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Java EE 5 Performance Management and Optimization

  • June 30, 2006
  • By Steven Haines
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Object Life Cycle Management

The most significant problem plaguing production enterprise Java applications is memory management. The root cause of 90 percent of my customers’ problems is memory related and can manifest in one of two ways:

  • Object cycling
  • Loitering objects (lingering object references)

Recall that object cycling is the rapid creation and deletion of objects in a short period of time that causes the frequency of garbage collection to increase and may result in tenuring short-lived objects prematurely. The cause of loitering objects is poor object management; the application developer does not explicitly know when an object should be released from memory, so the reference is maintained. Loitering objects are the result of an application developer failing to release object references at the correct time. This is a failure to understand the impact of reference management on application performance. This condition results in an overabundance of objects residing in memory, which can have the following effects:

  • Garbage collection may run slower, because more live objects must be examined.
  • Garbage collection can become less effective at reclaiming objects.
  • Swapping on the physical machine can result, because less physical memory is available for other processes to use.

Neglecting object life cycle management can result in memory leaks and eventually application server crashes. I discuss techniques for detecting and avoiding object cycling later in this article, because it is a development or design issue, but object life cycle management is an architectural issue.

To avoid loitering objects, take control of the management of object life cycles by defining object life cycles inside use cases. I am not advocating that each use case should define every int, boolean, and float that will be created in the code to satisfy the use case; rather, each use case needs to define the major application-level components upon which it depends. For example, in the Patient Management System, daily summary reports may be generated every evening that detail patient metrics such as the number of cases of heart disease identified this year and the common patient profile attributes for each. This report would be costly to build on a per-request basis, so the architects of the system may dictate that the report needs to be cached at the application level (or in the application scope so that all requests can access it).

Defining use case dependencies and application-level object life cycles provides a deeper understanding of what should and should not be in the heap at any given time. Here are some guidelines to help you identify application-level objects that need to be explicitly called out and mapped to use cases in a dependency matrix:

  • Expensive objects, in terms of both allocated size as well as allocation time, that will be accessed by multiple users
  • Commonly accessed data
  • Nontemporal user session objects
  • Global counters and statistics management objects
  • Global configuration options

The most common examples of application-level object candidates are frequently accessed business objects, such as those stored in a cache. If your application uses entity beans, then you need to carefully determine the size of the entity bean cache by examining use cases; this can be extrapolated to apply to any caching infrastructure. The point is that if you are caching data in the heap to satisfy specific use cases, then you need to determine how much data is required to satisfy the use cases. And if anyone questions the memory footprint, then you can trace it directly back to the use cases.

The other half of the practice of object life cycle management is defining when objects should be removed from memory. In the previous example, the medical summary report is updated every evening, so at that point the old report should be removed from memory to make room for the new report. Knowing when to remove objects is probably more important than knowing when to create objects. If an object is not already in memory, then you can create it, but if it is in memory and no one needs it anymore, then that memory is lost forever.

Application Session Management

Just as memory mismanagement is the most prevalent issue impacting the performance of enterprise Java applications, HTTP sessions are by far the biggest culprit in memory abuse. HTTP is a stateless protocol, and as such the conversation between the Web client and Web server terminates at the conclusion of a single request: the Web client submits a request to the Web server (most commonly GET or POST), and then the Web server performs its business logic, constructs a response, and returns the response to the Web client. This ends the Web conversation and terminates the relationship between client and server.

In order to sustain a long-term conversation between a Web client and Web server, the Web server constructs a unique identifier for the client and includes it with its response to the request; internally the Web server maintains all user data and associates it with that identifier. On subsequent requests, the client submits this unique identifier to identify itself to the Web server.

This sounds like a good idea, but it creates the following problem: if the HTTP protocol is truly stateless and the conversation between Web client and Web server can only be renewed by a client interaction, then what does the Web server do with the client’s information if that client never returns? Obviously, the Web server throws the information away, but the real question relates to how long the Web server should keep the information.

All application servers provide a session time-out value that constrains the amount of time user data is maintained. When the user makes any request from the server, the user’s time-out is reset, and once the time-out has been exceeded, the user’s stateful information is discarded. A practical example of this is logging in to your online banking application. You can view your account balances, transfer funds, and pay bills, but if you sit idle for too long, you are forced to log in again. The session time-out period for a banking application is usually quite short for security reasons (for example, if you log in to your bank account and then leave your computer unattended to go to a meeting, you do not want someone else who wanders by your desk to be able to access your account). On the other hand, when you shop at Amazon.com, you can add items to your shopping cart and return six months later to see that old book on DNA synthesis and methylation that you still do not have time to read sitting there. Amazon.com uses a more advanced infrastructure to support this feature (and a heck of a lot of hardware and memory), but the question remains: how long should you hold on to data between user requests before discarding it?

The definitive time-out value must come from the application business owner. He or she may have specific, legally binding commitments with end users and business partners. But an application technical owner can control the quantity of data that is held resident in memory for each user. In the aforementioned example, do you think that Amazon.com maintains everyone’s shopping cart in memory for all time? I suspect that shopping cart data is maintained in memory for a fixed session length, and afterward persisted to a database for later retrieval.

As a general guideline, sessions should be as small as possible while still realizing the benefits of being resident in memory. I usually maintain temporal data describing what the user does in a particular session, such as the page the user came from, the options the user has enabled, and so on. More significant data, such as objects stored in a shopping cart, opened reports, or partial result sets, are best stored in stateful session beans, because rather than being maintained in a hash map that can conceivably grow indefinitely like HTTP session objects, stateful session beans are stored in predefined caches. The size of stateful session bean caches can be defined upon deployment, on a per-bean basis, and hence assert an upper limit on memory consumption. When the cache is full, to add a new bean to it, an existing bean must be selected and written out to persistent storage. The danger is that if the cache is sized too small, the maintenance of the cache can outweigh the benefits of having the cache in the first place. If your sessions are heavy and your user load is large, then this upper limit can prevent your application servers from crashing.


Here you learned how to integrate proactive performance testing throughout the development life cycle. The process begins by integrating performance criteria into use cases, which involves modifying use cases to include specific SLA sections that include performance criteria for each use case scenario.

About the Author

Steven Haines is the author of three Java books: The Java Reference Guide (InformIT/Pearson, 2005), Java 2 Primer Plus (SAMS, 2002), and Java 2 From Scratch (QUE, 1999). In addition to contributing chapters and coauthoring other books, as well as technical editing countless software publications, he is also the Java Host on InformIT.com. As an educator, he has taught all aspects of Java at Learning Tree University as well as at the University of California, Irvine. By day he works as a Java EE 5 Performance Architect at Quest Software, defining performance tuning and monitoring software as well as managing and performing Java EE 5 performance tuning engagements for large-scale Java EE 5 deployments, including those of several Fortune 500 companies.

Source of this material

Pro Java EE 5 Performance Management and Optimization
By Steven Haines

Published: May 2006, Paperback: 424 pages
Published by Apress
ISBN: 1590596102
Retail price: $49.99
This material is from Chapter 5 of the book.

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