Learn to use GraphQL API gateways with a single-defined schema to improve microservice architecture and future development.
As your organization grows, it’s common for multiple API services to be created, each one providing its own feature set. Alongside these services, you’ll want to provide different client apps for your users to use your product. Eventually, your architecture might look like this:
Every client app has different needs.
There are many reasons why this happens. Over time, team structure may have directed service creation, and at the time, it was easier to have a single team own a single codebase instead of having a web of teams to service ownership.
Another reason could be that different features in your product had different scaling concerns. For example, your analytics stack may have drastically different needs than your user login stack, and it doesn’t make sense to combine the two.
Whatever the reasons are, this type of architecture will slow down future development for both the backend service teams and frontend client teams. Client apps will need to interface with multiple protocols, use different authentication strategies, worry about which API gives them what type of data, and potentially make multiple API calls to retrieve data for a single page.
Instead of refactoring every API service or re-building the entire architecture — which is costly and risky — API gateways can help in this situation.
Migrating containerized workloads to Kubernetes? See which Kubernetes resources work best, and how you can get multiple replicas of your application running simply, and quickly.
What You’ll Learn
- How Kubernetes deploys and manages groups of pods
- How to distinguish between the different use cases for a Kubernetes Deployment, DaemonSet, and Job
- How to create a YAML manifest for a Deployment
- How to create and inspect a Kubernetes Deployment
Shirts and Gifts for Your Friends & Loved ☞ https://bit.ly/36PHvXY
How to Host a 100% FREE Website with Wix. Learn how to host a free website on Wix that comes with a free subdomain name. With the free Wix plan, you get free hosting as well as 500MB storage and bandwidth.
Learn the basics of Microservices with Spring Boot, Spring Data, and Spring Data Rest.
#microservice #spring boot #spring
First Android App in Visual Studio 2022 | Visual Studio 2022 Preview
“Make the world become more programmable.”
That’s the goal of Nordic APIs, which holds a series of conferences and events throughout Scandinavia and, more recently, the US, to help organizations make smarter tech decisions and streamline their operations through APIs and strategies. Their work explores the API sector and sheds to light various emerging technologies and trends through their events and blogs.
In this round of Cocktails, we have Nordic APIs’ editor-in-chief, who gives us an insider perspective behind their work at Nordic APIs. We also dive into the current trends within the API space, where we talk about the different API styles and specifications that have emerged, some exciting prospects and innovations from industry bodies and providers, as well as some interesting insights as to how APIs can move the industry forward.
Kevin Montalbo: All right. Joining us today from Sydney, Australia is TORO Cloud CEO, and founder, David Brown. Good day, David!
David Brown: Good day, Kevin!
KM: Our guest for today is a well-known tech journalist who tracks the API economy, as well as covers DevOps topics, and analyzes state-of-the-art technologies in the enterprise cloud software space.
He’s the Editor-in-Chief for NordicAPIs, an API event company that also runs a high-impact blog on API practice. Interviewing key players, and sharing insights through his evergreen articles, he’s been featured in several publications such as ProgrammableWeb, Tech Beacon, DevOps.com, Container Journal, CMO.com, ReadWrite, and many more. He also speaks at API conferences, online events, and podcasts, such as this one.
Ladies and gentlemen, joining us today for Coding Over Cocktails is Bill Doerrfeld. Hi Bill, it’s great to have you on the show! How are you doing?
#api #microservice #podcast #openapi
Visual Studio 2022 | Download & Installation
Microsoft Visual Studio is an integrated development environment (IDE) from Microsoft. It is used to develop computer programs, as well as websites, web apps, web services and mobile apps. Visual Studio uses Microsoft software development platforms such as Windows API, Windows Forms, Windows Presentation Foundation, Windows Store and Microsoft Silverlight. It can produce both native code and managed code.
Windows Application Packaging with Visual Studio 2019
application packaging in visual studio 2019
You can use windows application packaging project in visual studio to generate a package for your desktop application. Afterwards, you can distribute your package to the Microsoft store, on the web, in your enterprise or any other distribution mechanism.
To be able to view the windows application packaging project in visual studio, we must at least install one of these workloads
• The ‘Universal Windows Platform development’ workload
• The Optional Component ‘MSIX Packaging Tools’ in the NET Core workload.
• The Optional Component ‘MSIX Packaging Tools’ in the .NET desktop development workload.
Microsoft Excel Web App in Visual Studio 2019 | Getting Started
Integrate Microsoft excel and web applications with excel add-in and visual studio 2019.
MS Excel is a commercial spreadsheet software developed by Microsoft for Windows, Mac OS and other Operating systems.
VBA, visual basic application is the programming language used for applications in excel.
In computer science, dependency injection is defined as a pattern, whereby one component gets other components (dependencies) from outside. Numerous posts were written about various implementations of dependency injection in Vertx using the Google Guice library. All of them are good and I do not want to reinvent a bicycle here and repeat the same things again. However, in my opinion, it is a good idea to give a bit more systematic approach to this topic. In my experience, in most cases, for developers is not a big deal to implement a basic DI with Vertx, rather it seems hard to incorporate Vertx components into the DI pipeline. In this post, we will review how to do dependency injection in Vertx with Google Guice and how to build a basic injection that has the
ConfigRetriever component (Vertx’s way to obtain an application configuration). Please note, that in this article we use Futures API, so it is focused on Vertx 4.x developers.
Google Guice is an established solution to implement a dependency injection technique in Java applications. Like most established libraries, it is quite simple to use, yet it does not mean that it is limited in any way. This is a very flexible and powerful solution. The main building blocks of the framework are modules and injectors. The module is used to define how to get dependencies. The injector serves as a main entry point of an application and is used for an actual component initialization. Let have a quick example, that uses a constructor injection technique. Imagine, that you develop a verticle, that has two external dependencies — a client class (that performed HTTP calls) and a repository class (that does database operations). For sure, we will not do here their precise implementations, because it is out of the scope of the post. In order to specify these dependencies classes inside the verticle, we need to use an
@Inject annotation. If you are familiar with the Spring framework, you would find a process familiar. Basically, we need to create fields to keep references for components, define a constructor and annotate it with the
@Inject, so Guice will know that we use constructor injection.
#java #vert.x #vertx #microservice
Too often, technical options are framed as an “either/or” proposition. There seems to be a sense that if you use technology A, then technology B cannot be implemented; the reality, however, is that the relationship between technologies is often so complex that they defy such comparisons.
Very few things in the software world fall into this paradigm as strongly as containers and microservices. Often discussed as competing strategies, the two represent different ways of thinking that can be leveraged to provide a better overall product.
Below, we’ll compare containers and microservices. We’ll look at what makes them suitable to the average developer, what some strengths and weaknesses are, and some use cases that they are appropriate for. We’ll also discuss some standard technologies behind both and look to the future to see where they are going.
What is a Container?
In its most basic form, a container is simply an isolated process. It is a collection of segmented resources and application functions to do one specific thing — it is, quite literally, a “container” filled with all the code, dependencies, and a runtime environment required to perform a process.
A container is essentially like a car. Cars contain everything a person could conceivably need to get from one location to another in relative comfort and safety. From the engine, the air conditioner, to the carburetor, everything is designed for the specific form function, and there’s very little extraneous functionality. You’ll never find a standard-issue sedan built with an oven inside, because that’s not within the domain of what it’s supposed to accomplish.
This limit in functionality and pre-packaged form factor delivers several benefits. Chief among them is the fact that it allows for portability across stacks and platforms. When a container contains everything required to function, it is platform agnostic — the container can do the work it needs to do regardless of where it lives. Additionally, this makes the containers lightweight and efficient, as there’s minimal outside dependency or unexpected requirements that make operations heavy.
#docker #graphql #kubernetes #microservice #rest apis
In this video I challenge myself to think more like an AI in order to become a better software designer, developer and architect.
I explore how artificial intelligence may design microservices in the cloud by using Commerce microservices as an example. I figure that an AI would probably have a very different physical model from it’s logical model by optimizing for performance and cost. I then think about how we could use those same techniques and software design patterns when designing our own applications.
No matter how long you work as an application developer and no matter what programming language you use, you probably still struggle to increase your development productivity. Additionally, new paradigms, including cloud computing, DevOps, and test-driven development, have significantly accelerated the development lifecycle for individual developers and multifunctional teams.
You might think open source tools could help fix this problem, but I’d say many open source development frameworks and tools for coding, building, and testing make these challenges worse. Also, it’s not easy to find appropriate Kubernetes development tools to install on Linux distributions due to system dependencies and support restrictions.
Fortunately, you can increase development productivity on Linux with Quarkus, a Kubernetes-native Java stack. Quarkus 2.0 was released recently with useful new features for testing in the developer console.
If you need to add more than 10 dependencies (e.g., database connections, object-relational mapping, JSON formatting, REST API specifications) to your Java Maven project, you must define more than 60 configurations with keys and values in one or more
application.properties files. More configurations decrease readability for individual developers and are harder for developer teams to manage.
Quarkus has an interactive interface to display all dependencies that have been added. It is available at the
localhost:8080/q/dev endpoint after you start Quarkus dev mode with the
mvn quarkus:dev command. You can also update configurations in the DEV user interface (UI), as Figure 1 shows, and the changes will automatically sync with the
(Note: You can find the entire Quarkus application code for this article in my GitHub repository.)
Figure 1. Quarkus DEV UI
#cloud #quarkus #microservice
It’s a common story: the product team gets early success and grows into a large monolithic code base. While everything is in a single code base, features can be added quickly. This is partly due to the ability to leverage shared code across each feature in the codebase.
When your team is adding a new feature, a developer can leverage the existing codebase for needs such as logging or special error handling. This gives developers more time to focus on writing the code that brings immediate value for the end-user.
The tricky part comes as the product and organization continue to grow. With growth, it becomes cumbersome to add new features to the codebase. Soon there are too many developers working on the same code, and, with so many concurrent changes, coordinating releases gets more difficult. Faced with this issue, many organizations decide to break up the monolith into microservices.
While it enables more developers to work independently from each other, breaking things up introduces new obstacles. One new problem is the additional complexity and unreliability of network calls between services.
This complexity also slows down development. It creates the need to resolve the same types of problems every time a new service is added to the architecture. The service can no longer just call previously written functions from other features.
Now the team has to decide how to handle the situation.
What third-party libraries should you use? What’s the right interface? How much should be tested? This is a cost incurred by every team that is forced to spend time implementing redundant solutions to cross-cutting concerns instead of solving the primary issues for that service.
One possible solution is to use common internal shared libraries across microservices. This approach enables teams to share solutions to common problems while retaining the autonomy to use custom solutions where needed.
Luca Palmieri discusses his HTTP mocking crate, wiremock-rs, and the challenges of black-box testing in a microservice environment.
00:03:05 Unit vs Black Box Tests
00:06:46 Disadvantage of Unit Tests
00:07:57 How software is really used
00:09:22 Black box tests allow you to refactor
00:10:55 Anatomy of a microservice
00:13:45 Implications for testing
00:23:13 Exploring a codebase with wiremock
00:29:50 Q: does wiremock work with gRPC?
00:32:01 Q: can wiremock be set up as a proxy?
00:35:25 Q: what are the advantages over HTTPmock?
00:37:29 Q: what about other protocols besides HTTP?
#rust #microservice #wiremock-rs