conduit VS prometheus

Leverage a service mesh like Istio or Linkerd to manage communication between microservices within the Kubernetes cluster. These service meshes can be configured to intercept JMX traffic and enforce access control policies. Benefits:

Looks like CNCF waved them through Graduation anyway, let's look at policies from July 28, 2021 when they were deemed "Graduated"

All maintainers of the LinkerD project had @boyant.io email addresses. [0] They do list 4 other members of a "Steering Committee", but LinkerD's GOVERNANCE.md gives all of the power to maintainers: [1]

> Ideally, all project decisions are resolved by maintainer consensus. If this is not possible, maintainers may call a vote. The voting process is a simple majority in which each maintainer receives one vote.

And CNCF Graduation policy says a project must "Have committers from at least two organizations" [2]. So it appears that the CNCF accepted the "Steering Committee" as an acceptable 2nd committer, even though the Governance policy still gave the maintainers all of the power.

I would like to know if the Steering Committee voted to remove stable releases from an un-biased position acting in the best interest of the project, or if they were simply ignored or not even advised on the decision.

I'm all for Boyant doing what they need to do to make money and survive as a Company. But at that point my opinion is that they should withdraw the project from the CNCF and stop pretending like the foundation has any influence on the project's governance.

[0] https://github.com/linkerd/linkerd2/blob/489ca1e3189b6a5289d...

From here, we can explore other developments and tutorials on Kubernetes, such as o11y or observability (PLG, ELK, ELF, TICK, Jaeger, Pyroscope), service mesh (Linkerd, Istio, NSM, Consul Connect, Cillium), and progressive delivery (ArgoCD, FluxCD, Spinnaker).

https://linkerd.io/ is a much lighter-weight alternative but you do still get some of the fancy things like mtls without needing any manual configuration. Install it, label your namespaces, and let it do it's thing!

Open source API Gateway (Apache APISIX and Traefik), Service Mesh (Istio and Linkerd) solutions are capable of doing traffic splitting and implementing functionalities like Canary Release and Blue-Green deployment. With canary testing, you can make a critical examination of a new release of an API by selecting only a small portion of your user base. We will cover the canary release next section.

I have experimented with other service meshes and I was able to get up to speed quickly: Linkerd = 1 day, Istio = 3 days, NGINX Service Mesh = 5 days, but Consul Connect service mesh took at least 11 days to get off the ground. This is by far the most complex solution available.

https://github.com/linkerd/linkerd2 (random example)

It’s not an operator but a major component of the Linkerd control plane is written in Rust with kube-rs. https://github.com/linkerd/linkerd2/tree/main/policy-controller

Out of the number of service mesh solutions that exist, the most popular open source ones are: Linkerd, Istio, and Consul. Here at Koyeb, we are using Kuma.

FlatBuffers does the trick as a better replacement to ProtoBuf when running in some resource-critical devices. We were working on a project that leverage Arrow IPC (internally FlatBuffers) and shared memory to collect metrics on edge devices with limit CPU and memory, hopefully we can open source it soon.

But in this case the format is defined by [Prometheus](https://github.com/prometheus/prometheus/blob/main/prompb/re...) and we just adopt that.

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

After that, you will set up a metrics server container. It will use Prometheus.io, an open-source monitoring and alerting toolkit designed to collect, store, and query time series data, making it a tool for monitoring your systems' performance and health through metrics.