With microservices architecture becoming the de facto standard for web applications now, effective debugging and anomaly detection calls for a system that is observable — which means, the internal state of an application can be inferred by observing and tracking the metrics, traces, and logs.
Observability is all about data exposure and easy access to information required to find issues when the communications fail, internal events do not occur as expected or events occur when they shouldn’t. Here, you’ll learn and know about different microservices monitoring tools and how to monitor microservices. Let’s take a look!
The Saga Pattern
The saga pattern is used to provide data integrity across multiple distributed, isolated services involved in potentially long-lived activities (sometimes referred to as business activities or business transactions to disambiguate). The classic example, and the one I use here and in the corresponding workshop, is a travel booking consisting of flight, hotel, and car reservations.
The original paper was published in 1987 and alludes to the advantages of an implementation that runs and stores data within a database and that is precisely what is being done in the Oracle database, making it a perfect fit for microservices and modern applications development for a number of reasons.
Stateful microservices are not a new concept. They have their pros and cons and can shine in highly loaded systems. There are examples of using stateful microservices with Apache Cassandra on board. In this article, I will describe how you can combine this approach with Apache Ignite.
Stateless Architecture
The traditional microservices architecture is stateless. In this case, the database cluster is deployed away from the application instances, as is the distributed cache. In case of increased load, each of these elements is scaled independently.
Introduction
I left Microsoft after 19 years, where I led teams that built system software for highly scalable cloud applications. This included leading development of the Microsoft Orleans framework from its inception at Microsoft Research until it became one of the most successful open-source projects within the .NET ecosystem. Orleans powers a number of large-scale Microsoft systems such as Xbox Game Services, Skype, Azure IoT, Azure ML, Azure Active Directory, and many more cloud services outside Microsoft. So if you’ve ever played online multiplayer games like Halo or Call of Duty, our team built much of the underlying infrastructure that supports it.
When I originally joined Orleans, cloud computing was still in its infancy. We had a 10,000-foot vision and not a single line of actual code. We needed to reimagine how cloud-scale applications should be coded because, at the time, available and high-performance scalable systems were only achievable by experts. And while everyone knew the cloud was coming, we had no idea how to build applications in a way that ensured they would be accessible and productive for millions of software engineers.
Netflix, eBay, Twitter, Paypal, Uber – these huge, household-name businesses all started out using traditional monolithic architectures.
But, as these organizations grew, they began to realize that using monoliths would no longer be viable for their operational needs. Thus, they turned to microservices, taking advantage of how this modern architecture can scale with rapid growth, constantly changing consumer demands, and unpredictable business environments.
I’m sure you want to build scalable apps, right? Who doesn’t? If so, you must have come across the words “Cloud Native.” This approach is like an angel which can resolve most of your scaling challenges. So, what exactly is cloud native?
Cloud native is an approach used to build apps which can harness all the capabilities of the cloud.
What Is a Service Mesh?
As enterprises are moving from monolithic to microservices/distributed microservices to achieve more resiliency and performance, service mesh has a key role in that kind of digital transformation.
A service mesh is a dedicated infrastructure layer for handling service-to-service communication. It’s responsible for the reliable delivery of requests through the complex topology of services that comprise a modern, cloud-native application. In practice, the service mesh is typically implemented as an array of lightweight network proxies that are deployed alongside application code, without the application needing to be aware. Service mesh has mainly two major components — the control plane and the data plane, which decouples the business logic from technical complexities.
Introduction
In this article, we will be covering the steps to create a Java WebSocket server, powered by Spring Boot, that communicates with Java clients and supports load balancing across multiple instances. We’ll start by describing a use case scenario, analyzing the requirements, and then we’ll proceed to choose the technology stack that suits the case. We’ll implement the code, and finally test the application to get some performance and durability benchmarks.
Use Case: A Smart Home
The first thought we had when writing this, was to go with the classic demonstration of WebSockets usage that is the chat application. But if you were to search the internet about WebSocket examples, 99% of them are about chat apps. So we needed to come up with another idea, one that would be more fascinating and relevant to today’s technology, and would cover both point-to-point communication, as well as one-to-many broadcasting. And that is a smart home device network.
As a thriving software development technique, microservices — and its underlying architecture — remain foundational to cloud-native applications. Apache Cassandra is a natural complement given that it's a database designed for the cloud. This Refcard examines the benefits of microservices architecture, demonstrates recommended data modeling techniques, and explains key microservice design principles for Cassandra using a sample hotel application.
An enterprise normally has a few thousand microservices, having autonomy for each team in selecting its own choice of the technology stack. Therefore, it’s inevitable that an enterprise should have a microservices governance mechanism to avoid building an unmanageable and unstable architecture.
Any centralized governance goes against the core principle of microservices architectures i.e. “provide autonomy and agility to the team.” But that also doesn’t mean that we should not have a centralized policy, standards, and best practices that each team should follow. With an enterprise-scale of integrations with multiple systems and complex operations, the question is, “How do we effectively provide decentralized governance?”
We’re seeing more businesses utilize microservices, service meshes and APIs to break down large, static applications and merge legacy systems with modern IT platforms. These agile and flexible application structures have changed the way we exchange data and are typically the method of choice when sharing data with external parties.
Microservices architecture is ideal for developing and updating mobile applications because it can simplify data sharing. In fact, according to recent research from Advanced Market Analytics “mobility and app proliferation is the primary factor augmenting the demand for API management” and they also point out “API security issues” as a potential constraint to growth.
There is an endless supply of blog posts, white papers, and slide decks, evangelizing the virtues of microservices. They talk about how microservices “increase agility,” are “more scalable,” and promise that when you make the switch, engineers will be pounding at your office door looking for a job.
Let’s be clear, though occasionally exaggerated, the benefits of microservices can be real in some cases. Particularly for large organizations, with many teams, microservices can make a lot of sense. However, microservices aren’t magic — for all of their benefits, they come with significant drawbacks. In this article, I’ll describe how the distributed nature of microservices makes them inherently more complex.
Introduction
The microservices architecture pattern is getting a lot of attention these days and it’s trending. If it is hard to believe, then just check what Google Trends says.
You can see a big spike of interest over this term, starting in 2014, and the trend keeps on increasing as time passes.
Learn how to design an optimal microservice architecture.
Microservices architecture is being fast adopted by enterprises to create flexible, scalable applications that can be iterated fast, with high fault tolerance and low downtimes. How do you build the right microservices architecture?
While the exact architecture will vary, there are certain best practices that help design effective and optimal microservices architecture.
Find out how to build microservices with Node.js.
Introduction
Building real-world applications in the JavaScript language requires dynamic programming, where the size of the JavaScript application grows uncontrollably. New features and updates get released, and you need to fix bugs to maintain the code.
To execute this, new developers need to be added to the project, which becomes complicated. The structure of modules and packages is unable to downsize and simplify the application. To run the application smoothly, it is essential to convert the large, homogeneous structure into small, independent pieces of programs. Such complexities can be resolved effortlessly when JavaScript applications are built on microservices, more so with the Node.js ecosystem.
Find out more about the benefits of microservices architecture.
Kubernetes is increasingly becoming the de-facto standard with more software and applications workloads moving into containers. Kubernetes has captured the cloud market by storm through the implementation of strong infrastructure development. It makes deploying and managing the app easier and improves reliability and reduces the time you need to spend on DevOps.
According to Gartner's report, by 2022 more than 75% of organizations globally will be running containerized applications.
Do you know what a service mesh is?
"Service mesh" is an umbrella term for products that seek to solve the problems that microservices' architectures create. These challenges include security, network traffic control, and application telemetry. The resolution of these challenges can be achieved by decoupling your application at layer five of the network stack, which is one definition of what service meshes do.
Learn more about Spring boot!
In this article, I'm going to propose my list of "golden rules" for building Spring Boot applications, which are a part of the microservices-based system. I'm basing on my experience in migrating monolithic SOAP applications running on JEE servers into REST-based small applications built on top of Spring Boot. This list of best practices assumes you are running many microservices on the production under huge incoming traffic. Let's begin.
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