core VS Grafana

An alternative is metacall. The example in the readme is about calling Python from Javascript, but it also works with other languages, like Ruby, C#, Java, and other languages

https://github.com/metacall/core

List of supported languages here https://github.com/metacall/core/blob/develop/docs/README.md...

In the future, maybe webidl (or extensions of it) will bring interoperability between languages too. At the moment there is https://mozilla.github.io/uniffi-rs/ for interoperability between Rust and a number of languages (basically the ones mozilla needs: Swift, Kotlin, Javascript)

Hi, I am writing a Polyglot Runtime called MetaCall, it provides interoperability between many different languages: https://github.com/metacall/core

MetaCall is an extensible, embeddable and interoperable cross-platform polyglot runtime. It supports NodeJS, Vanilla JavaScript, TypeScript, Python, Ruby, C#, Java, WASM, Go, C, C++, Rust, D, Cobol.

I started looking for past selected organizations in February, found an organization named Metacall, which made polyglot programming easy. I made some contributions there. I looked into their past projects and tried to understand how the code base worked. The tech stack was mainly Python, C++, Rust, Nodejs, Docker. I knew very little about these.

MetaCall helps you build serverless applications using a more fine-grained, scalable and NoOps oriented Function Mesh instead of ServiceMesh and DevOps approach. It automagically converts your code into a Function Mesh and auto-scales individual hot parts or functions of your app.

We are building a Polyglot Runtime and we are adding support for Rust, if you are interested you can participate on it: https://github.com/metacall/core

MetaCall Polyglot Runtime MetaCall.io | Install | Docs

I try to avoid any complicated tool and simplify my life with NoOps tools. Using Kubernetes or AWS from scratch is probably going to kill your startup.

In my case, I have tried MetaCall: https://metacall.io

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.