chaos-mesh VS prometheus

Ive been messing around with chaos mesh recently (https://chaos-mesh.org/) and im wondering: is there any way i can define custom behaviour in one of my experiments? Specifically, I want to deploy a Pod with a certain image using an experiment.

Litmus, Gremlin, Chaos Mesh, and Chaos Monkey are all popular open-source tools used for chaos engineering. As we will be using AWS cloud infrastructure, we will also explore AWS Fault Injection Simulator (FIS). While they share the same goals of testing and improving the resilience of a system, there are some differences between them. Here are some comparisons:

https://chaos-mesh.org/ (open source)

Implement Chaos Mesh and Litmus chaos engineering framework to understand the behavior and stability of application in real-world use cases.

Here is our recent experience with Chaos Mesh for performing basic chaos engineering experiments on an application in Kubernetes.

In March 2018, an article titled Service Mesh is the broad trend, what about Database Mesh?, was pubslished on InfoQ China and went viral in the technical community. In this article, Zhang Liang, the founder of Apache ShardingSphere, described Database Mesh concept along with the idea of Service Mesh. Four years later, the Database Mesh concept has been integrated by several companies together with their own tools and ecosystems. Today, in addition to Service Mesh, a variety of “X Mesh” concepts such as ChaosMesh, EventMesh, IOMesh have emerged. Following four years of development, Database Mesh has also started a new chapter: Database Mesh 2.0.

🐒 Chaos Mesh will turn 2 on 2021.12.31! We're grateful for every contribution that helped this project grow, and we’d like to hear your Chaos Mesh story!

So i came across this recently, haven't used it myself but it seems to fit your requirements: https://github.com/chaos-mesh/chaos-mesh

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