CHAPTER 10 JAVA EE PERFORMANCE ASSESSMENT against (Web server certificate)
CHAPTER 10 JAVA EE PERFORMANCE ASSESSMENT against a preproduction environment are not fruitful you can only tune your application to the user load it is subjected to. In this section, you ll learn how to mitigate the performance impact of monitoring in a production environment and look at the following topics: Identifying the best time intervals to record data Choosing the correct subset of your production environment to monitor Recording production data using a staged approach Configuring the recording to compute metrics effectively Recording at the Right Intervals The first consideration when recording live production data is when to record it. The target is to identify a period of time within a day or a week with average user load, when users are performing actions representative of their typical behavior. For example, in the case of an intranet application that requires users to log on in the morning, perform daily activities, and log off before they leave, the login and logoff hours of the day are less representative of the majority of user actions a user logs in once but may generate a couple dozen reports throughout the day. Therefore, recording a 30-minute session with users logging in may distort your tuning efforts, causing you to spend too much time tuning seldom-used functionality while missing true performance tuning opportunities. An access log analyzer or user experience monitor can help you pinpoint the best opportunities during the day or week to capture average user activities. The time period you are looking for is when the user load is average you do not want a dramatically under- or overutilized time. If the application is underutilized, then you risk missing problems that only manifest under load; if the application is overutilized, then you run the risk that the monitoring overhead may negatively affect end users experience. The ideal time period exhibits the following characteristics: Eighty percent or more of the most frequently executed requests are being performed. User load is within one standard deviation of the mean user load. The key to effectively identifying this time period is to perform historical analysis of your log files or user experience monitor over a significant time period. Although the analysis of a single day s activity may reveal a seemingly ideal recording period, you can only confirm that period as ideal after looking at the entire week or even the entire month. When implementing proactive tuning measures, you want to ensure that you choose the appropriate recording time window to maximize your tuning efforts. Note Although in the big picture, choosing the correct recording interval is required to maximize tuning efforts, I have never let this point be a sticking point for me. Most companies know approximately when user activity is representative of typical behavior, so while I am sure to later validate the interval as representative, I follow the lead of the companies I m working with and record and analyze data at the intervals they identify.
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