apisix-dashboard VS kubernetes

With various access control models and implementation methods available, constructing an authorization system for backend service APIs can still be challenging. However, the ultimate goal is to ensure that the correct individual has appropriate access to the relevant resource. In this article, we will discuss how to enable the Role-based access control(RBAC) authorization model for your API with open-source API Gateway Apache APISIX and Open Policy Agent (OPA).

The API Create phase is the first stage in the API product lifecycle management process where you design, orchestrate, transform, document, and test your API. At this stage, modern API gateways like Apache APISIX can be helpful to build your API from scratch or import API definitions from a range of sources like OpenAPI YAML/JSON structure to register Route and Upstreams.

Apache APISIX is an API Gateway. By default, it stores its configuration in etcd, a distributed key-value store - the same one used by Kubernetes. Note that in real-world scenarios, we should set up etcd clustering to improve the resiliency of the solution. For this post, we will limit ourselves to a single etcd instance. Apache APISIX offers an admin API via HTTP endpoints. Finally, the gateway forwards calls from the client to an upstream. Here's an overview of the architecture and the required certificates:

One of the key features of modern API Gateways such as Apache APISIX is its support for GraphQL APIs. APISIX makes it easy to manage and scale GraphQL APIs using its flexible configuration system and powerful plugins. One such plugin is the degrapghql plugin, which allows us to convert the GraphQL API into a REST API. In this post, we will explore this feature with an example.

Secure your API: Use an to secure API Gateway by adding authentication, rate limiting, and other security features. It reduces the number of exposed APIs, organizations can reduce surfaces of attacks.

An alternative exists, though, if you're using an API Gateway. I'll describe how to do it with Apache APISIX, but perhaps other gateways can do the same. grpc-transcode is a plugin that allows transcoding REST calls to gRPC and back again.

I've used Kong but currently evaluating APISix as it's a more 'free/open' API gateway.

In this article, we will show you how to create a custom connector for the open-source Apache APISIX API Gateway in Power Platform as an alternative to Azure API Management in case you are building up additional components to an existing system with usable APIs and your system's infrastructure is hosted on-premises or on other cloud services provider rather than Azure.

Here's how to do it with Apache APISIX.

Apache APISIX is an open-source, high-performance API gateway built on top of Nginx. One of its powerful features is the ability to create serverless functions, which are small, stateless programs that can extend the functionality of Apache APISIX. In this article, we'll cover the basics of the Apache APISIX serverless plugin and how it can be used to trigger serverless functions in response to events.

In the field of software development, efficiency and agility are always sought after. In the era of cloud-native apps, traditional deployment techniques—which are frequently laborious and prone to errors—are starting to become obstacles. This is when Kubernetes and GitOps come in handy.

Are you looking for a hassle-free way to deploy Moodle™ Learning Management Systems (LMS) on Kubernetes? Look no further! Krestomatio presents the LMS Moodle Operator, an open-source Kubernetes Operator designed to simplify the deployment and management of Moodle instances on Kubernetes clusters. Let's dive into what makes this tool a great choice for Moodle administrators and developers alike.

Securing access to your Kubernetes clusters is crucial as inadequate security measures can lead to unauthorized access and potential data breaches. However, navigating the complexities of Kubernetes access security, especially when setting up strong authentication, authorization, and network policies, can be challenging.

Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI applications that need to scale across multiple servers or cloud environments.

To learn more, you can start by exploring the official Kubernetes documentation.

With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

We closely monitor Kubernetes and cloud providers' updates by following official changelogsand using RSS feeds, allowing us to anticipate potential issues and adapt our infrastructure proactively.

"You are holding it wrong", huh?

From the homepage https://kubernetes.io/:

"Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications."

Do you see "not recommended for smaller-scale applications" anywhere? Including on the entire home page? Looking for "small", "big" and "large" also yields nothing.