github VS Grafana

I really like the go-github library, one can learn a lot about how good APIs are written and designed https://github.com/google/go-github.

what you described is basically what I did for the integration tests in the go-github library: https://github.com/google/go-github/tree/master/test

The tool itself is written in Go, leveraging a library called go-github to fetch gitignores. In the first iteration, it can either download a new gitignore file based on selected language and framework (for now it has to be one of the gitignore files present in the aformentioned github/gitignore repo) or replace existing gitignore by specifying new language and path of the ignore to be replaced.

I have a private Github repo where I store all the content for the computer science courses on Boot.dev in markdown files. It would be really convenient if user reported issues automatically manifested as Github issues on that repository! After just a few minutes of digging, I realized it was quite easy to do with the Github API. Given that my backend is a REST-ish API written in Go, I decided to use Google's Github API client package and the official Golang oauth2 package.

If you're building in Go, bradleyfalzon/ghinstallation is an implementation of http.RoundTripper which authenticates as a GitHub App, for example to use in combination with google/go-github.

Go client library for accessing the GitHub API v3

You may want to use the GitHub library as they have already done the work of wrapping the api. "GitHub - google/go-github: Go library for accessing the GitHub API" https://github.com/google/go-github

Use the google/go-github client (Github REST API client) to check GitHub releases for the latest release version

The exporter is written in Go and utilizes a GitHub client library to execute searches using the GitHub search API. The exporter is basically a CLI that takes in configuration options via arguments then runs a web server in a loop that looks for new pull request data periodically.

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.