tilt-extensions VS prometheus

The first is the load function which loads Tilt extensions. It's a way to expand the tool's features, and several are available. Here we are using docker_build_with_restart, which will update the container running inside our Kubernetes cluster.

This is the central viewport into manual resource control and environment enhancement through the open-source extensions for Tilt.

argocd for cd Tilt

Recently, I found Tilt [0] to be a good partner of mine to run "all services locally". It can be compared to "webpack (live-reloading, a lot of configuration possibilities) for backend". You want to run a bunch of services directly? Use local_resource()/local(). You have Procfile? There is procfile() function. You have docker-compose.yml with databases? You can run it too with docker_compose(). You want have Tiltfile and include them all-together? There is load(). You need some web-ui for frontend devs and a nice log browser? It is there too. You need to do some extra steps before running a service? You want to update your local cluster with newly built image on file save? No problem, tilt will do that with k8s_yaml() function. Tilt uses Titlfiles for configuration, which are written Pythonish Starlark language and you use them to run any specific logic there.

Also, I am not very lucky in having resemble 1:1 k8s cluster locally. You could be close but as long as you don't run already in cloud you will have different configuration (additional annotations, various quirks that do not exist in kind/k3s but they are on GCP). However, making dedicated dev environments in the cloud might be very costly and incur a lot of additional tinkering.

[0]: https://tilt.dev/

If `host` means your production/staging hosts or whatever, you should get out of that habit now. Look into something like tilt.dev or telepresence.io or any number of other solutions that help solve this issue. Doing it directly on any host is just a recipe for bad habits and disaster.

The article doesn't mention k3d (https://k3d.io/) which is a variant of k3s that runs in docker (rather than a VM) - very nice for k8s dev/test on developer workstations.

It integrates very nicely with https://tilt.dev/ also (another very useful tool for k8s related dev/test).

If this is an issue during development then you can use something like tilt with docker-compose to directly copy modified source inside container and incrementally build it. https://tilt.dev/

I'd add Tilt

Tilt

Along similar lines, how about comparing Devspace to [Tilt](https://tilt.dev/)?

It's like Prometheus, but for logs. Okay it's not really to do with the Norse or Greek gods, instead Loki is a horizontally-scalable, highly-available, multi-tenant log aggregation system inspired by the open source project Prometheus. Built by Grafana Labs, Loki is designed for ease of use. Instead of indexing the contents of the logs, Loki provides a set of labels for each log stream. The latest update includes query acceleration with Bloom filters, native OTel support, Helm charts, and more. Check out the changelog for all the major changes and deprecations.

WriteRequest::timeseries is a vector (https://github.com/prometheus/prometheus/blob/main/prompb/re...) and

Developers widely use Prometheus as a system for operational monitoring and alerting for their projects. Here is a list of tools for monitoring frontend services with Prometheus.

Just to give an example of the power of Go for CLI builds, you may have already used or at least heard of Docker, Kubernetes, Prometheus, Terraform, but what do they all have in common? They all have a large part of their usability via CLI and are developed in Go 🐿.

Distributed system administrators need mechanisms and tools for monitoring individual nodes in order to analyze the system and promptly detect anomalies. Developers also need effective mechanisms for analyzing, diagnosing issues, and identifying bugs in protocol implementations. Logging, tracing, and collecting metrics are common observability techniques to allow monitoring and obtaining diagnostic information from the system; most of the explored code bases use these techniques. OpenTelemetry and Prometheus are popular open-source monitoring solutions, which are used in many of the explored code bases.

Setting up monitoring for a system, especially one involving GRPC communication, provides crucial visibility into its operations. In this guide, we walked through the steps to instrument both a GRPC server and client with Prometheus metrics, exposed those metrics via an HTTP endpoint, and visualized them using Grafana. The Docker-Compose setup simplified the deployment of both Prometheus and Grafana, ensuring a streamlined process.

Alerting and Notification: Select a tool with flexible alerting mechanisms to proactively detect anomalies or deviations from defined thresholds. Consider asking questions like "Does this tool offer customizable alerting options and support notification channels that suit our team's communication preferences?" A tool like Prometheus provides robust alerting capabilities.

Prometheus

Prometheus is an open-source monitoring and alerting toolkit. It is designed to monitor highly dynamic containerized systems, making it an excellent choice for monitoring Docker containers and Kubernetes clusters.