Articles

GraphQL in Microservices With Spring and Angular

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The AngularAndSpringWithMaps project has been converted from REST endpoints to a GraphQL interface. The project uses Spring GraphQL to provide the backend interface. The Angular frontend uses the Angular  HttpClient to post the requests to the backend.

GraphQL vs REST From an Architectural Perspective

REST Endpoints

REST calls retrieve objects with their children. For different root objects, separate REST calls are sent. These calls can accumulate with the number of different root objects that the frontend requests. With relationships between the objects, sequential calls become necessary.

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Improve Microservices Security by Applying Zero-Trust Principles

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This is an article from DZone’s 2022 Enterprise Application Security Trend Report.

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According to a 2020 Gartner report, it is estimated that by 2023, 75 percent of cybersecurity incidents will result from inadequate management of identities and excessive privileges. To a large extent, this is attributable to the increased number of identities used by modern cloud infrastructures. Applications run as microservices in fully virtualized environments that consist of dynamically orchestrated clusters of multiple containers in the cloud. 

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Kubernetes-Native Inner Loop Development With Quarkus

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Microservices today are often deployed on a platform such as Kubernetes, which orchestrates the deployment and management of containerized applications. Microservices, however, don’t exist in a vacuum. They typically communicate with other services, such as databases, message brokers, or other microservices. Therefore, an application usually consists of multiple services that form a complete solution.

But, as a developer, how do you develop and test an individual microservice that is part of a larger system? This article examines some common inner-loop development cycle challenges and shows how Quarkus and other technologies help solve some of these challenges.

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Developing Cloud-Native Applications With Containerized Databases

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With the advent of microservices in Kubernetes, individual developer teams now manage their own data, middleware, and databases. Automated tests and CI/CD pipelines must be revisited to include these new requirements.

This session demonstrates how to use Kustomize and Tekton to provide Kube-Native automated workflows taking into account new parameters such as database operators, StorageClass, and PVC.

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Minimal API Using .NET Core 6 Web API

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We will go over the purpose of minimal APIs in.NET Core 6, as well as how to implement them step by step.

Prerequisites

  • .NET Core 6 SDK
  • Visual Studio 2022
  • SQL Server

Introduction

  • Minimal APIs are used to create HTTP APIs with minimum dependencies and configuration.
  • Mostly, it is used in microservices that have fewer files and functionality within a single file.
  • But there are a few things that are not supported in minimal APIs, like action filters and built-in validation; also, a few more are still in progress and will get in the future by .NET Team.

Step-by-Step Implementation Using .NET Core 6

Step 1

Create a new .NET Core Web API.

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Are We There Yet? When Do We Move to GraalVM?

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This is the perfect time to raise this point — just as Spring Native is coming to the forefront. Is it time to move to GraalVM? Spoiler: it depends. Yes, if you’re building serverless, probably no if you’re building pretty much anything else — with a few exceptions for some microservices.

Before I begin, I want to qualify that I’m talking about native image (SubstrateVM) which is what most people mean when they say GraalVM. That specific feature took over a much larger and more ambitious project that includes some amazing capabilities such as polyglot programming. GraalVM native images let us compile our Java projects to native code. It performs analysis and removes unnecessary stuff, it can reduce the size and startup time of a binary significantly. I’ve seen 10-20x improvement to startup time, that’s a lot. Ram usage is also much lower sometimes by a similar scale but usually not as significant.

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Manage Microservices With Docker Compose

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As microservices systems expand beyond a handful of services, we often need some way to coordinate everything and ensure consistent communication (avoid human error). Tools such as Kubernetes or Docker Compose have quickly become commonplace for these types of workloads. Today’s example will use Docker Compose.

Docker Compose is an orchestration tool that manages containerized applications, and while I have heard many lament the complexity of Kubernetes, I found Docker Compose to have some complexities as well. We will work through these along the way and explain how I solved them.

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Advancements in Cloud-Native and Kubernetes Observability

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This is an article from DZone’s 2022 Kubernetes in the Enterprise Trend Report.

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In today’s world, it’s more important than ever to have visibility into your system’s performance and health. Modern applications rely on complex microservices architectures and cloud-native technologies, like Kubernetes. Observability helps us understand not just application behavior, but also infrastructure configuration changes and dependencies, as they happen in real-time. 

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Data Management Patterns for Microservices

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This is an article from DZone’s 2022 Database Systems Trend Report.

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One of the key components of microservices is how to manage and access data. The means to do that are different compared to traditional monolithic or three-tier applications. Some patterns are quite common, but others are specific and need to be evaluated before being incorporated into a solution. We will briefly go over some of these common database patterns for microservices before exploring CQRS (including how it differs from CRUD) and, finally, look at how it can be combined with event sourcing.

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Reverse Pull-Requests

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In a fast-paced world, more teams have microservices architectures and are making the shift to Continuous Deployment and Trunk-Based Development. For one of our client’s teams, that meant no feature branches, pairs always committing to main, pushing frequently (multiple times per hour, as often as every 1–4 commits) and those changes landing in production 20–30 minutes later.

With pair programming, no feature branches, and such continuous change, code reviews would seem redundant or extremely difficult with little in the way of tooling support. How on earth would you use GitHub’s Pull Request review features in this setting when there’s no feature branch to diff?

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