2. Tools of the Tuning Trade | WildFly Performance Tuning

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WildFly Performance Tuning - Third Edition

3.8 (6)

WildFly Performance Tuning

3.8 (6)

Overview of this book

Packed with practical examples, this book looks at a different aspect of performance tuning in each chapter and shows you how to apply them to their existing Java applications. Anyone with an interest in learning more and improving the performance of Java-based technology in general, all the way to WildFly in particular, will find this book useful.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. The Science of Performance Tuning

1. The Science of Performance Tuning

Performance

Scalability

Performance tuning anti-patterns

Software development and quality assurance

Software development with performance focus

The iterative performance-tuning process

The environment of performance tests

The software life cycle

Tuning an enterprise stack

Summary

2. Tools of the Tuning Trade

2. Tools of the Tuning Trade

The key features of performance tuning

Profiling

Profiling and sampling

VisualVM

Monitoring

OS tools

WildFly tools

Generating load

Apache JMeter

Summary

3. Tuning the Java Virtual Machine

3. Tuning the Java Virtual Machine

JVM

JVM memory areas

GC

JVM memory management with the GC

Configuring the JVM

Large objects

Large memory pages

The java.lang.OutOfMemoryError error

Memory leaks

Types of GC strategies

Which collector to use

Setting VM parameters in WildFly

Having the relevant information available

VM parameters in production

Using tools

VM and GC stability

Summary

4. Tuning WildFly

4. Tuning WildFly

WildFly's history

WildFly's architecture

Various subsystem configurations

Logging

Summary

5. EJB Tuning in WildFly

5. EJB Tuning in WildFly

The history of EJBs

The different types of EJBs

Performance tuning EJBs in WildFly

Summary

6. Tuning the Persistence Layer

6. Tuning the Persistence Layer

Designing a good database

Tuning the Java Database Connectivity API

Tuning JPA and Hibernate

Summary

7. Tuning the Web Container in WildFly

7. Tuning the Web Container in WildFly

Enter Undertow

Using XNIO

Tuning Undertow

Tuning the servlet container and JSP compilation

Using Apache as a frontend

Summary

8. Tuning Web Applications and Services

8. Tuning Web Applications and Services

Web applications

Services

Summary

9. JMS and HornetQ

9. JMS and HornetQ

Introducing JMS

The message and its optimizations

Tuning the session

Tuning MessageProducer

Optimizing HornetQ

Summary

10. WildFly Clustering

10. WildFly Clustering

Cluster

Load balancing

Replication

Failover, failback, and session state

High Availability

The real need of clustering

A single point of failure

WildFly clustering basics

Clustering in Java EE and WildFly

Summary

Index

Index

Profiling and sampling

In the Java universe, profiling technically involves instrumenting the Java bytecode in order to make accurate calculations of execution down to the method level. Instrumenting the Java bytecode is a time-consuming operation that is performed for all code in the JVM when profiling starts. The amount of instrumented bytecode can, however, be limited by defining what should be included or excluded, thereby speeding up the instrumentation time in profiling. Depending on tool support, theses definitions can be done on package, class, or method level. As the profiling terminates, the bytecode is normally restored.

Sampling is a kind of lightweight profiling. Instead of physically instrumenting the Java bytecode as done in profiling, sampling relies on analyzing the stack traces and thread dumps that are taken at regular time intervals. These intervals can be defined, for example to 10 per second.

So, what approach should you use? As always, the answer is that it depends on...

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