Java Performance: The Definitive Guide: Getting the Most Out by Scott Oaks

By Scott Oaks

Coding and checking out are usually thought of separate parts of workmanship. during this accomplished consultant, writer and Java specialist Scott Oaks takes the strategy that anybody who works with Java will be both adept at figuring out how code behaves within the JVM, in addition to the tunings prone to aid its functionality. you will achieve in-depth wisdom of Java software functionality, utilizing the Java digital laptop (JVM) and the Java platform, together with the language and API. builders and function engineers alike will examine quite a few good points, instruments, and approaches for making improvements to the way in which Java 7 and eight functions practice.

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Extra info for Java Performance: The Definitive Guide: Getting the Most Out of Your Code

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Ideally it can include Java Flight Recorder (JFR) recordings (see Chap‐ ter 3) or other low-impact profiling information, periodic thread stacks, and other heap analysis data like histograms or full heap dumps (though the full heap dumps, in particular, take a lot of space and cannot necessarily be kept long term). info database (including performance reports like Oracle’s Automatic Workload Repos‐ itory [AWR] reports). This data will guide the analysis of any regressions that are uncovered. If the CPU usage has increased, it’s time to consult the profile information to see what is taking more time.

75 seconds The average of the specimen says there is a 25% improvement in the code. How confident can we be that the test really reflects a 25% improvement? Things look good: two of the three specimen values are less than the baseline average, and the size of the improvement is large—yet when the analysis described in this section is performed on those results, it turns out that the probability the specimen and the baseline have the same perfor‐ mance is 43%. When numbers like these are observed, 43% of the time the underlying performance of the two tests are the same.

As applications grow, this maxim becomes both more important to fulfill and more difficult to achieve. Complex systems are more than the sum of their parts; they will behave quite differently when those parts are assembled. Mocking out database calls, for example, may mean that you no longer have to worry about the database perfor‐ mance—and hey, you’re a Java person; why should you have to deal with someone else’s performance problem? But database connections consume lots of heap space for their buffers; networks become saturated when more data is sent over them; code is optimized differently when it calls a simpler set of methods (as opposed to the complex code in a JDBC driver); CPUs pipeline and cache shorter code paths more efficiently than longer code paths; and so on.

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