Gin VS scrutiny

Now let’s define the functions that will be called whenever a request hits our API. All the functions will be referencing the context provided by the Gin web framework. Paste the following code below the sample slice we just added to api.go:

We will be using Gorilla Mux. As per their last update, they have a new group of maintainers, and their repos have shown activity to confirm that. The tutorial can be easily replicated in any other framework or library as well. So, while we will be using Gorilla Mux, you can try to replicate it in Gin or Fiber as well.

Now, let's dive into the fun part – creating our basic ToDo application using the powerful Gin framework. This section will walk you through the steps, breaking down the code into manageable snippets.

Thanks to the AWS Lambda Web Adapter, the application built as a (good old) REST/HTTP API using a familiar library (in this case, Gin.

Excellent Performance: Sponge is built on the gin framework, providing outstanding performance for web service development.

In this blog, we will delve into the fascinating realm of handling images in a Golang application, leveraging the power of the Gin framework for RESTful API development, MongoDB as a robust NoSQL database, and the mongo-driver library for seamless interaction with MongoDB. To store images efficiently, we'll explore the intricacies of GridFS, a specification within MongoDB for storing large files as separate chunks.

It uses Gin as the HTTP framework and PostgreSQL as the database with pgx as the driver and Squirrel as the query builder. It also utilizes Redis as the caching layer with go-redis as the client.

I have created an application with Go in Gin-Gonic. In my frontend (Nuxt3/TypeScript) I always get a CORS error:

We use Gin web framework https://gin-gonic.com for the routing, Gin provides a balance between performance, ease of use and extensibility making it a preferred choice for building and running web applications in Go.

I've noticed a quite nice feature in Scrutiny, where older disk temp data is lower resolution than recent data. In queries it would probably look like that:

I cannot look into the scrutiny script. The website recommends "something like"

I would always run the fantastic Scrutiny and set up notifications, so at the very least if some stat changes for the worst you might get a bit of early warning.

Hi. I am trying to understand whether I should trust TrueNAS Scale smartctl tool or Scrutiny (https://github.com/AnalogJ/scrutiny) for S.M.A.R.T results.

Quick Google search later to answer my own question, scrutiny seems to be it. That with LibreNMS sounds like a pretty healthy combo.

https://github.com/AnalogJ/scrutiny should do the trick

Its right there: https://github.com/AnalogJ/scrutiny/blob/master/docker/example.hubspoke.docker-compose.yml