traefik VS prometheus

Treafik as Reverse proxy

apparently "traffic" https://github.com/traefik/traefik/issues/795

Pronounced "traffic", Traefik is a modern HTTP reverse proxy and load balancer aimed at making deploying microservices easier. It integrates with your existing infrastructure components such as Docker, Kubernetes, and others, and configures itself automatically and dynamically. The latest version adds lots of new options and enhancements such as adding healthcheck options, support for custom headers, and more. Read the migration guide on how to update to the latest version which is now required due to breaking changes.

> I think etcd is basically a k8s only project now

I hate etcd with the best of them, but etcd is used in a lot more places than just kubernetes:

https://github.com/apache/apisix/blob/master/docs/en/latest/...

https://github.com/traefik/traefik#:~:text=Etcd,

https://github.com/zalando/patroni#patroni-a-template-for-po...

https://github.com/purpleidea/mgmt/tree/0.0.26/etcd (this one shows up on HN quite a bit)

https://github.com/sorintlab/stolon#features

It's actually one of the major reasons I wouldn't touch those projects

However, it's very unlikely that .NET developers will directly expose their Kestrel-based web apps to the internet. Typically, we use other popular web servers like Nginx, Traefik, and Caddy to act as a reverse-proxy in front of Kestrel for various reasons:

Not as good though. Case in point: https://github.com/traefik/traefik/issues/5472#issuecomment-... (that's just from this morning)

I'm speak objectively here. Of course, any built-in auto HTTPS that works (more or less) is better than none. Traefik uses an ACME library that was originally written for Caddy. After the original author left that project, Traefik team started maintaining it. Caddy's users' requirements exceeded what the library was capable of, but unfortunately there was friction in getting it to achieve our requirements. So I ended up writing a new ACME client library in Go and, together with upgrades in CertMagic (Caddy's auto-TLS lib), Caddy has the more flexible, robust, and capable auto-HTTPS functionality.

That is to say, not all auto-HTTPS functionalities are the same.

We'll use Traefik, an open source cloud native gateway that can plug into a Kubernetes cluster. It has the concept of "middleware" that can process API requests before passing them through to a backend. We can configuring a rate limit for all of our API endpoints by matching on the request path:

I did the same question here and here

Prometheus is the best-known time-series database engine. It has many use cases, but in the context of Kubernetes, it's a great way to store and query metrics that provide observability for your cluster and its workloads. You can receive alerts when metrics change, such as a Node CPU usage spike or a Pod failure, and integrate with tools like Grafana to visualize your values on dashboards.

Implement health checks and monitoring to ensure the availability and performance of your microservices. Use tools like Prometheus, Grafana, or NestJS built-in health checks.

Kubernetes Documentation: https://kubernetes.io/docs/home/ Kubernetes Tutorials: https://kubernetes.io/docs/tutorials/ Kubernetes Community: https://kubernetes.io/community/ Prometheus: https://prometheus.io/ Grafana: https://grafana.com/ Elasticsearch: https://www.elastic.co/elasticsearch/ Kibana: https://www.elastic.co/kibana Helm: https://helm.sh/ Prometheus Helm Chart: https://github.com/prometheus-community/helm-charts/tree/main/prometheus Grafana Helm Chart: https://github.com/grafana/helm-charts/tree/main/grafana Elasticsearch Helm Chart: https://github.com/elastic/helm-charts/tree/main/elasticsearch Kibana Helm Chart: https://github.com/elastic/helm-charts/tree/main/kibana RBAC: https://kubernetes.io/docs/reference/access-authn-authz/rbac/ Network Policies: https://kubernetes.io/docs/concepts/services-networking/network-policies/ StatefulSets: https://kubernetes.io/docs/concepts/workloads/controllers/statefulset/ DaemonSets: https://kubernetes.io/docs/concepts/workloads/controllers/daemonset/ Taints and Tolerations: https://kubernetes.io/docs/concepts/scheduling-eviction/taint-and-toleration/ Custom Resource Definitions (CRDs): https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/ Operators: https://kubernetes.io/docs/concepts/extend-kubernetes/operator/

Monitoring tools and performance profiling methods are invaluable in identifying performance bottlenecks. These tools provide real-time insights into API behavior, enabling developers to spot inefficiencies and potential issues. There's a range of monitoring tools, including platforms like New Relic, Datadog, and Prometheus that offer extensive performance metrics like response times, error rates, and resource usage. Using these tools, developers can get a comprehensive view of API performance and uncover hidden problems affecting responsiveness.

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.