Measures of success in Java

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Measures of success
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9.1 Measures of success
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Before we shift into high gear, let s first nail down a definition of performance as a measurement. In general, two ways exist for viewing performance: response time and throughput. We tune and test applications differently, depending on the aspect on which we re focusing our improvements.
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9.1.1 Response time
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The response time of our application refers to the speed at which the application is able to service a given request, such as a user requesting a web page through a browser. The request may be serviced by any number of resources in our application, including servlets, EJB, a database, or a legacy system. We can manage certain types of resources by placing a limit on the maximum number of concurrent requests each resource can safely handle. That is, managed resources are control valves that help us throttle the application for consistent performance and stability. Consequently, each time a request requires the use of a managed resource, it may need to wait in a queue until the resource is available. Take, for example, a limited resource familiar to most enterprise developers database connections. Figure 9.1 (a) shows a database connection servicing a request for data. In this case, the database connection pool is sized with ample available connections capable of servicing requests without queuing. So no cost is incurred in waiting for a database connection to become available. In contrast, figure 9.1 (b) shows a queue of active requests waiting to be serviced by a single database connection. In this case, the size of the database connection pool is not able to keep up with the number of new requests without queuing. Step right up and take a number! From figure 9.1, we can infer that the response time of a request will include any time spent waiting in the request queue for an available database connection. The response time may also include any network latency in obtaining a database connection via a remote call. Furthermore, as concurrent requests for a database
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Request queue Database Connection Database Connection
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Figure 9.1 Database connections are examples of managed resources that may cause incoming requests to be queued before being serviced. Queuing incurs additional response time overhead.
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connection increase, more requests will be queued, waiting for the connection. Therefore, to characterize the response time of our application accurately, we must take two essential measurements: the response time for a single request and the response time for the same request under a load of concurrent requests. In section 9.7.2, we ll roll up our sleeves and write automated tests that measure the response time of a use case from our application. By continually running these tests, we should gain confidence, knowing that any optimizations we make have indeed improved response time. For now, let s begin by considering the possible measurements of such tests.
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9.1.2 Throughput
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While response time focuses on the speed of a specific request, throughput measures the number of requests our application can service in a given amount of time. For EJB applications, throughput is typically measured as the number of business transactions per second (tps). What constitutes an average business transaction is certainly application-specific, so throughput metrics always must be taken in appropriate context. For example, our application might be capable of processing 10 product catalog queries per second, with each query returning an average of five products. From a slightly different angle, we use throughput as an indicator of our application s potential to scale. Scalability is a measure of a load s affect on our application s performance. For example, if we say that our application can scale to handle five concurrent users, then we re referring to the application s ability to maintain a linear (not exponential) average response time for each user, while under the stress of a five-user load. Applications that scale well can deliver increasingly higher levels of throughput by adding resources, such as more hardware or more connections within a pool. When the average response time of a business transaction becomes intolerable under load, the application has reached its maximum effective throughput. Stressing the application beyond this point by piling on a heavier load will further degrade its responsiveness. Revisiting our example of database connections as managed resources, figure 9.2 shows how a database connection pool can be used to work off requests efficiently. As the number of concurrent requests increases, the single database connection in figure 9.2 (a) will eventually hit a wall. Try as it might, the connection won t be able to keep up with the number of pending requests in the queue. Consequently, the request queue will continue to grow, adding to the response time of all waiting requests. By tuning the size of the database connection pool to include two
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