You are running multiple microservices

This reference architecture shows a microservices application deployed to Azure Kubernetes Service (AKS). It describes a basic AKS configuration that can be the starting point for most deployments. This article assumes basic knowledge of Kubernetes. The article focuses mainly on the infrastructure and DevOps considerations of running a microservices architecture on AKS. For guidance on how to design microservices, see A reference implementation of this architecture is available on Architecture Download a If you would prefer to see a more advanced microservices example that is built upon the Workflow The architecture consists of the following components. Azure Kubernetes Service (AKS). AKS is a managed Kubernetes cluster hosted in the Azure cloud. Azure manages the Kubernetes API service, and you only need to manage the agent nodes. Virtual network. By default, AKS creates a virtual network into which agent nodes are connected. You can create the virtual network first for more advanced scenarios, which lets you control things like subnet configuration, on-premises connectivity, and IP addressing. For more information, see Ingress. An ingress server exposes HTTP(S) routes to services inside the cluster. For more information, see the section Azure Load Balancer. After creating an AKS cluster, the cluster is ready to use the load balancer. Then, once the NGINX service is deployed, the load balancer will be configured with a new public IP that will front your ingress controller...

I'm using Spring Boot 1.5.9 and we have a micro service that connects with another micro services. For integration test and development (the developer computer) I would like to mock/stub the rest calls made for another micro services. For unit/integration tests this question is already answered many times: WireMock, MockBean and MockRestServiceServer are good tools. But for development I'm trying to figure out the best approach. Why? Is a problem to start each microservice that the actual microservice depends. So the idea is make the microservice independent and start without any additional server running in the developer's machine or on other place. So, a simple `mvn spring-boot run' would be enough to start my microservice and work without any concern about another microservices. So, my question is: what the recommended aproach for mock/stub rest calls in development? Wiremock is a good approach in this case too? My project I have two profiles in src/main/java/resources/: the 'default' (application.properties, that's used for another environment) and the 'dev' (application-dev.properties, used for development). In my tests ( src/test/java/resources/) I have only one application.properties, that is basic a copy of application-dev.properties. I already try to use Wiremock for integration tests and works well. But I would not like to use it for development too because I don't like so much the idea to introduce a dependency only used for development in my JAR. My intention i...

I've been doing some research on microservices, and one question I've had that is not well addressed is whether there should be multiple instances of a microservice, and if so, how to deal with this? Suppose for example I have a system of two microservices, A and B where A depends on B. Looking at B, is B supposed to be a single instance always, and handle everything A throws at it, or is it possible to create a pool of service Bs, so they can handle the load? If the latter, how does one manage a pool of microservice B instances, with respect to load balancing and their data ownership? Answer mostly depends on your scalability/uptime/failover requirements. If it's necessary to have highly resilient architecture, then yes, you need to have multiple instances (sometimes even distributed across multiple data centers). This is actually one of the benefits of microservices - you can scale different services independently - some of them are more performance critical than others so you create more instances of these. Contrast this with monolithic applications where you can scale just the whole monolith (ignoring the fact different requirements for different parts of the monolith). If you're lucky, then you microservice is stateless - all instances are completely isolated. This is great because you can scale them almost infinitely. But often you have some state which needs to be distributed among the individual nodes. If you have relational database, you can have all nodes connect...

Some of the most innovative and profitable microservices architecture examples among enterprise companies in the world — like Amazon, Netflix, Uber, and Etsy — attribute their IT initiatives’ enormous success in part to the adoption of microservices. Over time these enterprises dismantled their monolithic applications. They then refactored them into microservices-based architectures to quickly achieve scaling advantages, greater business agility, and unimaginable profits. In this article, we’ll explore the microservices journeys of these wildly successful enterprises. But first, let’s look at the general circumstances that inspire enterprises to use microservices in the first place. Here’s the main things to know about microservices: • • • • What are Microservices? Quoting James Lewes and Martin Fowler, who played a key role in promoting What are Microservices in Java? Microservices in Java refers to a software architecture pattern where an application is built as a collection of small, independent, and loosely coupled services that work together to provide a complete functionality. Each microservice is a standalone component that can be developed, deployed, and scaled independently, allowing for greater flexibility and scalability in large-scale applications. In the context of Java, microservices are typically implemented using frameworks and tools that support the development of distributed systems. Some popular Java-based frameworks for building microservices include Sp...

In a microservices architecture, each microservice exposes a set of (typically) fine-grained endpoints. This fact can impact the client-to-microservice communication, as explained in this section. Direct client-to-microservice communication A possible approach is to use a direct client-to-microservice communication architecture. In this approach, a client app can make requests directly to some of the microservices, as shown in Figure 4-12. Figure 4-12. Using a direct client-to-microservice communication architecture In this approach, each microservice has a public endpoint, sometimes with a different TCP port for each microservice. An example of a URL for a particular service could be the following URL in Azure: http://eshoponcontainers.westus.cloudapp.azure.com:88/ In a production environment based on a cluster, that URL would map to the load balancer used in the cluster, which in turn distributes the requests across the microservices. In production environments, you could have an Application Delivery Controller (ADC) like A direct client-to-microservice communication architecture could be good enough for a small microservice-based application, especially if the client app is a server-side web application like an ASP.NET MVC app. However, when you build large and complex microservice-based applications (for example, when handling dozens of microservice types), and especially when the client apps are remote mobile apps or SPA web applications, that approach faces a few iss...

In this article, we will look at how to identify and fix performance issues in Go programs using the pprof and trace packages. We will begin by covering the fundamentals of the tools, then delving into practical examples of how to use them. By the end of this article, you will have a solid understanding of how to use these powerful tools to improve the performance of your Go applications. DuckDB is an open-source OLAP database for analytical data management that operates as an in-process database, avoiding data transfer overhead. Leveraging vectorized query processing and Morsel-Driven parallelism, the database optimizes performances and multi-core utilization for analytical data processing. Key Takeaways • Creating distributed systems is not an easy task. Best practices have emerged around “microservice” architecture and “12-factor app” design. These provide guidelines related to the delivery lifecycle, networking, state management, and binding to external dependencies. • However, implementing these principles consistently, and with an approach that is scalable and maintainable, is challenging. • Traditional technology-focused approaches to addressing these principles included Enterprise Service Bus (ESB) and Message-Oriented Middleware (MOM). While these solutions offered a good feature set, the main challenge was the monolithic architecture and tight technological coupling between business logic and platform. • As cloud, containers, and container orchestrators (Kubernet...

pattern deployment Pattern: Multiple service instances per host Context You have applied the Problem How are services packaged and deployed? Forces • Services are written using a variety of languages, frameworks, and framework versions • Each service consists of multiple service instances for throughput and availability • Service must be independently deployable and scalable • Service instances need to be isolated from one another • You need to be able to quickly build and deploy a service • You need to be able to constrain the resources (CPU and memory) consumed by a service • You need to monitor the behavior of each service instance • You want deployment to reliable • You must deploy the application as cost-effectively as possible Solution Run multiple instances of different services on a host (Physical or Virtual machine). There are various ways of deploying a service instance on a shared host including: • Deploy each service instance as a JVM process. For example, a Tomcat or Jetty instances per service instance. • Deploy multiple service instances in the same JVM. For example, as web applications or OSGI bundles. Examples Resulting context The benefits of this pattern include: • More efficient resource utilization than the The drawbacks of this approach include: • Risk of conflicting resource requirements • Risk of conflicting dependency versions • Difficult to limit the resources consumed by a service instance • If multiple services instances are deployed in the same...