garden VS prometheus

Hey there! Have you tried garden.io for caching? We also cache tests. Pretty much anything that's possible to cache. We're open source at https://github.com/garden-io/garden

To add to what's already been said: If you think about it, CI pipelines are typically a complete description of how your system is built, tested, and deployed.

Which is pretty fantastic except for how walled off they are. You can't really re-use these descriptions for e.g. development, they're not vendor agnostic, and they only way to run them is by pushing your code.

Maybe it's a silly analogy but it's almost like being a web dev that doesn't have a browser and needs to send their code to a friend who can tell them if that font size looks good.

I think we're way over due for freeing these "blueprints" of our system from the confines of CI and making them portable and flexible. And containers are the technology that's enabling that.

Full disclaimer (as always): I work at Garden[0] where we're also solving that problem but taking a slightly different approach to Dagger (it's still a DAG). Garden config is declarative and the jobs (we call them actions) have a semantic meaning. You can e.g. have a Build action of type container or a Deploy action of type Helm and Garden will figure out what to do with it.

[0] https://github.com/garden-io/garden

Yes, there's us over at https://github.com/garden-io/garden! We're big believers in pipelines that run anywhere. I even made a short little video that should give you the gist. [1]

Some of the short-list of differences: we use YAML for our configuration language, Dagger can use full-fat languages to define its pipelines. Our feature scope is broader: you can use us to vend IDP-like stacks to your developers if you're a Platform Team; we make development with remote Kubernetes clusters very easy, including all the remote image builds; and we have a number of integrations so you can bring your IaC tool of choice (Pulumi, Terraform) into your pipeline and set up service -> infra dependencies.

[1] https://www.youtube.com/watch?v=JFnan6s2cDg

Me too. In fact Garden (dev tooling for the Kubernetes)[0] is a Berlin start-up with three Icelandic founders.

And if I'm not mistaken, two of us worked briefly with @halldorel (above commenter) at an earlier Icelandic start-up. It's a small world (if you're Icelandic).

[0] https://garden.io

There are dedicated tools just for that. Apart from skaffold check also tilt.dev, garden.io, devspace.sh, okteto.com

Would appreciate any insights on garden.io. Thanks.

Telepresence, tilt, garden.io, okteto, skaffold etc.

- https://garden.io/

haha, do my pictures give off a strong not-web-dev vibe? Either way your right, I'm focusing on devxp and automation for kubernetes. Because my work is open source you can see it here https://github.com/garden-io/garden (btw we're also hiring another open core dev like me)

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.

wget https://github.com/prometheus/prometheus/releases/download/v2.46.0/prometheus-2.46.0.linux-amd64.tar.gz

Prometheus: Open-source monitoring system. Often used together with Grafana.