CHAPTER 10 (Yahoo web hosting) JAVA EE PERFORMANCE ASSESSMENT return
CHAPTER 10 JAVA EE PERFORMANCE ASSESSMENT return raw metrics and allow the calling program to derive values from the raw metrics, but the point is that it is all accomplished with a single HTTP call. Again, you are required to deploy an additional Web application to your environment in order to implement such an architecture. Application Recording Application recording involves bytecode instrumentation that traces requests as they happen, so application recording overhead cannot be mitigated by adjusting the time of snapshots. A snapshot of running methods would be almost worthless, but a list of processed requests with their associated call traces aggregated for a specific time interval is incredibly valuable. Therefore, the strategies we employ to mitigate performance overhead when recording application requests are more complicated. They fall into the following categories: Sampling percentage Sampling period/aggregate period Filters Level of detail Custom components I discuss each category in the sections that follow. Sampling Percentage Bytecode instrumentation differs from metric recording in that while metric recording can be performed at regular intervals by taking snapshots (for example, capturing at a specific time how many threads are in use or how many database connections are open), processed requests must be captured when they occur and aggregated at specific intervals. Therefore, your byte- code instrumentation tool should be configurable for the percentage of requests to trace as well as the interval to aggregate request information (that is, report how many times each request, class, and method was executed, and their average, high, and low response times). The higher the percentage of requests traced and the more frequent the aggregate period, the greater the performance overhead. But the higher the percentage of requests traced and the more frequent the aggregate period, the easier it is to accurately isolate a single request for troubleshooting. As with all performance tuning options, it is a balance between overhead and quality of data. Sampling Period/Aggregate Period In addition to configuring the percentage of requests to sample, you need to configure the time period in which to aggregate those samples. Capturing and storing every request is unwieldy from a storage perspective when you can glean significant insight by aggregating those samples over a time period and reporting back the average response time, maximum response time, execution count, and so on. The configuration of this time period, however, can greatly affect the overhead inflicted on the environment. For example, aggregating performance information every ten seconds requires more frequent computations than aggregating performance information every minute. But the trade-off is that if an individual request performs poorly, it is easier to find and analyze it if the aggregate period is small.
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