Реактивни системи в Java

1. Въведение

В този урок ще разберем основите на създаването на реактивни системи в Java с помощта на Spring и други инструменти и рамки.

В процеса ще обсъдим как реактивното програмиране е само двигател към създаването на реактивна система. Това ще ни помогне да разберем обосновката за създаване на реактивни системи и различни спецификации, библиотеки и стандарти, които тя е вдъхновила по пътя си.

2. Какво представляват реактивните системи?

През последните няколко десетилетия технологичният пейзаж претърпя няколко прекъсвания, които доведоха до пълна трансформация в начина, по който виждаме стойност в технологиите. Изчислителният свят преди Интернет никога не би могъл да си представи начините и средствата, по които ще промени сегашния ни ден.

С обхвата на Интернет до масите и непрекъснато развиващия се опит, който той обещава, архитектите на приложения трябва да бъдат нащрек, за да отговорят на тяхното търсене.

По принцип това означава, че никога не можем да проектираме приложение по начина, по който сме свикнали по-рано. А много отзивчив приложение вече не е лукс, а необходимост .

Това също е изправено пред случайни неуспехи и непредсказуемо натоварване. Необходимостта от часа не е само да получите правилния резултат, а да го получите бързо! Доста важно е да стимулираме невероятното потребителско изживяване, което обещаваме да предоставим.

Това е, което създава нуждата от архитектурен стил, който може да ни даде реактивни системи.

2.1. Реактивен манифест

Още през 2013 г. екип от разработчици, ръководен от Jonas Boner, се събраха, за да определят набор от основни принципи в документ, известен като Reactive Manifesto. Именно това постави основата на архитектурен стил за създаване на реактивни системи. Оттогава този манифест събра голям интерес от общността на разработчиците.

По принцип този документ предписва рецептата за реактивна система да бъде гъвкава, свободно свързана и мащабируема . Това прави такива системи лесни за разработване, толерантни към откази и най-важното отзивчиви, като основа за невероятно потребителско изживяване.

И така, каква е тази тайна рецепта? Е, това едва ли е някаква тайна! Манифестът определя основните характеристики или принципи на реактивната система:

  • Отзивчива : Реактивната система трябва да осигурява бързо и последователно време за реакция и следователно постоянно качество на услугата
  • Еластична : Реактивната система трябва да остане отзивчива в случай на случайни откази чрез репликация и изолиране
  • Еластична : Такава система трябва да остане отзивчива при непредсказуеми натоварвания чрез рентабилна мащабируемост
  • Управляван от съобщения : Той трябва да разчита на асинхронно предаване на съобщения между системните компоненти

Тези принципи звучат просто и разумно, но не винаги са по-лесни за изпълнение в сложната корпоративна архитектура. В този урок ще разработим примерна система в Java с оглед на тези принципи!

3. Какво е реактивно програмиране?

Преди да продължим, важно е да разберем разликата между реактивното програмиране и реактивните системи. Използваме и двата термина доста често и лесно погрешно разбираме едното за другото. Както видяхме по-рано, реактивните системи са резултат от специфичен архитектурен стил.

За разлика от това, реактивното програмиране е програмна парадигма, където фокусът е върху разработването на асинхронни и неблокиращи компоненти . Ядрото на реактивното програмиране е поток от данни, който можем да наблюдаваме и да реагираме, дори да прилагаме обратно налягане. Това води до неблокиращо изпълнение и оттам до по-добра мащабируемост с по-малко нишки на изпълнение.

Това не означава, че реактивните системи и реактивното програмиране се изключват взаимно. Всъщност реактивното програмиране е важна стъпка към реализирането на реактивна система, но не е всичко!

3.1. Реактивни потоци

Reactive Streams е инициатива на общността, която стартира през 2013 г., за да осигури стандарт за асинхронна обработка на потоци с неблокиращо обратно налягане . Целта тук беше да се определи набор от интерфейси, методи и протоколи, които могат да опишат необходимите операции и обекти.

Оттогава се появиха няколко изпълнения в множество програмни езици, които съответстват на спецификацията на реактивните потоци. Те включват Akka Streams, Ratpack и Vert.x, за да назовем само няколко.

3.2. Реактивни библиотеки за Java

Една от първоначалните цели зад реактивните потоци в крайна сметка трябваше да бъде включена като официална стандартна библиотека на Java. В резултат на това спецификацията на реактивните потоци е семантично еквивалентна на библиотеката Java Flow, въведена в Java 9.

Освен това има няколко популярни възможности за внедряване на реактивно програмиране в Java:

  • Реактивни разширения: Популярни като ReactiveX, те осигуряват API за асинхронно програмиране с наблюдаеми потоци. Те са достъпни за множество езици и платформи за програмиране, включително Java, където е известна като RxJava
  • Project Reactor: Това е друга реактивна библиотека, основаваща се на базата на спецификацията на реактивните потоци, насочена към изграждане на не-приложения на JVM. Случва се и да е в основата на реактивния стек в пролетната екосистема

4. Лесно приложение

За целите на този урок ще разработим просто приложение, базирано на архитектура на микроуслуги с минимален интерфейс. Архитектурата на приложението трябва да има достатъчно елементи, за да създаде реактивна система.

За нашето приложение ще приемем реактивно програмиране от край до край и други модели и инструменти за постигане на основните характеристики на реактивната система.

4.1. Архитектура

Ще започнем с дефиниране на проста архитектура на приложението, която не показва непременно характеристиките на реактивните системи . Оттам нататък ще направим необходимите промени, за да постигнем тези характеристики един по един.

И така, първо, нека започнем с дефиниране на проста архитектура:

This is quite a simple architecture that has a bunch of microservices to facilitate a commerce use-case where we can place an order. It also has a frontend for user experience, and all communication happens as REST over HTTP. Moreover, every microservice manages their data in individual databases, a practice known as database-per-service.

We'll go ahead and create this simple application in the following sub-sections. This will be our base to understand the fallacies of this architecture and ways and means to adopt principles and practices so that we can transform this into a reactive system.

4.3. Inventory Microservice

Inventory microservice will be responsible for managing a list of products and their current stock. It will also allow altering the stock as orders are processed. We'll use Spring Boot with MongoDB to develop this service.

Let's begin by defining a controller to expose some endpoints:

@GetMapping public List getAllProducts() { return productService.getProducts(); } @PostMapping public Order processOrder(@RequestBody Order order) { return productService.handleOrder(order); } @DeleteMapping public Order revertOrder(@RequestBody Order order) { return productService.revertOrder(order); }

and a service to encapsulate our business logic:

@Transactional public Order handleOrder(Order order) { order.getLineItems() .forEach(l -> { Product> p = productRepository.findById(l.getProductId()) .orElseThrow(() -> new RuntimeException("Could not find the product: " + l.getProductId())); if (p.getStock() >= l.getQuantity()) { p.setStock(p.getStock() - l.getQuantity()); productRepository.save(p); } else { throw new RuntimeException("Product is out of stock: " + l.getProductId()); } }); return order.setOrderStatus(OrderStatus.SUCCESS); } @Transactional public Order revertOrder(Order order) { order.getLineItems() .forEach(l -> { Product p = productRepository.findById(l.getProductId()) .orElseThrow(() -> new RuntimeException("Could not find the product: " + l.getProductId())); p.setStock(p.getStock() + l.getQuantity()); productRepository.save(p); }); return order.setOrderStatus(OrderStatus.SUCCESS); }

Note that we're persisting the entities within a transaction, which ensures that no inconsistent state results in case of exceptions.

Apart from these, we'll also have to define the domain entities, the repository interface, and a bunch of configuration classes necessary for everything to work properly.

But since these are mostly boilerplate, we'll avoid going through them, and they can be referred to in the GitHub repository provided in the last section of this article.

4.4. Shipping Microservice

The shipping microservice will not be very different either. This will be responsible for checking if a shipment can be generated for the order and create one if possible.

As before we'll define a controller to expose our endpoints, in fact just a single endpoint:

@PostMapping public Order process(@RequestBody Order order) { return shippingService.handleOrder(order); }

and a service to encapsulate the business logic related to order shipment:

public Order handleOrder(Order order) { LocalDate shippingDate = null; if (LocalTime.now().isAfter(LocalTime.parse("10:00")) && LocalTime.now().isBefore(LocalTime.parse("18:00"))) { shippingDate = LocalDate.now().plusDays(1); } else { throw new RuntimeException("The current time is off the limits to place order."); } shipmentRepository.save(new Shipment() .setAddress(order.getShippingAddress()) .setShippingDate(shippingDate)); return order.setShippingDate(shippingDate) .setOrderStatus(OrderStatus.SUCCESS); }

Our simple shipping service is just checking the valid time window to place orders. We'll avoid discussing the rest of the boilerplate code as before.

4.5. Order Microservice

Finally, we'll define an order microservice which will be responsible for creating a new order apart from other things. Interestingly, it'll also play as an orchestrator service where it will communicate with the inventory service and the shipping service for the order.

Let's define our controller with the required endpoints:

@PostMapping public Order create(@RequestBody Order order) { Order processedOrder = orderService.createOrder(order); if (OrderStatus.FAILURE.equals(processedOrder.getOrderStatus())) { throw new RuntimeException("Order processing failed, please try again later."); } return processedOrder; } @GetMapping public List getAll() { return orderService.getOrders(); }

And, a service to encapsulate the business logic related to orders:

public Order createOrder(Order order) { boolean success = true; Order savedOrder = orderRepository.save(order); Order inventoryResponse = null; try { inventoryResponse = restTemplate.postForObject( inventoryServiceUrl, order, Order.class); } catch (Exception ex) { success = false; } Order shippingResponse = null; try { shippingResponse = restTemplate.postForObject( shippingServiceUrl, order, Order.class); } catch (Exception ex) { success = false; HttpEntity deleteRequest = new HttpEntity(order); ResponseEntity deleteResponse = restTemplate.exchange( inventoryServiceUrl, HttpMethod.DELETE, deleteRequest, Order.class); } if (success) { savedOrder.setOrderStatus(OrderStatus.SUCCESS); savedOrder.setShippingDate(shippingResponse.getShippingDate()); } else { savedOrder.setOrderStatus(OrderStatus.FAILURE); } return orderRepository.save(savedOrder); } public List getOrders() { return orderRepository.findAll(); }

The handling of orders where we're orchestrating calls to inventory and shipping services is far from ideal. Distributed transactions with multiple microservices is a complex topic in itself and beyond the scope of this tutorial.

However, we'll see later in this tutorial how a reactive system can avoid the need for distributed transactions to a certain extent.

As before, we'll not go through the rest of the boilerplate code. However, this can be referenced in the GitHub repo.

4.6. Front-end

Let's also add a user interface to make the discussion complete. The user interface will be based on Angular and will be a simple single-page application.

We'll need to create a simple component in Angular to handle create and fetch orders. Of specific importance is the part where we call our API to create the order:

createOrder() { let headers = new HttpHeaders({'Content-Type': 'application/json'}); let options = {headers: headers} this.http.post('//localhost:8080/api/orders', this.form.value, options) .subscribe( (response) => { this.response = response }, (error) => { this.error = error } ) }

The above code snippet expects order data to be captured in a form and available within the scope of the component. Angular offers fantastic support for creating simple to complex forms using reactive and template-driven forms.

Also important is the part where we get previously created orders:

getOrders() { this.previousOrders = this.http.get(''//localhost:8080/api/orders'') }

Please note that the Angular HTTP module is asynchronous in nature and hence returns RxJS Observables. We can handle the response in our view by passing them through an async pipe:

Your orders placed so far:

  • Order ID: {{ order.id }}, Order Status: {{order.orderStatus}}, Order Message: {{order.responseMessage}}

Of course, Angular will require templates, styles, and configurations to work, but these can be referred to in the GitHub repository. Please note that we have bundled everything in a single component here, which is ideally not something we should do.

But, for this tutorial, those concerns are not in scope.

4.7. Deploying the Application

Now that we've created all individual parts of the application, how should we go about deploying them? Well, we can always do this manually. But we should be careful that it can soon become tedious.

For this tutorial, we'll use Docker Compose to build and deploy our application on a Docker Machine. This will require us to add a standard Dockerfile in each service and create a Docker Compose file for the entire application.

Let's see how this docker-compose.yml file looks:

version: '3' services: frontend: build: ./frontend ports: - "80:80" order-service: build: ./order-service ports: - "8080:8080" inventory-service: build: ./inventory-service ports: - "8081:8081" shipping-service: build: ./shipping-service ports: - "8082:8082"

This is a fairly standard definition of services in Docker Compose and does not require any special attention.

4.8. Problems With This Architecture

Now that we have a simple application in place with multiple services interacting with each other, we can discuss the problems in this architecture. There are what we'll try to address in the following sections and eventually get to the state where we would have transformed our application into a reactive system!

While this application is far from a production-grade software and there are several issues, we'll focus on the issues that pertain to the motivations for reactive systems:

  • Failure in either inventory service or shipping service can have a cascading effect
  • The calls to external systems and database are all blocking in nature
  • The deployment cannot handle failures and fluctuating loads automatically

5. Reactive Programming

Blocking calls in any program often result in critical resources just waiting for things to happen. These include database calls, calls to web services, and file system calls. If we can free up threads of execution from this waiting and provide a mechanism to circle back once results are available, it will yield much better resource utilization.

This is what adopting the reactive programming paradigm does for us. While it's possible to switch over to a reactive library for many of these calls, it may not be possible for everything. For us, fortunately, Spring makes it much easier to use reactive programming with MongoDB and REST APIs:

Spring Data Mongo has support for reactive access through the MongoDB Reactive Streams Java Driver. It provides ReactiveMongoTemplate and ReactiveMongoRepository, both of which have extensive mapping functionality.

Spring WebFlux provides the reactive-stack web framework for Spring, enabling non-blocking code and Reactive Streams backpressure. It leverages the Reactor as its reactive library. Further, it provides WebClient for performing HTTP requests with Reactive Streams backpressure. It uses Reactor Netty as the HTTP client library.

5.1. Inventory Service

We'll begin by changing our endpoints to emit reactive publishers:

@GetMapping public Flux getAllProducts() { return productService.getProducts(); }
@PostMapping public Mono processOrder(@RequestBody Order order) { return productService.handleOrder(order); } @DeleteMapping public Mono revertOrder(@RequestBody Order order) { return productService.revertOrder(order); }

Obviously, we'll have to make necessary changes to the service as well:

@Transactional public Mono handleOrder(Order order) { return Flux.fromIterable(order.getLineItems()) .flatMap(l -> productRepository.findById(l.getProductId())) .flatMap(p -> { int q = order.getLineItems().stream() .filter(l -> l.getProductId().equals(p.getId())) .findAny().get() .getQuantity(); if (p.getStock() >= q) { p.setStock(p.getStock() - q); return productRepository.save(p); } else { return Mono.error(new RuntimeException("Product is out of stock: " + p.getId())); } }) .then(Mono.just(order.setOrderStatus("SUCCESS"))); } @Transactional public Mono revertOrder(Order order) { return Flux.fromIterable(order.getLineItems()) .flatMap(l -> productRepository.findById(l.getProductId())) .flatMap(p -> { int q = order.getLineItems().stream() .filter(l -> l.getProductId().equals(p.getId())) .findAny().get() .getQuantity(); p.setStock(p.getStock() + q); return productRepository.save(p); }) .then(Mono.just(order.setOrderStatus("SUCCESS"))); }

5.2. Shipping Service

Similarly, we'll change the endpoint of our shipping service:

@PostMapping public Mono process(@RequestBody Order order) { return shippingService.handleOrder(order); }

And, corresponding changes in the service to leverage reactive programming:

public Mono handleOrder(Order order) { return Mono.just(order) .flatMap(o -> { LocalDate shippingDate = null; if (LocalTime.now().isAfter(LocalTime.parse("10:00")) && LocalTime.now().isBefore(LocalTime.parse("18:00"))) { shippingDate = LocalDate.now().plusDays(1); } else { return Mono.error(new RuntimeException("The current time is off the limits to place order.")); } return shipmentRepository.save(new Shipment() .setAddress(order.getShippingAddress()) .setShippingDate(shippingDate)); }) .map(s -> order.setShippingDate(s.getShippingDate()) .setOrderStatus(OrderStatus.SUCCESS)); }

5.3. Order Service

We'll have to make similar changes in the endpoints of the order service:

@PostMapping public Mono create(@RequestBody Order order) { return orderService.createOrder(order) .flatMap(o -> { if (OrderStatus.FAILURE.equals(o.getOrderStatus())) { return Mono.error(new RuntimeException("Order processing failed, please try again later. " + o.getResponseMessage())); } else { return Mono.just(o); } }); } @GetMapping public Flux getAll() { return orderService.getOrders(); }

The changes to service will be more involved as we'll have to make use of Spring WebClient to invoke the inventory and shipping reactive endpoints:

public Mono createOrder(Order order) { return Mono.just(order) .flatMap(orderRepository::save) .flatMap(o -> { return webClient.method(HttpMethod.POST) .uri(inventoryServiceUrl) .body(BodyInserters.fromValue(o)) .exchange(); }) .onErrorResume(err -> { return Mono.just(order.setOrderStatus(OrderStatus.FAILURE) .setResponseMessage(err.getMessage())); }) .flatMap(o -> { if (!OrderStatus.FAILURE.equals(o.getOrderStatus())) { return webClient.method(HttpMethod.POST) .uri(shippingServiceUrl) .body(BodyInserters.fromValue(o)) .exchange(); } else { return Mono.just(o); } }) .onErrorResume(err -> { return webClient.method(HttpMethod.POST) .uri(inventoryServiceUrl) .body(BodyInserters.fromValue(order)) .retrieve() .bodyToMono(Order.class) .map(o -> o.setOrderStatus(OrderStatus.FAILURE) .setResponseMessage(err.getMessage())); }) .map(o -> { if (!OrderStatus.FAILURE.equals(o.getOrderStatus())) { return order.setShippingDate(o.getShippingDate()) .setOrderStatus(OrderStatus.SUCCESS); } else { return order.setOrderStatus(OrderStatus.FAILURE) .setResponseMessage(o.getResponseMessage()); } }) .flatMap(orderRepository::save); } public Flux getOrders() { return orderRepository.findAll(); }

This kind of orchestration with reactive APIs is no easy exercise and often error-prone as well as hard to debug. We'll see how this can be simplified in the next section.

5.4. Front-end

Now, that our APIs are capable of streaming events as they occur, it's quite natural that we should be able to leverage that in our front-end as well. Fortunately, Angular supports EventSource, the interface for Server-Sent Events.

Let's see how can we pull and process all our previous orders as a stream of events:

getOrderStream() { return Observable.create((observer) => { let eventSource = new EventSource('//localhost:8080/api/orders') eventSource.onmessage = (event) => { let json = JSON.parse(event.data) this.orders.push(json) this._zone.run(() => { observer.next(this.orders) }) } eventSource.onerror = (error) => { if(eventSource.readyState === 0) { eventSource.close() this._zone.run(() => { observer.complete() }) } else { this._zone.run(() => { observer.error('EventSource error: ' + error) }) } } }) }

6. Message-Driven Architecture

The first problem we're going to address is related to service-to-service communication. Right now, these communications are synchronous, which presents several problems. These include cascading failures, complex orchestration, and distributed transactions to name a few.

An obvious way to solve this problem is to make these communications asynchronous. A message broker for facilitating all service-to-service communication can do the trick for us. We'll use Kafka as our message broker and Spring for Kafka to produce and consume messages:

We'll use a single topic to produce and consume order messages with different order statuses for services to react.

Let's see how each service needs to change.

6.1. Inventory Service

Let's begin by defining the message producer for our inventory service:

@Autowired private KafkaTemplate kafkaTemplate; public void sendMessage(Order order) { this.kafkaTemplate.send("orders", order); }

Next, we'll have to define a message consumer for inventory service to react to different messages on the topic:

@KafkaListener(topics = "orders", groupId = "inventory") public void consume(Order order) throws IOException { if (OrderStatus.RESERVE_INVENTORY.equals(order.getOrderStatus())) { productService.handleOrder(order) .doOnSuccess(o -> { orderProducer.sendMessage(order.setOrderStatus(OrderStatus.INVENTORY_SUCCESS)); }) .doOnError(e -> { orderProducer.sendMessage(order.setOrderStatus(OrderStatus.INVENTORY_FAILURE) .setResponseMessage(e.getMessage())); }).subscribe(); } else if (OrderStatus.REVERT_INVENTORY.equals(order.getOrderStatus())) { productService.revertOrder(order) .doOnSuccess(o -> { orderProducer.sendMessage(order.setOrderStatus(OrderStatus.INVENTORY_REVERT_SUCCESS)); }) .doOnError(e -> { orderProducer.sendMessage(order.setOrderStatus(OrderStatus.INVENTORY_REVERT_FAILURE) .setResponseMessage(e.getMessage())); }).subscribe(); } }

This also means that we can safely drop some of the redundant endpoints from our controller now. These changes are sufficient to achieve asynchronous communication in our application.

6.2. Shipping Service

The changes in shipping service are relatively similar to what we did earlier with the inventory service. The message producer is the same, and the message consumer is specific to shipping logic:

@KafkaListener(topics = "orders", groupId = "shipping") public void consume(Order order) throws IOException { if (OrderStatus.PREPARE_SHIPPING.equals(order.getOrderStatus())) { shippingService.handleOrder(order) .doOnSuccess(o -> { orderProducer.sendMessage(order.setOrderStatus(OrderStatus.SHIPPING_SUCCESS) .setShippingDate(o.getShippingDate())); }) .doOnError(e -> { orderProducer.sendMessage(order.setOrderStatus(OrderStatus.SHIPPING_FAILURE) .setResponseMessage(e.getMessage())); }).subscribe(); } }

We can safely drop all the endpoints in our controller now as we no longer need them.

6.3. Order Service

The changes in order service will be a little more involved as this is where we were doing all the orchestration earlier.

Nevertheless, the message producer remains unchanged, and message consumer takes on order service-specific logic:

@KafkaListener(topics = "orders", groupId = "orders") public void consume(Order order) throws IOException { if (OrderStatus.INITIATION_SUCCESS.equals(order.getOrderStatus())) { orderRepository.findById(order.getId()) .map(o -> { orderProducer.sendMessage(o.setOrderStatus(OrderStatus.RESERVE_INVENTORY)); return o.setOrderStatus(order.getOrderStatus()) .setResponseMessage(order.getResponseMessage()); }) .flatMap(orderRepository::save) .subscribe(); } else if ("INVENTORY-SUCCESS".equals(order.getOrderStatus())) { orderRepository.findById(order.getId()) .map(o -> { orderProducer.sendMessage(o.setOrderStatus(OrderStatus.PREPARE_SHIPPING)); return o.setOrderStatus(order.getOrderStatus()) .setResponseMessage(order.getResponseMessage()); }) .flatMap(orderRepository::save) .subscribe(); } else if ("SHIPPING-FAILURE".equals(order.getOrderStatus())) { orderRepository.findById(order.getId()) .map(o -> { orderProducer.sendMessage(o.setOrderStatus(OrderStatus.REVERT_INVENTORY)); return o.setOrderStatus(order.getOrderStatus()) .setResponseMessage(order.getResponseMessage()); }) .flatMap(orderRepository::save) .subscribe(); } else { orderRepository.findById(order.getId()) .map(o -> { return o.setOrderStatus(order.getOrderStatus()) .setResponseMessage(order.getResponseMessage()); }) .flatMap(orderRepository::save) .subscribe(); } }

The consumer here is merely reacting to order messages with different order statuses. This is what gives us the choreography between different services.

Lastly, our order service will also have to change to support this choreography:

public Mono createOrder(Order order) { return Mono.just(order) .flatMap(orderRepository::save) .map(o -> { orderProducer.sendMessage(o.setOrderStatus(OrderStatus.INITIATION_SUCCESS)); return o; }) .onErrorResume(err -> { return Mono.just(order.setOrderStatus(OrderStatus.FAILURE) .setResponseMessage(err.getMessage())); }) .flatMap(orderRepository::save); }

Note that this is far simpler than the service we had to write with reactive endpoints in the last section. Asynchronous choreography often results in far simpler code, although it does come at the cost of eventual consistency and complex debugging and monitoring. As we may guess, our front-end will no longer get the final status of the order immediately.

7. Container Orchestration Service

The last piece of the puzzle that we want to solve is related to deployment.

What we want in the application is ample redundancy and a tendency to scale up or down depending upon the need automatically.

We've already achieved containerization of services through Docker and are managing dependencies between them through Docker Compose. While these are fantastic tools in their own right, they do not help us to achieve what we want.

Hence, we need a container orchestration service that can take care of redundancy and scalability in our application. While there are several options, one of the popular ones includes Kubernetes. Kubernetes provides us with a cloud vendor-agnostic way to achieve highly scalable deployments of containerized workloads.

Kubernetes wraps containers like Docker into Pods, which are the smallest unit of deployment. Further, we can use Deployment to describe the desired state declaratively.

Deployment creates ReplicaSets, which internally is responsible for bringing up the pods. We can describe a minimum number of identical pods that should be running at any point in time. This provides redundancy and hence high availability.

Let's see how can we define a Kubernetes deployment for our applications:

apiVersion: apps/v1 kind: Deployment metadata: name: inventory-deployment spec: replicas: 3 selector: matchLabels: name: inventory-deployment template: metadata: labels: name: inventory-deployment spec: containers: - name: inventory image: inventory-service-async:latest ports: - containerPort: 8081 --- apiVersion: apps/v1 kind: Deployment metadata: name: shipping-deployment spec: replicas: 3 selector: matchLabels: name: shipping-deployment template: metadata: labels: name: shipping-deployment spec: containers: - name: shipping image: shipping-service-async:latest ports: - containerPort: 8082 --- apiVersion: apps/v1 kind: Deployment metadata: name: order-deployment spec: replicas: 3 selector: matchLabels: name: order-deployment template: metadata: labels: name: order-deployment spec: containers: - name: order image: order-service-async:latest ports: - containerPort: 8080

Here we're declaring our deployment to maintain three identical replicas of pods at any time. While this is a good way to add redundancy, it may not be sufficient for varying loads. Kubernetes provides another resource known as the Horizontal Pod Autoscaler which can scale the number of pods in a deployment based on observed metrics like CPU utilization.

Please note that we have just covered the scalability aspects of the application hosted on a Kubernetes cluster. This does not necessarily imply that the underlying cluster itself is scalable. Creating a high availability Kubernetes cluster is a non-trivial task and beyond the scope of this tutorial.

8. Resulting Reactive System

Now that we've made several improvements in our architecture, it's perhaps time to evaluate this against the definition of a Reactive System. We'll keep the evaluation against the four characteristics of a Reactive Systems we discussed earlier in the tutorial:

  • Responsive: The adoption of the reactive programming paradigm should help us achieve end-to-end non-blocking and hence a responsive application
  • Resilient: Kubernetes deployment with ReplicaSet of the desired number of pods should provide resilience against random failures
  • Elastic: Kubernetes cluster and resources should provide us the necessary support to be elastic in the face of unpredictable loads
  • Message-Driven: Having all service-to-service communication handled asynchronously through a Kafka broker should help us here

While this looks quite promising, it's far from over. To be honest, the quest for a truly reactive system should be a continuous exercise of improvements. We can never preempt all that can fail in a highly complex infrastructure, where our application is just a small part.

A reactive system thus will demand reliability from every part that makes the whole. Right from the physical network to infrastructure services like DNS, they all should fall in line to help us achieve the end goal.

Often, it may not be possible for us to manage and provide the necessary guarantees for all these parts. And this is where a managed cloud infrastructure helps alleviate our pain. We can choose from a host of services like IaaS (Infeastrure-as-a-Service), BaaS (Backend-as-a-Service), and PaaS (Platform-as-a-Service) to delegate the responsibilities to external parties. This leaves us with the responsibility of our application as far as possible.

9. Conclusion

In this tutorial, we went through the basics of reactive systems and how does it compare with reactive programming. We created a simple application with multiple microservices and highlighted the problems we intend to solve with a reactive system.

Освен това продължихме, въвеждайки реактивно програмиране, базирана на съобщения архитектура и услуга за оркестрация на контейнери в архитектурата, за да реализираме реактивна система.

И накрая, обсъдихме получената архитектура и как тя остава пътешествие към реактивната система! Този урок не ни запознава с всички инструменти, рамки или модели, които могат да ни помогнат да създадем реактивна система, но ни въвежда в пътуването.

Както обикновено, изходният код за тази статия може да бъде намерен в GitHub.