nodejs-pubsub VS kubernetes

Secondly, Go is incredibly easy to learn and in my opinion, maintain. This means that if you're a growing company and expect to onboard new teams and team members, having Go as a basis for your systems should mean that new engineers can get up to speed quickly.  Below is a small sample application that can connect to Google PubSub, subscribe to a topic, send an event and then clean up. In total, its 82 lines of code including liberal line breaks. Even if you have never written or read a line of Go before, I hope you'll agree that it's quite clear and readable:

Pub/Sub

Google Cloud Pub/Sub is a fully-managed, globally scalable and secure queue provided by Google Cloud for asynchronous processing messages. Cloud Pub/Sub has many of the same advantages and disadvantages as SQS due to also being cloud hosted. It has a free and paid tier.

Cloud Pub/Sub: A global messaging service for event-driven architectures

Google Cloud Pub/Sub (*https://cloud.google.com/pubsub*)

Google Cloud Functions is a FaaS offering from Google Cloud Platform (GCP). It allows developers to run their code in response to events, such as changes in a database or the arrival of a message in a Pub/Sub topic. Like AWS Lambda, Google Cloud Functions can be used to build a variety of applications, including serverless websites, data processing pipelines, and real-time data streams.

that gets triggered when a Pub/Sub topic is fired (from the webhook function)

Maybe Pub/Sub from GCP?

I'm curious - given your limited knowledge, is there a reason you're looking to self host this in your own cluster rather than using a managed service like https://cloud.google.com/find-a-partner/partner/confluent-inc?redirect= or just native Google PubSub https://cloud.google.com/pubsub/ ?

Pub/Sub, the GCP managed service for message queuing, has two levels of services on standard and one Lite. Standard is the high availability version of it and Lite could be a zonal or regional service with infrastructure managed by the client. Obviously, the model pricing will be very different with a x10 between Standard and zonal Lite. However, the model pricing is the same is based on throughput for message publishing, message storage costs and egress for message distribution. Here, we totally break the similarity with a VM model (except on storage). Everything is drived on volumetry and performance of inbound and outbound messages.

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.