Book Image

IBM WebSphere eXtreme Scale 6

By : Anthony Chaves
Book Image

IBM WebSphere eXtreme Scale 6

By: Anthony Chaves

Overview of this book

A data grid is a means of combining computing resources. Data grids provide a way to distribute object storage and add capacity on demand in the form of CPU, memory, and network resources from additional servers. All three resource types play an important role in how fast data can be processed, and how much data can be processed at once. WebSphere eXtreme Scale provides a solution to scalability issues through caching and grid technology. Working with a data grid requires new approaches to writing highly scalable software; this book covers both the practical eXtreme Scale libraries and design patterns that will help you build scalable software. Starting with a blank slate, this book assumes you don't have experience with IBM WebSphere eXtreme Scale. It is a tutorial-style guide detailing the installation of WebSphere eXtreme Scale right through to using the developer libraries. It covers installation and configuration, and discusses the reasons why a data grid is a viable middleware layer. It also covers many different ways of interacting with objects in eXtreme Scale. It will also show you how to use eXtreme Scale in new projects, and integrate it with relational databases and existing applications. This book covers the ObjectMap, Entity, and Query APIs for interacting with objects in the grid. It shows client/server configurations and interactions, as well as the powerful DataGrid API. DataGrid allows us to send code into the grid, which can be run where the data lives. Equally important are the design patterns that go alongside using a data grid. This book covers the major concepts you need to know that prevent your client application from becoming a performance bottleneck. By the end of the book, you'll be able to write software using the eXtreme Scale APIs, and take advantage of a linearly scalable middleware layer.
Table of Contents (15 chapters)
IBM WebSphere eXtreme Scale 6
Credits
About the Author
About the Reviewers
Preface

Removal versus eviction


Setting an eviction policy on a BackingMap makes more sense now that we're using a Loader. Imagine that our cache holds only a fraction of the total data stored in the database. Under heavy load, the cache is constantly asked to hold more and more data, but it operates at capacity. What happens when we ask the cache to hold on to one more payment? The BackingMap needs to remove some payments in order to make room for more.

BackingMaps have three basic eviction policies: LRU (least-recently used), LFU (least-frequently used), and TTL (time-to-live). Each policy tells the BackingMap which objects should be removed in order to make room for more. In the event that an object is evicted from the cache, its status in the database is not changed. With eviction, objects enter and leave the cache due to cache misses and evictions innumerable times, and their presence in the database remains unchanged.

The only thing that affects an object in the database is an explicit call...