Elastic Caching with IBM WebSphere eXtreme Scale
IBM Redbooks Solution Guide
Published 19 December 2013
Authors: Deb Landon, Jonathan Marshall, John Pape, Kristi Peterson, Greg Reid, Fabio Santos B. da Silva, Federico Senese
IBM® WebSphere® eXtreme Scale provides a solution to scalability issues through caching and grid technology. It provides an enhanced quality of service (QoS) in high performance computing environments.
This IBM Redbooks® Solution Guide introduces WebSphere eXtreme Scale and provides an introduction to its features, benefits, and architecture. This information is intended for architects and implementers who want to implement WebSphere eXtreme Scale solution and need to understand the business value of using WebSphere eXtreme Scale.
IBM® WebSphere® eXtreme Scale provides an extensible framework to simplify the caching of data that is used by an application. It can be used to build a highly scalable, fault-tolerant data grid with virtually unlimited scaling capabilities beyond terabyte capacity. Because its capacity can be dynamically increased to an extreme size, it can be thought of as an elastic cache.
Figure 1 illustrates many business and application challenges that suggest an elastic caching solution. WebSphere eXtreme Scale enables infrastructure with the ability to deal with extreme levels of data processing and performance. When the data and resulting transactions experience incremental or exponential growth, the business performance does not suffer because the grid is easily extended by adding more capacity.
Figure 1. Challenges that lead to an elastic caching solution
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Much of the functionality that is provided by the WebSphere eXtreme Scale software product is also available in an easy-to-install “appliance” called the IBM WebSphere DataPower® XC10 Appliance. The key point to consider about the WebSphere DataPower XC10 Appliance is that, as a pre-configured and secure ready-to-go “black box”, it does not allow you to install any software onto it. You can use its administration console to configure it, but you cannot install any of your own software on it. This situation means that you cannot use some of the more advanced features that you might want to use WebSphere eXtreme Scale for, such as in-line loaders, agents, or parallel grid processing. Each of these advanced features requires that your unique code is available on the grid side.
WebSphere eXtreme Scale is an essential tool for elastic scalability and provides the following valuable benefits:
- Processes massive volumes of transactions with extreme efficiency and linear scalability.
- Rapidly builds a seamless, flexible, and highly available elastic grid that scales out as applications scale, removing the performance limits of the database.
- Provides high availability and security with redundant copies of cache data, and authentication schemas that help ensure system security.
- Enables your existing back-end systems to support significantly more applications, reducing your total cost of ownership (TCO).
The key features of WebSphere eXtreme Scale include the following ones:
- A highly available elastic and scalable grid
- Extreme transaction support
- Easy integration into existing solutions
- Support for JSE, Java Platform, Enterprise Edition, ADO.NET data services, and REST capable client applications
Scalability is the ability of a system to handle an increasing load in a graceful manner. This ability implies that a system can be readily extended. A system has perfectly linear scaling capabilities if doubling its processor capacity also doubles its maximum throughput. In general, there are two ways an IT system can be scaled:
- Horizontally, by adding more hosts to a tier. This is called scale out.
- Vertically, by enlarging the capabilities of a single system, for example, adding processors. This is called scale up.
Consider a classical three-tier application, such as the one shown in Figure 2.
Figure 2. Scaling options in a traditional three-tier application
The application server tier is both scaled out by having three hosts and scaled up by having three application servers on each host. The database tier is scaled up by using a single powerful system with many processors. The database tier is scaled out by having a shadow database that uses log shipping capability to support reports, analysis, and so on.
Scaling is successful if all involved resources can cope with the increased load indefinitely. But at some point a resource reaches its maximum throughput, limiting the overall throughput of the system. This point is called the saturation point, and the limiting resource is called a bottleneck resource.
The obvious approach to solve the scalability challenge is to reduce the number of requests that are made to the bottlenecked resource or resources. Often, the most practical way to accomplish this task is by introducing a cache. A cache can be defined as a copy of frequently accessed data that is held in a relatively nearby location, such as within process memory. The intent of any caching mechanism is to reduce response time by reducing access time to data, and to increase scalability by reducing the number of requests to a constrained resource.
Virtually every major business application incorporates some caching techniques. A frequent example is a database cache, as illustrated in Figure 3. Here, the cache sits between the application and the database to reduce the load on the database.
Figure 3. Introducing caching as a response to the scalability challenge
A cache is used to hold the syntax of prior requests to the resource and its response. This situation allows subsequent requests of the same syntax to be quickly served up from the cache rather than making another request to the resource. Caches can be introduced at any tier, and in multiple places within each tier. In an end-to-end request from a browser to an e-commerce system, for example, there might be significant caching that is used:
- Within the browser, for images and CSS files.
- In a caching proxy within the user’s company intranet, for identical image requests made by other users in that company.
- In an edge-of-network service as provided by, for example, Akamai.
- At the web server tier, for static content.
- In the application server tier, for caching various oft-used items, and copies of browser pages or page fragments already rendered for a request.
- In the database, caching past read and write data to avoid having to read them from disk.
- At other back-end systems that are used to complete the request. For example, you can use caches in a server that is used to handle a web service call to return appropriate municipal and state taxes for a locale.
Caching is a universally accepted and commonly implemented practice to increase performance while reducing system requirements. The appropriate uses of caching are ever expanding.
After you determine that caching is valuable at a particular tier, the next decision is where to hold the cached data. Usually, the easiest and fastest place is within the local addressable memory of the component that is using the cache. However, being easiest does not make it correct for every circumstance.
At the application server tier in particular, the amount of cached data can be substantial, in the hundreds of megabytes. It often includes caching of HTTP session data, servlet/JSP page fragments, commands, inventory, prices, and many other application objects, all working to reduce back-end system loads while improving responsiveness. If the number or sizes of these caches can be increased, the cache hit ratio can be further improved, saving even more calls to constrained back-end resources.
The amount of cached data at the application tier can lead to several issues:
- How to ensure all Java virtual machines (JVMs) within the application server tier have a consistent copy of each cached object.
- How to propagate a cache invalidation from one JVM to the other JVMs so that none of them use a stale cache entry.
- Performing both of the above tasks in a timely manner so that caches are synchronized as changes are made.
- How to hold all of the cached data within the limited application server JVM heap size.
- Excessive garbage collection (GC) pause times because of increased application server JVM heap size.
In a modern WebSphere Application Server Network Deployment environment, built-in Data Replication Service (DRS) features are available to address the first three bullets. But this service comes at extra cost in terms of processor and network bandwidth usage. As the number of application server JVMs in the cluster is increased, the DRS “network chatter” to keep all of the caches synchronized can become a significant portion of the overall work that is done by each JVM. A point of negative return can be reached, where adding another application server JVM to the cluster decreases the responsiveness of the overall application.
Furthermore, if the cached data is held within the application server’s JVM heap, it can become constrained in the total size of its caches. In 32-bit mode, there is an addressability limit of around 2 GB as a usable heap size. Switching to 64-bit mode is not a cure either, as there can be excessive GC impact as heap sizes grow over 4 GB or more. As the caches grow bigger, GC activity and pause times can rise to 10% or more of the total processor usage. Users can also experience excessive response time delays when lengthy full-GC operations freeze the entire JVM, sometimes for several seconds at a time.
WebSphere eXtreme Scale provides an extensible framework to simplify the caching of data that is used by an application. It can be used to build a highly scalable, fault-tolerant data grid with virtually unlimited scaling capabilities beyond terabyte capacity. Because its capacity can be dynamically increased to an extreme size, it can be thought of as an elastic cache. WebSphere eXtreme Scale enables infrastructure with the ability to deal with extreme levels of data processing and performance. When the data and resulting transactions experience incremental or exponential growth, the business performance does not suffer because the grid is easily extended by adding more capacity (JVMs and hardware).
Integrating WebSphere eXtreme Scale with other middleware
Business applications are not the only way to take advantage of the features of WebSphere eXtreme Scale. Several IBM products can also take advantage of these performance and scalability enhancements:
- IBM WebSphere Commerce
- IBM WebSphere Portal Server
- IBM WebSphere Application Server
- IBM WebSphere Business Events
- IBM Rational® Jazz™
WebSphere eXtreme Scale addresses two major concerns for scalability: cost savings and adaptation to growing business needs.
Integration of applications and application serving products with WebSphere eXtreme Scale can help you scale your solution with minimal cost. Specifically, major cost savings can be realized in session offloading and dynamic cache offloading.
- Session offloading
- Session offloading enhances efficiency in application performance and minimizes the need for additional JVM capacity to hold the session data. A specific example of this can be seen in the integration of WebSphere eXtreme Scale and WebSphere Portal Server. Offloading sessions from session heavy customer portlets can reduce the need for storage in the JVMs, thus increasing the efficiency of each JVM and reducing the need for additional Portal JVMs (and additional Portal licenses).
- Dynamic cache offloading
- The dynamic cache service in WebSphere Application Server is used extensively by WebSphere Commerce Server for caching pages, catalogs, and other application-critical objects. Offloading the dynamic cache from the JVM memory into eXtreme Scale grids reduces the load on the Commerce JVMs, reducing the need for additional JVMs and additional Commerce server licenses.
Adapting to growing business needs
Adapting your application infrastructure according to the future needs of your business is critical and requires precise engineering. Designing your scalability solution to be dynamic enough to keep pace with your growing business requirements is important for keeping your business competitive. WebSphere eXtreme Scale provides you with the capability to do this task.
Two major areas where eXtreme Scale can provide benefits for meeting business growth requirements are in extreme transaction processing and complex event processing.
- Extreme transaction processing
- Scalability that is combined with performance is something every business wants. Conducting transactions using WebSphere eXtreme Scale caching is much faster than transactions that span multiple back-end systems. Although positioning WebSphere eXtreme Scale for extreme transaction processing requires changes to your existing architecture, the cost of this change is minimal compared to the benefits that can be realized over the long run.
- Complex event processing
- WebSphere eXtreme Scale can be positioned as the first layer in event processing architectures to filter out events that are not business relevant. WebSphere eXtreme Scale also provide event enrichment by efficiently caching event-related data. Using WebSphere eXtreme Scale increases the scalability of the event processing platform by leveraging a routing optimization technique. The integration of WebSphere eXtreme Scale and WebSphere Business Events is a good example of this use.
You can order WebSphere eXtreme Scale, by accessing the WebSphere eXtreme Scale product page and clicking Ready to Buy at the following website:
For more information, see the following documents:
- WebSphere eXtreme Scale V8.6: Key Concepts and Usage Scenarios, SG24-7683
- Scalable, Integrated Solutions for Elastic Caching Using IBM WebSphere eXtreme Scale, SG24-7926
- WebSphere eXtreme Scale Best Practices for Operation and Management, SG24-7964
- Enterprise Caching Solutions using IBM WebSphere DataPower SOA Appliances and IBM WebSphere eXtreme Scale, SG24-8043
- WebSphere eXtreme Scale product page
- IBM Offering Information page (announcement letters and sales manuals):
On this page, enter WebSphere eXtreme Scale, select the information type, and then click Search. On the next page, narrow your search results by geography and language.
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This material has not been submitted to any formal IBM test and is published AS IS. It has not been the subject of rigorous review. IBM assumes no responsibility for its accuracy or completeness. The use of this information or the implementation of any of these techniques is a client responsibility and depends upon the client's ability to evaluate and integrate them into the client's operational environment.
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