workflows-samples VS kubernetes

Have you seen this? https://cloud.google.com/workflows

Cloud Workflows: A serverless workflow orchestration service

Workflows can trigger actions, like Cloud Run Jobs, in a sequence of steps. The Workflows product waits for the job to complete, fail, or time out before it moves on to the next step. It uses polling to check on the job, so there may be a delay between the job finishing and the next step.

Has anyone integrated firestore/realtime database together with GCP workflows? What was your use case? How was your experience with it? Why have you decided to go that way?

In the past few years, YAML (http://yaml.org) has become an essential part of software, particularly for infrastructure-as-code tools. Yaml at the heart of kubernetes configuration, kubernetes-inspired APIs like Google's config connector, and a number of workflow systems like Google Cloud Workflows and Github Actions.

If your routine is just a bunch of API calls, you can also replace steps 1-2 with Workflows.

I have come at this problem from a bit of a different angle by asking what is the closest I can possibly get to the hypothetical dream state of everything is automated, autoscaling blah blah blah as possible with the absolute smallest budget in terms of not only actual costs but time budget as well.

I only know the GCP ecosystem kind of well so I don't fully know to what extent these things exist in AWS and Azure but there I think there is a really nice path you can get on with the serverless route that skips K8s entirely but keeps you very well aligned in case you ever need to "upgrade" or get out of the GCP ecosystem.

I write very stock standard gRPC services and then put them onto Cloud Run (which has a very Heroku like workflow) and stick https://cloud.google.com/api-gateway in front of things and now my API is running on the exact same setup as any other service Google is running in production. Huge amounts of logic get moved out of my code base as a result.

If you are also willing to write your APIs a fairly particular way https://google.aip.dev/ it starts to become trivial to integrate other things like https://cloud.google.com/workflows, https://cloud.google.com/pubsub and https://cloud.google.com/tasks which is traditionally where a lot of the "state" and weirdly complicated logic previously lived in my code. I'm now not really writing any of that.

Now it's all declarative where I just say what I want to happen and I don't have to think about much else beyond that because it too is using that same internal GCP infrastructure to handle all the complicated parts around what to do when things go wrong.

But to me they are all extremely heavily aligned with the K8s path so the lock in certainly doesn't feel as scary.

Google Workflows combines Google’s cloud services and APIs to help developers build reliable large-scale applications, process automation, and deploy machine learning and data pipelines.

Lately, I build a system using Cloud Workflows which can combine Google Cloud Services such as Cloud Functions and Cloud Run. Sometimes, I was in a situation where I want to examine more efficiently using logs on Cloud Logging when debugging or daily monitoring.

Look at Cloud Workflows for simple workflows

I’m joking of course. I’m not really sure about what a faded keyboard says about its owner. What I do know for sure is how important kubectl is to anybody who wants to be a proficient Kubernetes administrator.

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.