Java Performance: The Definitive Guide by Scott Oaks

By Scott Oaks

Coding and trying out are usually thought of separate parts of craftsmanship. during this accomplished consultant, writer and Java specialist Scott Oaks takes the procedure that anybody who works with Java might be both adept at figuring out how code behaves within the JVM, in addition to the tunings prone to support its performance.

You'll achieve in-depth wisdom of Java program functionality, utilizing the Java digital laptop (JVM) and the Java platform, together with the language and API. builders and function engineers alike will research quite a few positive factors, instruments, and tactics for bettering the best way Java 7 and eight purposes perform.
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Additional info for Java Performance: The Definitive Guide

Example text

Tests all along that continuum have some merit, and choosing the tests along that continuum is one place where experience and intuition will come into play. However, in the end there can be no substitute for testing a full application as it is deployed in production; only then can the full effect of all performance-related issues be understood. Similarly, understanding what is and is not an actual regression in code is not always a black-and-white issue. Programs always exhibit random behavior, and once random‐ ness is injected into the equation, we will never be 100% certain about what data means.

JPA, for example, will typically cache data it has read from the database (see Chap‐ ter 11); the second time that data is used, the operation will often be faster since the data can be obtained from the cache rather than requiring a trip to the database. Similarly, when an application reads a file, the operating system typically pages that file into mem‐ ory. , in a loop to measure performance) will run faster the second time, since the data already resides in the computer’s main memory and needn’t actually be read from disk.

The usual reason a test is statistically inconclusive is that there isn’t enough data in the samples. So far, the example here has looked at a series with three results in the baseline and the specimen. 5 seconds for the specimen? 19: the probability that the results are different has risen from 57% to 81%. Running additional tests and adding the three data points again increases the probability to 91%—past the usual level of statistical signifi‐ cance. Running additional tests until a level of statistical significance is achieved isn’t always practical.

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