Building microservices through Event Driven Architecture part3: Presenters, Views, and Controllers
During this journey, I will implement the command side of Presenters, Views, and Controllers
In this step, I will implement the presentation. Here, the presentation is not the user interface but the Web API.
It is also possible to implement the repository first, before the presentation, the order does not matter.
Note that it will be necessary to configure the dependency injection later on.
IUnitOfWork and ISpeechRepository are injected into RegisterSpeechUseCase. Therefore, IUnitOfWork and ISpeechRepository must be instantiated to get an instance of IRegisterSpeechUseCase
But , since I am doing a mock of IRegisterSpeechUseCase, I still do not need a concrete implementation of IUnitOfWork and ISpeechRepository.
So I will configure the dependency injections when my repository is implemented and ready to use.
This is useful when one team is working on the presention and another team is working on the repository. In this case every team can advance on his side without waiting for other team’s
Presentation belongs to interface adapter in the clean architecture
The software in this layer is a set of adapters that convert data from the format most convenient for the use cases and entities, to the format most convenient for some external agency such as the Database or the Web. It is this layer, for example, that will wholly contain the MVC architecture of a GUI. The Presenters, Views, and Controllers all belong in here. The models are likely just data structures that are passed from the controllers to the use cases, and then back from the use cases to the presenters and views.
Here, I have to create an HttpPost action that accepts a dto, which contains information about the speech I want to create.
If this information is correct, I call the handle function of my usecase and I pass it as argument this dto converted into command. If informations into the dto are not correct I return BadRequest or 400
Then , usecase executes, and if everything is Ok, I return 200 otherwise, I return 500
So my first test will be : Register Speech With Invalid ModelState Return BadRequest
So let us create it
The next step is to create a SpeechForCreationDto class and a SpeechController class with a post action:
then my first test pass
my second test will verify that _registerSpeechUseCase.Handle(command) is called only once and everything is Ok
So, to make the previous test pass, I have to call _registerSpeechUseCase.Handle like this
The last test is to handle InternalServerError (500) if an exception occured
I prefer to create a middlware so as to catch errors globaly, if an exception is thrown I proceed as follow :
I’m using a single try / catch block in the entire application. It is also possible to have no try / catch by doing the following:
Here is the unit test of ExceptionMiddleware
Code Coverage of presentation is 63%:
What is not tested are startup class and Programm class and ExceptionMiddleware.MoveNext() function
I can add more tests to cover them, but I can also leave them like that because they will be covered by the integration tests
#dotnet #aspdotnet #csharp #microservices
Companies need to be thinking long-term before even starting a software development project. These needs are solved at the level of architecture: business owners want to assure agility, scalability, and performance.
The top contenders for scalable solutions are serverless and microservices. Both architectures prioritize security but approach it in their own ways. Let’s take a look at how businesses can benefit from the adoption of serverless architecture vs microservices, examine their differences, advantages, and use cases.
#serverless #microservices #architecture #software-architecture #serverless-architecture #microservice-architecture #serverless-vs-microservices #hackernoon-top-story
Event-driven architecture, or EDA, is an integration pattern where applications are oriented around publishing events and responding to events. It provides five key benefits to modern application architecture: scalability, resilience, agility, data sharing, and cloud-enabling.
This article explores how EDA fits into enterprise integration, its three styles, how it enables business strategy, its benefits and trade-offs, and the next steps to start an EDA implementation.
Although there are many brokers you can use to publish event messages, the open-source software Apache Kafka has emerged as the market leader in this space. This article is focused on a Kafka-based EDA, but much of the principles here apply to any EDA implementation.
If asked to describe integration a year ago, I would have said there are two modes: application integration and data integration. Today I’d say that integration is on a spectrum, with data on one end, application on the other end, and event integration in the middle.
The spectrum of integration.
Application integration is REST, SOAP, ESB, etc. These are patterns for making functionality in one application run in response to a request from another app. It’s especially strong for B2B partnership and exposing value in one application to another. It’s less strong for many data use cases, like BI reporting and ML pipelines, since most application integrations wait passively to be invoked by a client, rather than actively pushing data where it needs to go.Data integration is patterns for getting data from point A to point B, including ETL, managed file transfer, etc. They’re strong for BI reporting, ML pipelines, and other data movement tasks, but weaker than application integration for many B2B partnerships and applications sharing functionality.
Event integration has one foot in data and the other in application integration, and it largely gets the benefits of both. When one application subscribes to another app’s events, it can trigger application code in response to those events, which feels a bit like an API from application integration. The events triggering this functionality also carry with them a significant amount of data, which feels a bit like data integration.
EDA strikes a balance between the two classic integration modes. Refactoring traditional application integrations into an event integration pattern opens more doors for analytics, machine learning, BI, and data synchronization between applications. It gets the best of application and data integration patterns. This is especially relevant for companies moving towards an operating model of leveraging data to drive new channels and partnerships. If your integration strategy does not unlock your data, then that strategy will fail. But if your integration strategy unlocks data at the expense of application architecture that’s scalable and agile, then again it will fail. Event integration strikes a balance between both those needs.
EDA often begins with isolated teams as a tactic for delivering projects. Ideally, such a project would have a deliberative approach to EDA and a common event message broker, usually cloud-native brokers on AWS, Azure, etc. Different teams select different brokers to meet their immediate needs. They do not consider integration beyond their project scope. Eventually, they may face the need for enterprise integration at a later date.
A major transition in EDA maturity happens when the investment in EDA shifts from a project tactic to enterprise strategy via a common event bus, usually Apache Kafka. Events can take a role in the organization’s business and technical innovation across the enterprise. Data becomes more rapidly shareable across the enterprise and also between you and your external strategic partners.
Before discussing the benefits of EDA, let’s cover the three common styles of EDA: event notification, event-carried state transfer, and event sourcing.
This pattern publishes events with minimal information: the event type, timestamps, and a key-value like an account number or some other key of the entity that raised the event. This informs subscribers that an event occurred, but if subscribers need any information about how that event changed things (like which fields changed, etc.), it must invoke a data retrieval service from the system of record. This is the simplest form of EDA, but it provides the least benefit.
In this pattern, the events carry all information about the state change, typically a before and after image. Subscribing systems can then store their cache of data without the need to retrieve it from the system of record.
This builds resilience since the subscribing systems can function if the source becomes unavailable. It helps performance, as there’s no remote call required to access source information. For example, if an inventory system publishes the full state of all inventory changes, a sales service subscribing to it can know the current inventory without retrieving from the inventory system — it can simply use the cache it built from the inventory events, even during an inventory service outage.
It also helps performance because the subscriber’s data storage can be custom-tuned just for that subscriber’s unique performance needs. Using the previous example, perhaps the inventory service is best suited using a relational database, but the sales service could get better performance from a no-SQL database like MongoDB. Since the sales services no longer need to retrieve from the inventory service, it’s at liberty to use a different DBMS than the inventory service. Additionally, if the inventory service is having an outage, the sales service would be unaffected since it pulls inventory data from its local cache.
The cons are that lots of data is copied around and there is more complexity on the receivers since they have to sort out maintaining all the state they are receiving.
#integration #microservices #data #kafka #enterprise architecture #event driven architecture #application architecture
We have been building software applications for many years using various tools, technologies, architectural patterns and best practices. It is evident that many software applications become large complex monolith over a period for various reasons. A monolith software application is like a large ball of spaghetti with criss-cross dependencies among its constituent modules. It becomes more complex to develop, deploy and maintain monoliths, constraining the agility and competitive advantages of development teams. Also, let us not undermine the challenge of clearing any sort of technical debt monoliths accumulate, as changing part of monolith code may have cascading impact of destabilizing a working software in production.
Over the years, architectural patterns such as Service Oriented Architecture (SOA) and Microservices have emerged as alternatives to Monoliths.
SOA was arguably the first architectural pattern aimed at solving the typical monolith issues by breaking down a large complex software application to sub-systems or “services”. All these services communicate over a common enterprise service bus (ESB). However, these sub-systems or services are actually mid-sized monoliths, as they share the same database. Also, more and more service-aware logic gets added to ESB and it becomes the single point of failure.
Microservice as an architectural pattern has gathered steam due to large scale adoption by companies like Amazon, Netflix, SoundCloud, Spotify etc. It breaks downs a large software application to a number of loosely coupled microservices. Each microservice is responsible for doing specific discrete tasks, can have its own database and can communicate with other microservices through Application Programming Interfaces (APIs) to solve a large complex business problem. Each microservice can be developed, deployed and maintained independently as long as it operates without breaching a well-defined set of APIs called contract to communicate with other microservices.
#microservice architecture #microservice #scaling #thought leadership #microservices build #microservice
The software industry has come a long journey and throughout this journey, Software Architecture has evolved a lot. Starting with 1-tier (Single-node), 2-tier (Client/ Server), 3-tier, and Distributed are some of the Software Architectural patterns we saw in this journey.
The majority of software companies are moving from Monolithic architecture to Microservices architecture, and Microservices architecture is taking over the software industry day-by-day. While monolithic architecture has many benefits, it also has so many shortcomings when catering to modern software development needs. With those shortcomings of monolithic architecture, it is very difficult to meet the demand of the modern-world software requirements and as a result, microservices architecture is taking control of the software development aggressively. The Microservices architecture enables us to deploy our applications more frequently, independently, and reliably meeting modern-day software application development requirements.
#microservice architecture #istio #microservice best practices #linkerd #microservice communication #microservice design #envoy proxy #kubernetes architecture #api gateways #service mesh architecture
Are you trying to claw your way out of the web of API calls that ties your microservices together? Does a seemingly innocent change or bug fix result in a ripple effect across several business serving capabilities? Well, you’re not alone.
Microservices have been gaining steam since their introduction as an architectural style in 2011. Initially pioneered by companies like Amazon and Netflix as an alternative to their exploding monolithic codebases, they are now increasingly popular even at companies operating at a much smaller scale than the behemoths. And with good reason. When designed well, microservices are a great alternative to the problems often seen with their monolithic counterparts. The key phrase there though is “when designed well”. It’s simple enough when you have ten microservices - so scalable, such fun! But when those quickly grow to 50, and then 100, and then 500, you have a real problem on hand if you haven’t paid attention to how they all talk to each other.
Imagine you have several microservices all communicating via API calls. In this web of tightly coupled services, changes to one service may necessitate corresponding changes across multiple other services, and scaling one service would necessitate scaling a number of others as well. This problem was first described as the Death Star Architecture.
In the world of microservices, Death Star architecture is an anti-pattern where poorly designed microservices become highly interdependent, forming a complex network of interservice communication. When this happens, the entire thing becomes slow, inflexible, and fragile – and easy to blow up. ( ref .)
At this point, you’ve lost many of the benefits of having microservices in the first place, and in reality, are left with a distributed monolith
So how do you avoid the Death Star Architecture trap and allow your microservices to scale? How do you keep your microservice relatively isolated but still remain an integral node in the set of business flows that it serves? Enter the Event-Driven Microservice Architecture. The golden rule of Event-Driven microservices is that all communication is asynchronous. No API calls for us! Microservices instead publish records of their doings, also known as events. An Event is a record of a business action and must contain all information relevant to that action. Events are published to messaging infrastructure (think Kafka, RabbitMQ) and it is left to consuming microservices to figure out how to operate on them. By removing this tight coupling between services, it’s possible to truly reap the benefits offered by the microservices architecture pattern.
Event-Driven Messaging comes in two flavors - choreography and orchestration.
What is the choreography pattern?
Choreography is pretty much what it sounds like! Each dancer in a ballet troupe knows their position and performs their routine based on musical cues. Choreographed microservices behave in the same way - each service (dancer) is aware of it’s place in the business flow and acts on certain cues (events).
Let’s look at a simplified example of an order processing flow. The customer completes checking out their cart and the following steps need to happen next
#event-driven #microservices #microservice-architecture #scaling