go-cloud VS prometheus

Even when going multi-cloud you can employ different strategies. Vault is definitely one of them, but you can also use federation to exchange one cloud's credentials for another's, giving you the ability to centralize secrets in one of them. You can use a layer of abstraction like GoCloud [0]. You can also build for each cloud separately and decide either not to centralize secrets at all, or build some trivial bespoke tooling to synchronize some of them. I'm not endorsing any of the options, just pointing out that Vault isn't the only one.

https://github.com/google/go-cloud

> If Google started adding Google Cloud specific primitives natively to Go would you call that forward thinking as well?

Go actually ships with a quite forward thinking SQL interface. It's an abstract interface over a DB, and you just import the "driver" that powers it. The driver conforms to a standard interface, so all of them behave roughly the same.

I think this is what everyone wants from Deno/etc - why can't there also be a KV interface that's universal, or a Queue interface that's universal?

People attempted this w/ go [1], where it attempts to use the same nice experience of the SQL logic, but it never seemed to gain traction.

https://gocloud.dev

github.com/google/go-cloud/pubsub package provides a set of interfaces and tools to work with publish/subscribe messaging. This package allows easy communication between independent components by decoupling the sender and the receiver.

I'm guessing this is a reasonable example of what they're using it for? server.go

https://github.com/google/go-cloud probably out of context, but not IaC but agnostic backend development with Go across multiple clouds

The plan is to have a process for generating AWS CDK targeting Lambda (pluggable providers, but start with AWS CDK, because it's what I use), and to use the Google Cloud Development Kit (also called CDK, but not the same) https://github.com/google/go-cloud to abstract the services.

In this post, I will talk about an exciting project maintained by the team that develops the Go language: the Go Cloud Development Kit, also known as the Go CDK.

The API of gocloud.dev, is stable. We are at ariga.io, already use gocloud.dev for internal service, and even in the public for easy adopt multi-clouds provider: https://github.com/ariga/atlas/commit/ef0b0eae65a61375482497ceb9ed9790a469b56e

On Go, which has a community focused on the cloud, there is even GoCloud, a library with a single, common, and high-level API that allows an application to support any of those clouds and even on-premise alternatives for those services. All can be configurable at deploy time by the infrastructure team.

WriteRequest::timeseries is a vector (https://github.com/prometheus/prometheus/blob/main/prompb/re...) and

Developers widely use Prometheus as a system for operational monitoring and alerting for their projects. Here is a list of tools for monitoring frontend services with Prometheus.

Just to give an example of the power of Go for CLI builds, you may have already used or at least heard of Docker, Kubernetes, Prometheus, Terraform, but what do they all have in common? They all have a large part of their usability via CLI and are developed in Go 🐿.

Distributed system administrators need mechanisms and tools for monitoring individual nodes in order to analyze the system and promptly detect anomalies. Developers also need effective mechanisms for analyzing, diagnosing issues, and identifying bugs in protocol implementations. Logging, tracing, and collecting metrics are common observability techniques to allow monitoring and obtaining diagnostic information from the system; most of the explored code bases use these techniques. OpenTelemetry and Prometheus are popular open-source monitoring solutions, which are used in many of the explored code bases.

Setting up monitoring for a system, especially one involving GRPC communication, provides crucial visibility into its operations. In this guide, we walked through the steps to instrument both a GRPC server and client with Prometheus metrics, exposed those metrics via an HTTP endpoint, and visualized them using Grafana. The Docker-Compose setup simplified the deployment of both Prometheus and Grafana, ensuring a streamlined process.

Alerting and Notification: Select a tool with flexible alerting mechanisms to proactively detect anomalies or deviations from defined thresholds. Consider asking questions like "Does this tool offer customizable alerting options and support notification channels that suit our team's communication preferences?" A tool like Prometheus provides robust alerting capabilities.

Prometheus

Prometheus is an open-source monitoring and alerting toolkit. It is designed to monitor highly dynamic containerized systems, making it an excellent choice for monitoring Docker containers and Kubernetes clusters.

wget https://github.com/prometheus/prometheus/releases/download/v2.46.0/prometheus-2.46.0.linux-amd64.tar.gz

Prometheus: Open-source monitoring system. Often used together with Grafana.