Server-Side Programming Techniques (Java(TM) Performance and Scalability, Volume 1)

This book was written with one goal in mind: to provide Java programmers with the expertise needed to build efficient, scalable Java code. The author shares his experience in server-side performance tuning through measured performance assessments, called optimizations. Each optimization discusses techniques to improve the performance and scalability of your code. Every claim is substantiated with hard numbers and an experience-based evaluation. Java(TM) Performance and Scalability, Volume 1, provides invaluable advice that you will, no doubt, find useful in your coding. Presented in 48 concise lessons that target the most common and critical performance pitfalls, this book offers a plethora of practical tips and solutions for boosting the performance of your programs. These lessons cover performance-critical areas such as memory management, garbage collection, caching, and multithreading. Specific lessons include:

Reserving StringBuffer capacity

Avoiding premature object creation

Creating an efficient vector class

Designing caching into your API

The cost of synchronization

Parallel subtasks

JNI efficiency

Varying the server workload and RMI network plumbing

Using ServletOutputStream

Caching JDBC connections In addition to providing hard numbers that quantify the optimizations, the author concludes the book with an application demonstrating the effectiveness of the performance optimizations. The exercise takes a typical program and increases its performance fourfold through a series of steps that tie together the lessons learned throughout the book. He offers both the means and the proof to better coding.

Dov Bulka has spent fifteen years in the trenches of software development delivering large-scale software products to market. He was the performance architect of the IBM Domino-Go Web server that has powered some of the biggest Web sites ever hosted on the Internet, including that of the 1996 Atlanta Olympics. He received his Ph.D. in computer science from Duke University.

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