Dr. Eoin Lane, Senior Solution Engineer, is the lead for harvesting and developing of application pattern from key IBM SOA engagements and driving those patterns through IBM pattern governance process to accelerate adoption. Eoin also specializes in Model Driven Development (MDD), asset based development and Reusable Asset Specification (RAS) to facilitate SOA development.

Among the school children

A couple of weeks ago I was giving a talk on enabling (pattern) asset consumability at an IBM hosted best practices conference down in Palisades, New York. The talk was scheduled to be at the end of the day so I knew my audience brains would be fried and overload by technical content by this time. Also, the title of my talk enabling (pattern) asset consumability would make the best of us yawn. So I decided on a different tack here, I am still not sure how successful it was, since only a handful of poor souls made it to my talk, but here it is anyway.

Recently I have read two excellent book on speech giving. The first is Give Your Speech, Change the World and the second on is Make to Stick. Now where there first book is excellent in detailing how to actually give presentation, the second book excels in detailing how to make your messages, in those presentations, stick to your audience. So for my talk I decided I would shake things up and open with a poem in a effort to exercise the right side of my their brains. The poem titled "Among the school children" from one my favorite poets W. B Yeats. Now this poem is long over 8 stanzas, so I just focused on one of the stanzas, here it is:

Plato thought nature but a spume that plays
Upon a ghostly paradigm of things;
Solider Aristotle played the taws
Upon the bottom of a king of kings;
World-famous golden-thighed Pythagoras
Fingered upon a fiddle-stick or strings
What a star sang and careless Muses heard:
Old clothes upon old sticks to scare a bird.

Here Yeats is examining the philosophies of some of the worlds famous philosophers from Plato to Pythagoras, yet with all their knowledge and insights, they all still withered and died away. In his later poetry, Yeats uses this recurring theme of a scarecrow to symbolize his own mortality. Here from the same poem:

Better to smile on all that smile, and show
There is a comfortable kind of old scarecrow.

As I said I am not sure how successful this technique was, my audience of five just was enough to swing it either way, but I would certainly be interested to know of other unconventional speech giving techniques that you have used in the past that have worked?

We will be publishing a paper on this talk soon and will post the link as soon as it becomes available.

A common message

1. The first best practice is simply to have one. The business and the IT need to agree upon an enterprise message format, or lingua franca across the organization. Thus what was traditionally a silo-ed organization in terms of personal and commercial insurance (each has a difference via of what a customer/party is) could now start to share service based on a common understanding about what what was a claim, a policy or indeed a customer/party. This really is not news to anyone starting to mature in the SOA space, however, the next best practice is a bit trickier.
2. The second best practice is not to build this enterprise common message model yourself. This can not be overstated. Building a enterprise strength common message model in a big job. To get a sense as to how big, consider IBM Insurance Application Architecture or IAA models. Now I am not here to plug these models, but they have been built up over ten years worth of experience, of practitioners, in the insurance industry. As a result these models are highly normalized and contain some of the best patterns and lessons learned and are what I would consider a mature enterprise model. There are many other enterprise strength message models out there such as ACORD, SID (telco), STAR (automotive) . One also needs to differentiate between industry standard models and priority models and here again I want to outline a best practices in terms of their usage. Industry standard model such as ACORD have been debated and agreed upon by concerned parties within an industry vertical, these models are there typically to level the playing field and provide a good, typically flat, model structure to enable B2B exchanges between companies in that particular business vertical. Priority models on the other hand such as IAA and IBM Information FrameWork Service Models (IFW), are highly normalized models, and are there to provide a company with core differentiation within the marketplace. The best practices then become to use a priority model internally within the organization, to define the internal services and as a lingua franca for the messaging infrastructure and then to use one or more industry standard models to define services that are exposed external to the organization and typically involved in B2B exchanges and delegate to the message infrastructure to convert between them.
3. Finally the enterprise message model must be managed and governed. New versions of these message model are being released all the time as new markets and opportunities arise and your company needs a coherent and consistent way of managing this change. Let us go back to our UN example and imaging that the Irish language was continuously changing (it is not, the Irish language in a considered a dead language), think of the nightmare for the interpreters having to continuously learn a new version of the common language.

For those you foolish enough to want to learn Irish, to champion the cause of having the UN adopt it as a common international language, if refer you to Des Bishop web site

Slán go fóill...

SOA information as a service pattern specifications

Guenter Sauter et al have three excellent SOA information as a service pattern specifications papers on data federation, data consolidation, and data cleansing. You can find these three papers on developerWorks:

• Data federation pattern: The data federation pattern virtualizes data from multiple disparate information sources. The pattern creates an integrated view into distributed information without creating data redundancy while federating both structured and unstructured information. This article describes the federation of structured information (data) with a focus on the SOA context. This pattern specification helps data and application architects make informed decisions on data architecture and document decision guidelines.
• Data consolidation pattern: The data consolidation pattern specification helps data and application architects make informed architectural decisions and improve decision guidelines. In this article you will see how you can apply the pattern in the SOA context. The primary business driver for the data consolidation pattern, also referred to as the data population pattern, is to gather and reconcile data from multiple data sources before this information is needed. To do so, it extracts data from one or more sources, transforms that data into the desired target format, and loads it into some persistent data target. The prepopulation of a persistent target is a key differentiator between this pattern and the data federation pattern.
• Data cleansing pattern: The data cleansing pattern specifies rules for data standardization, matching, and survivorship that enforce consistency and quality on the data it is applied against. The data cleansing pattern validates the data (whether persistent or transient) against the cleansing rules and then applies changes to enforce consistency and quality. In this article, you learn to apply the data cleansing pattern within a Service-Oriented Architecture (SOA) context. This pattern specification helps you, as a data or application architect, to make informed architectural decisions and to improve decision guidelines.

Services-based enterprise integration patterns made easy

A new artilce from Dr. Waseem Roshen, in the series titled: Services-based enterprise integration patterns made easy, appeared today on dW SOA and Web services zone. Here is the abstract:

This series of articles explains services-based enterprise integration patterns in an easy-to-understand, step-by-step way. In this installment, Part 1 of the series, you learn about the two earliest integration patterns—data sharing only and remote procedure call (RPC)—which help introduce the concepts of service provider and service consumer, platform independence, and connectivity. Exploring RPC helps you get familiar with the basic steps necessary for two applications to share functionality. This article also includes a general description of the concepts of loose coupling, code reuse, and layering and componentization. Part 2 of the series will continue the discussion of the early patterns, while Parts 3 and 4 cover the Service-Oriented Architecture (SOA)-based integration patterns, including examples.

The asset graveyard

One of my favorite movies of all time is Sergio Leone spaghetti western classic: The Good, The Bad and the Ugly. Part of the appeal of this movie is the simplicity of the plot; at its heart it is, a treasure hunt, with the three main characters, the good, the bad and the ugly hunting for confederate gold. Between them they know the location of the treasure, buried in a grave in a cemetery, yet only their combined knowledge will lead them to the prize. Towards the end of the film the Ugly a character called Tuco, played by Eli Wallach, comes upon the graveyard, called Sad Hill, and here in one of cinema truly great moments, backed by a unforgettable score, he begins his frantic and haphazard search for the grave of Arch Stanton.

What is impressive about this scene is the sheer scale and size of the graveyard itself, a conservative estimate would put it at over 2000 graves, arranged in a circular fashion. Tuco starts from the center of Sad Hill, where the graves are oldest, and works his way outward initially in concentric circles but soon his search becoming more haphazard as he despairs at the magnitude of the task. Finally, after a dizzy and Groundhog Day like scene of multiple graves passing by he come upon the grave of Arch Stanton, only to find it devoid of any gold, indeed all that remains is a rotting corpse.

Today, this story is echoed in ever IT projects I have ever been a part of, one of my favorites is from within my own group in IBM. It should be noted here that our team is geographically dispersed so although we talk on the phone regularly,we do not get the chance to chew the fat at lunch or around a water cooler. A good friend and colleague of mine on our team, lets call him Bob, was leading the engineering of one the first SOA service registry for an automotive customer in California. One requirement the customer had for the service registry that it performs within certain limits. Unknown to Bob, our group has tackled a very similar problem a couple of years earlier on another engagement. We realized the importance of this work and created a number of assets around it. These assets feel under the broad classification of the requester side caching pattern of which we created pattern specifications, pattern implementation, pattern documentation (including, dW articles, flash movies and a detailed case study). A full listing of all of these and other pattern asset related articles can be found on my home page.

Bob unaware of the previous asset work we had done in this space and having I am sure ran in circles around his own repository graveyard a number of times, he did what any other good architect would do, he started from scratch and reengineered the exact same asset as we had two years earlier.

Just like Tuco desperately searching Sad Hill for the confederate gold, Bob too faced the same dilemma, in fact I have witness that same dilemma on every IT engagement and software project I are involved in. I dare say that this dilemma is not evenexclusive to IT but that you will find the exact same problem is almost every human endeavor whether it be an engineer building a rocket to put a man on mars to a lawyer drafting claims for a patent application, How do we provide the right assets,the content, to help solve the problem at hand, the context.In other words, how do we automate a context to content mapping to suggest the best assets to our architects, engineers, lawyers to help them?

IBM Redbook on Strategic Reuse with Asset-Based Development

A good friend of mine Lee Ackerman let me know last week that a IBM have just publicly released a draft of a new Redbook, titled: "Strategic Reuse with Asset-Based Development". The book is available from: http://www.redbooks.ibm.com/redpieces/abstracts/sg247529.html?Open

The Redbook is structured as follows:

• Chapter 1. Introduction to Asset-Based Development
• Chapter 2. ZYX Electronics case study
• Chapter 3. Tools in support of Asset-Based Development
• Chapter 4. Introduction to the IBM Rational Unified Process
• Chapter 5. Asset-Based Development and Asset Governance Rational Method Composer plug-ins
• Chapter 6. Value, impact and return on investment

• Chapter 7. Adopting Asset-Based Development
• Chapter 8. Configure your asset repository
• Chapter 9. Produce reusable assets
• Chapter 10. Consume reusable assets
• Chapter 11. Manage reusable assets

• Chapter 12. Introduction to service assets
• Chapter 13. Producing service assets
• Chapter 14. Consuming service assets
• Chapter 15. Managing service assets

• Chapter 16. Introduction to patterns
• Chapter 17. Produce: model-to-text pattern implementations
• Chapter 18. Produce: model to model
• Chapter 19. Produce: packaging for deployment
• Chapter 20. Consuming pattern implementations
• Chapter 21. Managing pattern implementation assets

• Appendix A. Rational Asset Manager: installation and administration
• Appendix B. Integrating Asset-Based Development into a process
• Appendix C. Additional material

The value and use of Rational Data Architect in SOA

The IBM Information management yesterday announced that the paper "The value and use of Rational Data Architect in SOA" was published today as part of the series "The Information perspective of SOA design"

Here is the abstract from that paper:

Discover how you can use the IBM® Rational® Data Architect, IBM Industry Models and the unified metadata management of IBM Information Server to align process, service, and data models. Use these tools to accelerate your SOA project. The fifth part of "The information perspective of SOA design" series describes the key features of the products that support the data modeling pattern in SOA.

Pattern (reusable) assets classification

The value of applying the canonical modeling pattern in SOA

Today the IBM Information management people published another articles in a series called The Information perspective of SOA design. This article is titled The value of applying the canonical modeling pattern in SOA. Here is the abstract from that paper:

Discover the approach and value of canonical modeling in SOA design. See how the canonical data models can be aligned in SOA with canonical message models. In this fourth article in the "Information Aspect of SOA Related Design" series, learn about the concept's underlying data and message modeling regardless of the technology and tool choices. A future article in this series describes how various IBM® software products can be used to implement the concepts described here.

If you have any questions or suggestions, feel free to contact the authors of the articles in this series Brian Byrne, John Kling, David McCarty, Guenter Sauter, Harald Smith, and Peter Worcester.

The requester side caching pattern implementation specification

developerWorks, SOA and Web services zone has just published the paper, on the requester side caching pattern implementation specification. Part 1 of this article series provided an overview of the requester side caching (RSC) pattern specification, which can help you make and document design decisions around the cache and policies. In this second installment in the series, examine the requester side caching pattern implementation specification, a bridge between the human readable pattern specification from the Gang of Four and the pattern implementation that can be used in a development environment to automate the application of the pattern. From this implementation specification, you have the freedom to create numerous implementations.

Readers can find the article here: The requester side caching pattern implementation specification

The many buckets problem

As I mentioned in my previous post one of the problems that we run into with mapping context to content is the multi bucket problem. Consider the following. At home I organize all my clothes into buckets.

Each bucket has a different item of clothing in it e.g. one bucket contains all my shoes; another bucket contains all my pants; yet another bucket has all my jackets.

How let's imagine for a moment that these are very sophisticated buckets. These buckets have the ability to classify all of the items contained within as well as matching items together. For example my shoe bucket has the ability to classify all of my shoes by color and size as well as matching all of my left shoes with their corresponding right shoes. To top it off these buckets have a shinny panel on the front that gives me detailed information about any item in the bucket. So with my bucket of shoes I can see from the front panel all the information that i need about a certain shoe, such as when I bought it, how often I have worn it etc.

 This is all fine and dandy, but I run into a problem when for example I am invited out to a work related cocktail party. Since this is my first cocktail party I am unsure about what to wear and would like to be able to say to all my clothes buckets, please give me an ensemble to make me look good for this cocktail party. My clothes buckets I am sure will look back blackly at me, with the shinny digital displays, beeping nervously as I start to frantically root through one bucket after another to try and put together some vestige of an outfit that will make me presentable at the cocktail party.

This simple story serves to illustrate the problems inherent in any repository based information management system. However, there are a couple of lessons to be learned from this simple analogy.

1. No one bucket can be responsible for management relationships between the items in its bucket and the items in another bucket as the would quickly be unmanageable for all but the smallest number of bucket items
2. I could put all my clothes into one bucket and call it a clothes bucket, however by treating all of clothes as just generic clothes items I lose a lot of flexibility I has in the multi bucket scenario.

Enabling asset consumability

I live a charmed life where my wife and I get to drive to work together. The drive is about 40 minutes from where we live to where we both work in Cambridge Boston. Because of all of this quality time we often get to talk about what we are doing. So one day last week I was trying to explain to her some of my ideas around mapping of 'context to content'. My wife does not suffer my nonsense easily and told me that this statement made no sense to her. In an effort to explain I gave her a driving analogy since we were already is an automobile.

Behind the wheel of a car I am in a driver context and in this context I need access to driver related information and tooling such as my speed, the condition of my engine, weather conditions, a GPS device, road conditions, including this particular road's speed limit (i.e. the car's context) etc.

This is an example of 'context to content'. I am in a driver context and I need access to the driver relevant and related content.

As a Software developer or a consultant on an engagement I am again in a certain context. The context is now given by the scope of the project and the functional and non-functional requirements for that particular project. This context can also be mapped to content to better help me do my job. For example on insurance project there may be a functional requirement around creating a claims system. Here the functional requirement can be mapped to reusable software assets such as an insurance UML model of a claim system. A nonfunctional requirement on the other hand such as a transactional claims system can maps to another type of reusable software asset such as a software pattern assets to help me made consistent architectural decisions.

The question now becomes how we do automate this context to content mapping for developing software in a consistency manner to allow better consumabitity of reusable asset such as models and patterns?

The problem with implementing such a vision is that we quickly run into what I call the two bucket problem. But more about that in my next blog entry .

The Information perspective of SOA design

• data semantics and in particular Business Glossary + Industry Models
• data structure / canonical data model and in particular RDA + Industry Models (+ RSA)
• data quality analysis and in particular Information Analyzer

The requester side caching pattern implementation specification (part 2)

In this two part blog we will examine the requester side caching pattern implementation specification. This implementation specification is a bridge between the human readable pattern specification (e.g. pattern specifications found in the book on Design pattern from the Gang of Four) and a pattern implementation that can be used in a development environment to automatic the application of the pattern. From this implementation specification we have the freedom to create numerous implementation.

In this second part we will also go into more detail on the Java code that a pattern implementation would need to generate if and when these parameters are bound.

UML to Java Transformation

The transformation implemented for this pattern will extend or reuse the UML to Java transformation.

Service

The AcceleratedService UML class created during pattern instantiation expands to a Java class that implements the Service interface. The constructor of this class will take as argument a service of type Service as argument. The class will also have a private member, service of type Service that is instantiated by the constructor to the 'slow' original service implementation.

Another member of the AcceleratedService class is the cache of type Map. The operations generated in AcceleratedService are operations defined in IService. In addition, we will have a method to retrieve the cache, getCache(), that returns the cache implementation object.

getItem

The getItem pattern parameter expands into an operation in the AcceleratedService class that mirrors its name and signature. This operation takes a 'key' as input and returns an Item as output. The implementation of this method is fully specified during the UML to Java transformation phase. In the fragment below, the method body contains the following variable values:

Item: the return type of the original getItem method.

ItemKey: the argument (that corresponds to the key value) of the original getItem method in the service.

public Item getItem(ItemKey itemKey) {
Item item = (Item)this.cache.get(itemKey);
if (item == null)
item = this.service.getItem(itemKey);
return item;
}

getItems

The getItems pattern parameter expands into an operation in the AcceleratedService class that mirrors its name and signature. The signature of this operation is arbitrary and this can contain any number of parameters and parameter types. The return type of this operation must be a List JDK collection type. The implementation of this method is fully specified during the UML to Java transformation phase. In the fragment below, the method body contains the following variable values:

List: the return type class.

arguments: the argument list of the original getItems method in the service.

ItemKey: the key class/type of the Item class. This value is identified in the argument list of the getItem() pattern parameter.

Item: the item class/type. This value is identified as the return type of the getItem() pattern parameter.

.
public List getItems(arguments) {
List keys = this.getItemKeys(arguments);
Iterator iterator = keys.iterator();
List list = new ArrayList();
while (iterator.hasNext()) {
ItemKey key = (ItemKey) iterator.next();
Item item = this.getItem(key);
list.add(item);
}
return list;
}

getItemKeys

The getItemKeys pattern parameter expands into an operation in the AcceleratedService class that mirrors its name and signature. The signature of this operation is arbitrary and this can contain any number of parameters and parameter types. The return type of this operation must be a List JDK collection type. The implementation of this method is fully specified during the UML to Java transformation phase. In the fragment below, the method body contains the following variable values:

List: the return type class.

arguments: the argument list of the original getItems method in the service.

public List getItems(arguments) {
List keys = this.service.getItemKeys(arguments);
return keys;
}

Users are expected to properly select Qualifiers of getItemKeys, it is recommended that 'Ordered' Qualifier is selected while others de-selecte d, in order to generate a method that expresses multiplicity of return parameter in term of 'java.util.List'.

changeItemKey Parameter

The changeItemKey pattern parameter expands into a specific argument (corresponding the key) of an operation changeItem in the AcceleratedService class. The changeItem operation mirrors the changeItem method declared in the Service interface. The implementation of this method is fully specified during the UML to Java transformation phase. The method fragment below contains the following variables:

Arguments1: zero or more arguments of the original changeItem method that precede the changeItemKey argument.

changeItemKey: the argument that corresponds to the changeItemKey parameter.

Arguments2: zero or more arguments of the original changeItem method that follow the changeItemKey argument.

public changeItem(Arguments1, changeItemKey, Arguments2) {
this.service.changeItem(Arguments1, changeItemKey, Arguments2);
this.cache.remove(changeItemKey);
}

Clustering Parameter

As explained earlier, the clustering parameter effects the kind of cache implementation used by the AcceleratedService. Based on the value of clustering, the implementation of Map will be either an inmemory cache, i.e., class Cache if clustering is false, or class DistributedCache if clustering is true. If clustering is true, the getCache() of the AcceleratedService will look like:

public Map getCache() {
com.ibm.websphere.cache.DistributedMap map = null;
try {
javax.naming.InitialContext ic = new
javax.naming.InitialContext();
Object obj = ic.lookup("services/cache/EmployeeCache");
if (obj != null) {
map = (com.ibm.websphere.cache.DistributedMap)
javax.rmi.PortableRemoteObject
.narrow(obj,
com.ibm.websphere.cache.DistributedMap.class);
System.out.println("successfully retrieved a map");
}
} catch (Exception e) {
System.out.println("failed to retrieve a map");
e.printStackTrace();
}
return map;
}

If clustering is false, a class, Cache that implements the Map interface is generated with an prepackaged code template. The getCache() of the AcceleratedService will look like:

public Map getCache() {
Map map = new Cache(size, timeout);
return map;
}

Cache size Parameter

If clustering is true, the cacheSize parameter expands into the cacheSize value in the properties file used by the underlying WebSphere caching implementation (cacheinstances.properties). If clustering is false, the cacheSize parameter expands into the workingSetSize value in the Custom cache implementation, InMemoryCache.

timeOut Parameter

If clustering is true, the cacheSize parameter expands into the cacheSize value in the properties file used by the underlying WebSphere caching implementation (cacheinstances.properties).

Implementing the specification

Armed with the pattern implementation specification a pattern implementation can now be authored using the RSA pattern engine. The output of this authoring process is an eclipse plug-in that can be used in RSA to automate the application of the requester side caching (RSC) pattern to a model.

The approach to using this pattern follows the model-driven development approach of instantiating the pattern parameters to specific UML model elements of the service or interface. Once the pattern parameters are bound, additional UML elements such as the cache and the cache-aware service proxy are automatically created. A UML-to-Java transformation, invoked on the resulting model, will generate the resulting Java implementation artifacts. This implementation also allows the user to chose between a custom (in memory) user-defined cache or the WebSphere Platform dynamic cache. If the user chooses the latter, the pattern transformation will automatically generate configuration files to be used with dynacache. To see this pattern in action in RSA the reader is referred to the follow article that details how to use the RSA requester side caching (RSC) pattern implementation.

The read can also find a detailed study of the life cycle requester-side caching (RSC) pattern.

The requester side caching pattern implementation specification (part 1)

In this two part blog we will examine the requester side caching pattern implementation specification. This implementation specification is a bridge between the human readable pattern specification (e.g. pattern specifications found in the book on Design pattern from the Gang of Four) and a pattern implementation that can be used in a development environment to automatic the application of the pattern. From this implementation specification we have the freedom to create numerous implementation.

In this first part we will focus of the pattern parameters that need to be identified and the use cases for binding these pattern parameters. In part two we will go into more detail on the lava code that a pattern implementation would need to generate if and when these parameters are bound.

Introduction

In a previous article we have detailed the pattern specification of the requester side caching pattern. A number of implementations of the requester side caching (RSC) pattern can now be created based upon the pattern specification. This pattern could be implemented by hand coding but typically any implementation should provide some level of automation for applying the pattern. The follow three examples showcase different ways that this pattern specification can be implemented:

• A manual implementation (hand coding) that uniquely tailors the design and the implementation for each situation.
• An executable program that automates the application of the pattern in the context of a larger model-driven based development project (e.g., in the context of IBM's Rational Software Architect).
• See Building SOA applications with reusable assets: The requester side caching pattern
• An aspect-based implementation: aspect-oriented programming can be used to implement the caching as a cross cutting concern and can be a simple and useful mechanism for developing applications. This approach would work if aspect-oriented expertise is already available in the organization.
• See Modeling with Aspects: An introduction to modeling with aspects

However, before authoring a pattern, a pattern implementation specification must first be created.

Background

The requester side caching pattern is one of mediating the interaction between one or more clients and one or more data providers. The mediation consists of holding data items that have been produced by the provider(s) and using them to support requests from the client(s). The purpose of the mediation is to speed up and/or reduce the cost of access to the data. This is a very general pattern that has many variations to meet different design goals and issues. The pattern should make it easy for a developer to make design decisions and also to provide a basis for documentation about design decisions made around the cache and policies.

There are a number of issues that must be addressed in any pattern variation. They are:

• Style of access (visible to the client; hidden from the client)
• Item identification (client provided domain key; explicit, well defined key space; computed from data items etc.)
• Populating the cache with data items (on cache misses; on cache misses but with some anticipatory population; fully pre-populated)
• Cache capacity and capacity management (unlimited with respect to the set of data items; easily exceeds to working set of the clients; marginal with respect to the working set of clients)
• Tolerance for data staleness (must be exact; can be slightly out of date; accuracy of the data is a performance concern, not a logical concern)
• Management of data volatility (unchanging data; rarely changing data; rapidly changing data)
• Topology of deployment (single instance; multiple coordinated instances; multiple independent instances)

The requester side caching pattern will facilitate accelerating service operations via caching. The cache used can be an in-memory, custom cache provided by the pattern itself or the WebSphere runtime's caching mechanism (i.e., Dynacache). The runtime cache can be in memory or distributed. One of the goals of this pattern is to make it easy for the developer to programmatically use our runtime caching mechanisms.

This pattern currently assumes that the service operations exposed to the requester include methods for

• retrieving objects from the service provider based on a selection criteria, getItems(criteria)
• retrieving the keys that uniquely identify the objects based on selection criteria, getItemKeys(criteria)
• retrieving a single object based on a key value, getItem(key)
• making changes to objects based on key value, changeItem(..., key, ...)

If operations such as getItemKeys(criteria) and changeItem(key, ...) are not available, one proposal is to have some kind of a facade that will sit between this caching pattern and the service interface where these operations will be inserted.

References

WebSphere Dynamic Cache: Improving J2EE application performance, IBM Systems Journal, Vol 43, No 2, 2004.

Requirements

• The implementation will generate UML model elements as a result of applying the pattern.
• The generated source code should be error free and capable of being executed. Exceptions may be thrown if code marked up with a mandatory 'TODO' has not been updated accordingly by the user.
• This pattern must allow for at least two choices in the caching implementation.
• The pattern should also allow for cache configurations that include cache population, management of data volatility, cache capacity management and item identification.
• The pattern should make it easy for a developer to make design decisions and also to provide a basis for documentation about design decisions made around the cache and policies. The transformation is also constrained by the caching implementations provided by the WebSphere platform, but we will also provide custom caching skeleton code.

Design Constraints

The design and implementation of this pattern will be constrained by the pattern and transformation authoring functions of Rational Software Architect 6.x and 7.x.

Design Artifacts

Pattern Name

Requester side cache

Overview Diagram

The pattern UI will represent this pattern with the following overview diagram.

Group

The pattern will be defined in the following group: SOA Patterns

Pattern Type

An instance of this pattern is of the UML2 element type Collaboration.

Short Description

The Requester side caching pattern provides an accelerated implementation of operations that retrieve information from a service provider. The pattern provides options for cache implementations and caching policies.

Search Keywords

The following keywords will be defined for the pattern to assist the client with locating the desired pattern:

1. SOA Patterns
2. Caching
3. Service requester

Pattern Parameters

Service

• Short Description: The interface/class that contains the operation that we want to accelerate
• Type: Interface/Class
• Multiplicity: 1
• Parameter Dependency: none
• Default Value: none
• Keyword: none

getItem

• Short Description: The operation on the service interface/class that is used to get a single item given an item key.
• Type: Operation
• Multiplicity: 1
• Parameter Dependency: this parameter depends on Service
• Default Value: none
• Keyword: none

getItemKeys

• Short Description: The operation on the service interface/class that is used to get keys given a set of criteria. Note that this operation is essential to be able to implement the accelerated version of the service.
• Type: Operation
• Multiplicity: 1
• Parameter Dependency: this parameter depends on Service
• Default Value: none
• Keyword: none

getItems

• Short Description: Short Description: The operation on the service interface/class that is used to get items given a set of criteria.
• Type: Operation
• Multiplicity: 1
• Parameter Dependency:depends on Service, getItemKeys and getItem
• Default Value: none
• Keyword: none

changeItemKey

• Short Description: The parameter in the change item operation that corresponds to the key of the item.
• Type: Parameter
• Multiplicity: 0..* (if change item is specified then this parameter is required)
• Parameter Dependency: none
• Default Value: none
• Keyword: none

Cache Size

• Short Description: The size of the cache. The size of the cache can be specified as an integer or the character * to denote a unlimited cache.
• Type: LiteralInteger
• Multiplicity: 0..1
• Parameter Dependency: none
• Default Value: 500
• Keyword: none

Clustering

• Short Description: Boolean value: true implies the underlying topology is clustered, false implies the underlying topology is not clustered.
• Type: LiteralBoolean
• Multiplicity: 1
• Parameter Dependency: none
• Default Value: true
• Keyword: none

timeOut

• Short Description: The time out value (measured in milli seconds) after which an item has to be evicted from cache. If this parameter is not specified a time out eviction policy is not used.
• Type: LitrealInteger
• Multiplicity: 0..1
• Parameter Dependency: none
• Default Value: -1
• Keyword: none

The derived parameters from the above listed pattern parameters are: ItemKey class that can be derived as the argument of the getItem operation, Item class that can be derived from the return parameter of the getItem operation, changeItem() method that can be inferred from the changeItemKey, i.e., its owning operation.

Use cases

The actions denoted below occur only at the time the argument is supplied to the pattern parameter. After that, the argument may change irrespective of the constraints of the pattern definition.

Pattern Interactions

Service Parameter

Supply an argument to the parameter

• Add the 'Accelerated' keyword to the argument if the keyword does not exist.
• Create a new class in the same package as the argument.
• The name of the generated class is going to be the name of the argument prepended with the string 'Accelerated'.
• The generated class will realize the argument if the parameter is an interface.
• The generated class will provide implementations for all of the arguments operations if the parameter is an interface. If the parameter is a class, the generated class simply inherits the operations.
• The generated class will provide implementations for the constructor (where Service is the name of the argument)
  AcceleratedService ( service : Service )
• The generated class will have a directed private association to the argument called service
• The generated class will have a have a public getter method for the instance member service.
• The generated class will have a directed private association to a Map interface.
• The generated class will have a have a public getter method for the instance member Map.
• If the clustering parameter is set to false, the constructor creates a cache object of type Cache that is an implementation of the Map interface which supports get, put, remove and clear operations.
• If the clustering parameter is set to true, the constructor (AcceleratedService) gets a cache object of type DistributedCache that is an implementation of the Map interface. The operations of this class are again the same as declared in Map.

Supply the default argument to the parameter

• N/A

Remove an argument from the parameter

• Undo what we did before, in particular remove the newly created class and the associated to the IService parameter. However do not remove the Map interface if it is being referenced by any other modeling element

Replace the parameter argument

• Default to the framework semantics of removing the current argument and supplying the new argument.

Pattern reapply

• Default to the framework semantics of removing the current argument and supplying the new argument.

getItem Parameter

• This argument is only referenced during the UML to Java code transformation.

getItems Parameter

• This argument is only referenced during the UML to Java code transformation.

getItemKeys Parameter

• This argument is only referenced during the UML to Java code transformation.

Change Item Key Parameter

• This argument is only referenced during the UML to Java code transformation.

Cache size Parameter

• This argument is only referenced during the UML to Java code transformation.

Clustering Parameter

• This argument is only referenced during the UML to Java code transformation.

timeOut Parameter

• This argument is only referenced during the UML to Java code transformation.

The derived parameters from the above listed pattern parameters are: ItemKey class that can be derived as the argument of the getItem operation, Item class that can be derived from the return parameter of the getItem operation, changeItem() method that can be inferred from the changeItemKey, i.e., its owning operation.