of the individual response times of every request that makes up the test script Each request in a test script is executed one after another, by each simulated user for a certain period of time Our base measurement of analysis is the arithmetic mean of the response times for all users of a particular request: the average response time (ART).

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Aggregate average response time (AART) is a measurement that we use extensively when analyzing performance data, and we define it as the sum of the ARTs of every individual request in a test script, divided by the number of requests in that test script Admittedly, AART has no real meaning in terms of how an application is performing, but it does provide an excellent indicator of how loaded the whole system is As such, we sometimes refer to this measurement as the load factor A typical AART curve when plotted against the number of simultaneous users looks as presented in Figure 9-1..

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@Stateless @Name("changeRestAction") public class ChangeRestaurantAction implements IChangeRestaurantAction{ @In String zipCode; @In(required = false) @Out String bestRestaurant; public String findBestRestaurant() { // do work here bestRestaurant = "Aladdins in " + zipCode; return "success"; } public String saveBestRestaurant() { // save restaurant code using bestRestaurant variable return "completePage"; } }

Throughput is not a clear-cut metric in the same way as response time The standard way of expressing throughput is in TPS, and it is vital to understand what a transaction represents in the application being tested It might be a single query or a specific group of queries In a messaging system, it might be a single message; and in a servlet-based application, it might be a request Even when there is consensus as to exactly what is being measured, the values obtained for throughput can often be misinterpreted The reason for this is that many people regard this metric in much the same way as they regard miles per hour : as a measurement of speed In fact, throughput is a measure of capacity We can attempt to explain how throughput works in terms of a supermarket analogy.

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As you can see in this example, we only had to add another method to save the restaurant, and we had to simply add another annotation to declare that the restaurant variable can be inputted as well. Quite the minimal modifications for the performance increase.

Imagine that a supermarket runs a promotion whereby ten shoppers will get for free everything they can put in their shopping carts in 15 minutes The supermarket is the application, the shoppers are analogous to the requests (or messages), and the supermarket staff that are restocking the shelves are analogous to the components of your system that are working to cope with the demand Even if all ten shoppers reach capacity (by completely filling their carts in 15 minutes), it doesn t necessarily mean that they ve taken everything available in the supermarket However, as we increase the number of shoppers, we will reach a point at which there are enough shoppers to empty the supermarket in that time This point is the throughput capacity of the supermarket the point of saturation.

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