Crawford and Kaplan's J2EE Design Patterns approaches the subject in a unique , highly practical and pragmatic way. Rather than simply present another. O'Reilly books may be downloadd for educational, business, or sales promotional use. . in the book Core J2EE Patterns and were separated into three main. O'Reilly books may be downloadd for educational, business, or sales .. in the book Core J2EE Patterns and were separated into three main.
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J2EE Component. Design Patterns. Thomas Liou. Manager, Skills Development. Valtech Technologies, Inc. [email protected] O'Reilly. J2EE Design Patterns. Be watching for more books in the Head First series! Other related books from O'Reilly. Head First Java. Head First Servlets & JSP. PDF | In this paper we propose a model based on the Model-View-Controller design Keywords: Enterprise platforms, design patterns, J2EE, frameworks .. Prentice Hall. Crawford, W., Kaplan, J., J2EE design patterns. O'Reilly. Giang.
Although design patterns have been applied practically for a long time, formalization of the concept of design patterns languished for several years. Freshly written code can often have hidden subtle issues that take time to be detected, issues that sometimes can cause major problems down the road. Reusing design patterns helps to prevent such subtle issues[ citation needed ], and it also improves code readability for coders and architects who are familiar with the patterns.
In order to achieve flexibility, design patterns usually introduce additional levels of indirection , which in some cases may complicate the resulting designs and hurt application performance. By definition, a pattern must be programmed anew into each application that uses it. Since some authors see this as a step backward from software reuse as provided by components , researchers have worked to turn patterns into components.
Meyer and Arnout were able to provide full or partial componentization of two-thirds of the patterns they attempted. And last but not least, there are dedicated products, like JBoss Keycloak. Migration Approaches Putting the discussion in Designing Software for a Scalable Enterprise about greenfield versus brownfield development into practice, there are three different approaches to migrating existing applications to microservices.
After the initial assessment, you know exactly which parts of the existing application can take advantage of a microservices architecture. And while moving out individual services one at a time, the team has a fair chance to adapt to the new development methodology and make its first experience with the technology stack a positive one. A load balancer or proxy decides which requests need to reach the original application and which go to the new parts. There are some synchronization issues between the two stacks.
Parallel operations: strangler pattern Big Bang: Refactor an Existing System In very rare cases, complete refactoring of the original application might be the right way to go. This is the least recommended approach because it carries a comparably high risk of failure.
Microservices Design Pattern Functional decomposition of an application with the help of DDD is a prerequisite for building a microservices architecture. Only this approach allows you to effectively design for loose coupling and high cohesion. However, unlike with applications, which are tied together by the frontend, microservices can interact with each other and span a network of service calls.
To keep the variety of interactions comprehensible and maintainable, a first set of patterns have emerged that will help you to model the service interaction. These patterns were first published by Arun Gupta , but have been revised for this report. Common Principles Every microservice has some common basic principles that need to be taken into account. One reason for this is that teams can be fully responsible for putting new versions into production.
It also enables the team to use the needed downstream services at the correct revision by querying the repository. Compare Independently Deployable and Fully Contained. Needing to replicate state across various services is a strong indicator of a bad design.
Services are fully contained and independent and should be able to work without any prepopulated state. Compare Designing Software for a Scalable Enterprise.
The Data Access Layer Is Cached In order to keep service response times to a minimum, you should consider data caching in every service you build. And keep in mind Design for Performance. It is already well known from the Enterprise Integration pattern catalog and has proven to be useful outside microservices architecture.
The primary goal of this pattern is to act as a special filter that receives a stream of responses from service calls and identifies or recognizes the responses that are correlated. Once all the responses have been been collected, the aggregator correlates them and publishes a single response to the client for further processing.
In its most basic form, aggregator is a simple, single-page application e. Assuming all three services in this example are exposing a REST interface, the application simply consumes the data and exposes it to the user. The services in this example should be application services compare above and do not require any additional business logic in the frontend.
If they represent domain services, they should be called by an application service first and brought into a representable state. It is totally valid to use different protocols. Because the aggregator is another business service heavily accessing asynchronous domain services, it uses a message-driven approach with the relevant protocols on top e. The wrapper service can add additional functionality to the service of interest without changing its code.
Proxy pattern The proxy may be a simple pass-through proxy, in which case it just delegates the request to one of the proxied services.
It is usually called a smart proxy when additional logic is happening inside the proxy service. The applicable logic varies in complexity and can range from simple logging to adding a transaction. If used as a router, it can also proxy requests to different services by parameter or client request. Pipeline Pattern In more complex scenarios, a single request triggers a complete series of steps to be executed.
In this case, the number of services that have to be called for a single response is larger than one. A pipeline can be triggered synchronously or asynchronously, although the processing steps are most likely synchronous and rely on each other. But if the services are using synchronous requests, the client will have to wait for the last step in the pipeline to be finished. As a general rule of thumb, according to usability studies , one-tenth of a second is about the limit for having the user feel that the system is reacting instantaneously.
Normally, no special feedback is necessary during delays of more than 0. Shared Resources One of the critical design principles of microservices is autonomy.
Especially in migration scenarios see "Migration Approaches" on page 43 , it might be hard to correct design mistakes made a couple of years ago. And instead of reaching for the big bang, there might be a more reasonable way to handle those special cases.
The key here is to keep the business domain closely related and not to treat this exception as a rule; it may be considered an antipattern but business needs might require it. With that said, it is certainly an antipattern for greenfield applications. Most likely, they are implemented in a synchronous and therefore blocking manner.
Even if this can be changed in Java EE, and the implementations support asynchronous calls, it might still be considered a second-class citizen in the enterprise systems you are trying to build. Message-oriented middleware MOM is a more reasonable solution to integration and messaging problems in this field, especially when it comes to microservices that are exposed by host systems and connected via MOMs.
Asynchronous messaging Conclusion The world of IT as we know it is changing dramatically. Just over five years ago, developers would spend months or even years developing infrastructures and working on the integration of various applications.
Huge projects with multiple participants were required to implement the desired specific features. With the advent of DevOps and various Platform as a Service PaaS environments, many complex requirements must now be met within a much shorter timeframe. The Internet of Things IoT is also anticipated to change established applications and infrastructures. As a result of these converging trends, the way in which developers work is set to undergo a fundamental shift in the coming years.
As these trends unfold, the industry is already mapping the way forward, anticipating how all the components—from technologies to processes—will come together in this new development paradigm. While the adoption speed will vary and the pure doctrine of the early adopters will have to be tweaked, there are strong signs that the recent uptake in microservices architectures will not fade.
Knowing this, we need to be aware of the challenges to come and figure out how to adapt to these paradigms in practice. It is a core responsibility for enterprise developers to help further shape this future and keep on learning how to best adopt the new technologies in the field.
Further Resources contains a long list of references and recommended readings for getting started with this future. Additional Technologies and Team Considerations As already mentioned, software architecture does not adhere to a strict process for creation.
However, what it does involve is a lot of teamwork, creativity, and flexibility in adopting changing requirements.
J2EE Design Patterns
This not only covers the design of the system or individual services, but also reaches out to the technologies used and various team dynamics. Unlike with traditional Java EE applications, where the infrastructure is well defined by the application server in use, the solution space for microservices-based systems is open ended and requires a different perspective on teams.
This appendix is designed to point you to alternative microservices solutions outside of the traditional Java EE ecosystem. It also provides greater insight into aligning teams to work with highly scalable architectures. This is also true for microservices, although the service contracts in a microservices-based architecture allow for a flexible decision about the underlying implementation.
Although it has been mainly discussed in the context of web applications, it has far broader appeal than purely the Web. This is the most critical component for microservices-based applications, which naturally have to handle a lot of concurrent processing of messages or events while holding up a lot of connections.
This type of functionality can be achieved without being a container or an invasive framework. You can use Vert. The nonblocking nature and reactive programing model speeds along the adoption of basic microservices design principles and recommendations, making this framework easier to use than other platforms.
WildFly Swarm allows developers to package just enough of its modules back together with their application to create a self-contained executable JAR. All the required Java EE dependencies are already available to the application with the application server base installation, and containers provide additional features like transactions and security. Multimodule applications typically are deployed together on the same instance or cluster and share the same server base libraries.
With Swarm, you are able to freely decide which parts of the application server base libraries your application needs. After the packaging process, the application can be run using the java -jar command. This reduces the available amount of specifications and containers for the application to the needed minimum. It also improves the footprint, rollout, and scaling in the final infrastructure while still utilizing the Java EE programing model.
They make it easy to hide a service behind an interface, find instances of services, and load-balance between them. In the default case, Ribbon uses the Netflix Eureka server to register and discover individual services. With WildFly Swarm, the standard clustering subsystem can be used to locate these services and maintain the lists of endpoints. It has evolved as a framework especially designed for microservices.
It is built on top of the Spring framework and uses the maturity of it while adding additional features to aid the development of microservices-based applications. Developer productivity is a "first class" citizen, and the framework adds some basic assumptions about how microservices applications should be built.
This includes the assumption that all services have RESTful endpoints and are embedded into a standalone web application runtime. The overall Spring methodology to adopt the relevant features and leave out the others is also practiced here. This leads to a very lean approach that can produce small units of deployments that can be used as runnable Java archives.
You can enable and configure the common patterns inside your application via Java annotations and build distributed systems while transparently using a set of Netflix OSS components. It pulls together well-known, stable, mature libraries from the Java ecosystem e. The individual technologies are wired together with the help of various interfaces and annotations that can be viewed as the glue in between.
This leaves the user with having to know the individual technologies first, plus the wiring in between them. So, there is a learning curve involved, but not a steep one.
By packaging the relevant and needed modules together, it can be a feasible alternative even if it will require a lot more effort in building the initial stack of frameworks and libraries. Thoughts About Teams and Cultures While you can read a lot about how early adopters like Netflix structured their teams for speed instead of efficiency, there is another more reasonable approach for enterprise software development teams.
Implementing automatic failure routines has to be part of every service call that is happening. Looking back at the usability metrics and acceptable response times, it is incredibly beneficial to always fail sooner than later. But what can be done with a failed service? And how do you still produce a meaningful response to the incoming request?
Service load balancing and automatic scaling A first line of defense is load balancing based on service-level agreements SLAs. Every microservice needs a defined set of metadata that allows you to find out more information about utilization and average response times. Depending on thresholds, services should be scaled automatically, either horizontally add more physical machines or vertically add more running software instances to one machine.
At the time of writing, this is a commodity feature of most known cloud platforms with respect to applications. Scaling based on individual SLAs and metrics for microservices will be implemented soon enough with orchestration layers like Kubernetes. Until then, you will have to build your own set of metainformation and scaling automations. The easiest part in all of this is to fail fast and detect those failures early. To mark services as failing, you need to keep track of invocation numbers and invent a way to retry a reasonable number of times until you decide to completely dismiss a service instance for future calls.
There are four patterns that will help you to implement the desired behavior of services: Retry on failure This pattern enables the application to handle anticipated, temporary failures when it attempts to connect to a service by transparently retrying an operation that has previously failed in the expectation that the cause of the failure is transient. You may implement the retry pattern with or without a dynamic and configurable number of retries or just stick to a fixed number based on service metadata.
The retries can be implemented as synchronous, blocking, or asynchronous nonblocking, and there are a couple of libraries available to help you with the implementation. Working with messages and a messaging system makes retry on failure a little easier.
The relevant metadata for services can be interpreted by the queues or the event bus and reacted upon accordingly. In the case of a persistent failure, the messages will end up in a compensating service or a dead-letter endpoint. Either way, the messaging or event bus-driven solution will be easier to integrate and handle in most enterprise environments because of the available experience in messaging.
Circuit breaker The circuit breaker handles faults that may take a variable time to connect to a remote service. It acts as a proxy for operations that are at risk to fail. The proxy monitors the number of recent failures, and then uses this information to decide whether to allow the operation to proceed or simply return an exception immediately.
It was first popularized by Michal Nygard in his book Release It! Bulkheads As bulkheads prevent a ship from going down in real life, the name stands for partitioning your system and making it failure-proof. If this is done correctly, you can confine errors to one area as opposed to taking the entire system down.
Partitions can be completly different things, ranging from hardware redundancy, to processes bound to certain CPUs, to segmentation of dedicated functionality to different server clusters. Timeouts Unlike endlessly waiting for a resource to serve a request, a dedicated timeout leads to signaling a failure early.
This is a very simplistic form of the retry or circuit breaker and may be used in situations when talking to more low-level services. Design for Data Separation Consider a traditional monolithic application that stores data in a single relational database. Data seperation is different with microservices.
If two or more services operate on the same data store, you will run into consistency issues. There are potential ways around this e. So, the first approach is to make all of the systems independent. This is a common approach with microservices because it enables decoupled services. But you will have to implement the code that makes the underlying data consistent. This includes handling of race conditions, failures, and consistency guarantees of the various data stores for each service.
You will need to explicitly design for integrity. Design for Integrity While data for each service is kept fully separate, services can be kept in a consistent state with compensating transactions. The rule of thumb should be that one service is exactly related to one transaction.
This is only a viable solution while all services which persist data are up and running and available. This might not be enough for enterprise systems. The following subsections discuss several different approaches you can use to solve this issue. There are plenty of ways to use atomic or extended transactions with different technologies that consider themselves part of the modern software stack.
Implementing equivalent capabilities in your infrastructure or the services themselves e. Given that a significant portion of services will only read the underlying domain objects instead of modifying them, it will be easier to separate services by this attribute to reduce the number of compensation actions you might have to take.
Event-driven design Another approach to transactions is the event-driven design of services. This requires some logic to record all writes of all services as a sequence of events. By registering and consuming this event series, multiple services can react to the ordered stream of events and do something useful with it.
The consuming services must be responsible and able to read the events at their own speed and availability. This includes a tracking of the events to be able to restart consumption after a particular service goes down. With the complete write history as an events database, it would also be possible to add new services at a later stage and let them work through all the recorded events to add their own useful business logic.
By adding a transaction ID into the payload, the subsequent service calls are able to identify long-running transactional requests. Until all services successfully pass all contained transactions, the data modification is only flagged and a second asynchronous service call is needed to let all contributing services know about the successful outcome.
As this significantly raises the number of requests in a system, it is only a solution for very rare and complex cases that need full consistency while the majority of services can run without it. Design for Performance Performance is the most critical part of all enterprise applications. Even if it is the most underspecified, nonfunctional requirement of all, it is still the most complained about. Microservices-based architectures can significantly impact performance in both directions.
First of all, the more coarse-grained services lead to a lot more service calls. Depending on the business logic and service size, this effect is known to fan out a single service call to up to 6 to 10 individual backend-service calls, which only adds the same amount of additional network latency in the case of a synchronous service. The strategies to control this issue are plenty and vary depending on many factors.
Load-test early, load-test often Performance testing is an essential part of distributed applications. This is even more important with new architectures. This is equally important as actual runtime monitoring.
But the biggest difference is that load testing is a proactive way to verify the initial metainformation of an individual service or group of services. It is also a way to identify and define the initial SLAs. Use the right technologies for the job The usual approach is to base all your endpoints on RESTful calls. As a matter of fact, this might not be the only feasible solution for your requirements. Everything about endpoint technologies, interface architecture, and protocols can be put to the test in enterprise environments.
Some services will be better off communicating via synchronous or asynchronous messaging, but others will be ideally implemented using RESTful endpoints communicating over HTTP. There may even be some rare instances that require the use of even more low-level service interfaces based on older remoting technologies.
Further on, it might be valid to test different scenarios and interface technology stacks for optimal performance. There are different API management solutions out there, and these come with all kinds of complexity ranging from simple frameworks and best practices to complete products, which have to be deployed as part of the product.
It will help you to keep track of various aspects of your interfaces. Most importantly, they allow you to dispatch based on service versions, and most of them offer load-balancing features. Besides monitoring, versioning, and load balancing, it is also important to keep track of the individual number of calls per service and version. This is the first step to actually acquiring a complete SLA overview and also tracking down issues with service usage and bottlenecks. Outside performance-relevant topics, API gateways and management solutions offer a broad range of additional features, including increased governance and security.
Use caches at the right layer Caching is the most important and performance-relevant part of microservices architectures.
There are basically two different kinds of data in applications: the type that can be heavily cached, and the type that should never be cached. The latter is represented by constantly refreshing data streams e.
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Everything else can be heavily cached on different levels. The UI aspects of a microservice can actually take advantage of the high-performance web technologies already available, such as edge caches, content delivery networks CDN , or simpler HTTP proxies. All of these solutions rely on the cache expiry settings negotiated between the server and the client.
A different layer of caching technology comes in at the backend. The easiest case is to use a second-level cache with a JPA provider or a dedicated in-memory datastore as a caching layer for your domain entities. The biggest issue is maintaining consistency between cache replicas and between the cache and the backend data source. The best approach here is to use an existing implementation such as JBoss Infinispan.
Independently Deployable and Fully Contained A microservices architecture will make it easier to scale development. With this technology, there is no large team of developers responsible for a large set of features or individual layers of the monolith. However, with constantly shifting team setups and responsibilities for developers comes another requirement: services need to be independently deployable.
Teams are fully responsible for everything from implementation to commissioning and this requires that they are in full control of the individual services they are touching.
Another advantage is that this design pattern supports fault isolation. If every service ideally comes with its own runtime, there is no chance a memory leak in one service can affect other services.
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Crosscutting Concerns Crosscutting concerns typically represent key areas of your software design that do not relate to a specific layer in your application.
This is where design concepts like dependency injection DI and aspect-oriented programming AOP can be used to complement object-oriented design principles to minimize tight coupling, enhance modularity, and better manage the crosscutting concerns. Figure One important item to keep in mind: Java EE was never built to work with distributed applications or microservices.
It also enables the team to use the needed downstream services at the correct revision by querying the repository. Morgan Kaufmann Publishers. Well, before to reply, I would like to remind you that there is a site, on the interweb, that has a little less anwsers than StackOverflow, but that may, sometimes, with the proper query, give you smart replies.
A few guidelines to help you follow the Principle Most importantly, they allow you to dispatch based on service versions, and most of them offer load-balancing features. In the default case, Ribbon uses the Netflix Eureka server to register and discover individual services. Although this is strongly recommended, there are plenty of other solutions starting from custom implementations up to more complex monitoring approaches.
Bulkheads As bulkheads prevent a ship from going down in real life, the name stands for partitioning your system and making it failure-proof.
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