service VS Grafana

Ultimate Service was useful for me. None of the "backend" concepts were new, but you get to see how Bill would layout/design an API-based service. If you're experienced you'll notice the opinionated choices he makes, and I found myself saying "Nah, I'm not sure I'd do it like that". I appreciated its use of Kubernetes and KIND as I'd never played with them before. How he uses Docker to spin up a DB instance for tests is pretty cool. There's a lot of copy & paste as you code along with him (you copy from the "finished project" and paste into your work in progress). The full example project is online at https://github.com/ardanlabs/service. You won't write all that code, and this version is newer than the one I did, but it gives you an idea of what you might learn.

So what can you do insted? For testing databases, setup a docker instance for tests (e.g. like in https://github.com/ardanlabs/service), or start an embedded-postgres daemon (see https://github.com/fergusstrange/embedded-postgres). For communication with external APIs, just pass the http.Client (either in context.Context or as a field on the struct). Then in tests, you can override the http.Client.Transport func.

Take a look at https://github.com/ardanlabs/service from Bill Kennedy. You can probably simplify the structure a bit since your project is minimal, but that repo is gold.

Checkout https://github.com/ardanlabs/service for inporation. Tip: try to avoid creating a service package with all services, a domain package with all domain structs, etc.

Or go through this repo https://github.com/ardanlabs/service

I've seen this handler modification and wrapping pattern in Ardan Labs' service repository. https://github.com/ardanlabs/service/tree/master/foundation/web

https://github.com/ardanlabs/service This is something which I really like and has taken into account a lot of engineering decisions.

Monitoring application logs is a crucial aspect of the software development and deployment lifecycle. In this post, we'll delve into the process of observing logs generated by Docker container applications operating within HashiCorp Nomad. With the aid of Grafana, Vector, and Loki, we'll explore effective strategies for log analysis and visualization, enhancing visibility and troubleshooting capabilities within your Nomad environment.

To help us visualize these scenarios, we'll build a Grafana Dashboard so we can follow along.

Visualization and Analysis: Choose a tool with intuitive and customizable dashboards, charts, and visualizations. A question to ask is, "Are the visualization features of this tool user-friendly and adaptable to our team's specific needs?" Tools like Grafana and Kibana provide powerful visualization capabilities.

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

Grafana

Yes I'm aware that Grafana is open source but the method I used to find the API endpoints is far quicker than digging through hundreds of files in a codebase I'm not familiar with.

So, you will add one last container to allow us to visualize this data: Grafana, an open-source analytics and visualization platform that allows us to see traces and metrics simply. You can set Grafana to read data from both Tempo and Prometheus by setting them as datastores with the following grafana.datasource.yaml config file:

In example above, we use 2 additional parameters: code (HTTP response code) and page (page identifier), which provide detailed statistics. For example, you can build such graphs in Grafana:

Robin switched to the Grafana dashboard tab, and sure enough, the 5xx volume on web service was rising. It had not hit the critical alert thresholds yet, but customers had already started noticing.