spectrum VS Grafana

For a good reference repository, you should check out Spectrum’s GitHub repo. It’s organized well, uses good practices, and given that it is the entire Spectrum product, can provide a lot of system design and architecture insight.

You have now started on your first React Native app in Expo. This is the same tool which is used for creating apps like Facebook, Instagram, Coinbase, shopify, Tesla, Uber Eats and many more. You can read more on Expo here: https://docs.expo.dev/ or check out an open source app here: https://github.com/withspectrum/spectrum and checkout an enterprise boilerplate here: https://github.com/infinitered/ignite

GraphCDN passes subscriptions through to your origin, so they keep working just as they did before! I personally used GraphQL subscriptions with relative success at Spectrum and I'm very excited about live queries nowadays.

We were using Slate at Spectrum[0] back in 2017/2018, eventually switched to DraftJS due to cross-browser issues but that was honestly equally frustrating to use and support across many browsers.

In hindsight, we should've just had a GitHub-style markdown editor: https://mxstbr.com/thoughts/tech-choice-regrets-at-spectrum

It sounds like the situation has improved since then! I'll definitely try Tiptap if I ever need to build another RTE.

[0]: https://github.com/withspectrum/spectrum

Checkout spectrum

I was lucky enough that I somehow stumbled upon withspectrum/spectrum repo and found out that there actually are great applications running on the internet, under load and are dutifully maintained while being open-source! Spectrum has been back then one of the most eye-opening experiences for me as a junior developer. Although I didn't actually ever got to build a system like that, it taught me a great deal on how such app operates and how can various libraries be used.

Special thanks to the Spectrum Project (Here) for laying the foundations to Bulletproof Express. Also, many thanks to Node.js Best Practices (Here) and Bulletproof React (Here) for providing guidance on how Enterprise-Level Software should be written.

During last few years i managed to stumble upon a few repositories with large apps that have been a great learning source and an inspiration (such as https://github.com/withspectrum/spectrum) but these are few and far apart.

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.