nodejs-pubsub VS workflows-samples

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.

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