watermill VS Grafana

I’ve successfully worked on projects using an asynchronous event-driven way of connecting services. I really like the decoupling of business logic and the events triggering it. I highly recommend https://github.com/ThreeDotsLabs/watermill to be more flexible when it comes to choosing the actual technology driving the async patter. It might be NATS today but requirements might change and you need to change. Watermill prepares you for this.

Highly recommend https://watermill.io lib for building async apps (using among others pub/sub patterns). It greatly decouples your code from the underlying technology and gives you the freedom to choose depending on the environment (e.g dev, prod). For dev or small scale purposes, it supports pub/sub purely based on go channels.

Look at this library: https://github.com/ThreeDotsLabs/watermill

Watermill is a good pubsub abstraction. You can start with native go channels and grow into a cloud pubsub or Kafka/Pulsar if you need to.

These conditions have led me to discover two packages watermill.io and go-kit. Both of these seem to do what I want and I am thinking of implementing the project using watermill and go Chanels and deploying to a server or some sort.

github.com/ThreeDotsLabs/watermill package provides a framework for building event-driven applications. It allows easy communication between independent components by decoupling the sender and the receiver.

My personal favorite tools: - https://github.com/go-kit/ for building services (although it's not necessary a great tool for prototyping) - https://github.com/gorilla/mux router (although it's been recently deprecated, so I'm looking for a similar, maintained library) - https://entgo.io/ ORM - https://watermill.io/ for messaging

Checkout https://watermill.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.