Packer VS prometheus

The missing piece of puzzle is the AMI "golden image" that will be used to start the instances in autoscaling group. The AMI has to have NGINX and PHP installed with the list of required modules enabled. The great tool to brew one is hashicorp packer.

To manage a VM, you can use something as simple as just manual actions over SSH, or can use tools like Ansible, Hashicorp's Packer and Terraform or other automations. For an app where there is minimal load and security/reliability concern, VMs are still a great option that provide a lot of value for the buck

Server templating: Using Packer has never been easier to create reusable server configurations in a platform-independent and documented manner.

I'll give you hard, but rewarding and easy to modify(once you know what you're doing) way. Packer may be a thing you're looking for.

It is straightforward to build them with packer (I have built AMIs for 13.0 and 13.1, but 13.2 should be exactly the same). I've been meaning to write a blog post about it for a while, but have not gotten to it yet... In any case, what I am doing is using the EBS Surrogate Builder to start an instance running the official FreeBSD 13.2 image with an extra volume attached and run a script to create a zpool on the extra volume and bootstrap and configure FreeBSD 13.2-RELEASE on it. After that packer takes care of creating an AMI out of that extra volume, so you can use it... If you have any issues, let me know, and maybe I will finally get to writing that blog post...

HashiCorp Packer is a tool for creating machine images for a variety of platforms, including AWS, Azure, and VMware. It allows you to define machine images as code and supports a wide range of configuration options.

Packer is a tool that can be used to build machine images. Basically, it takes a base image, runs a series of steps to provision that image, and then burns a new image. In my workplace we use it heavily to build AWS AMIs. But it has an ARM plugin that looks to be very very suitable for building customised Raspberry Pi images (my quick read of the doco there says it can go ahead and write the final image to an SD card for you too).

Seems related to https://github.com/hashicorp/packer/issues/12118 But the workaround discribed in the comments doesn’t seems to work anymore

I'd suggest using a tool like Packer to build a gold image based on your base AMI and all your changes. Then you'll have your own AMI you can launch new instances with.

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.

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