rook
keda
rook | keda | |
---|---|---|
51 | 91 | |
11,949 | 7,791 | |
0.8% | 1.8% | |
9.9 | 9.5 | |
3 days ago | 2 days ago | |
Go | Go | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
rook
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Ceph: A Journey to 1 TiB/s
I have some experience with Ceph, both for work, and with homelab-y stuff.
First, bear in mind that Ceph is a distributed storage system - so the idea is that you will have multiple nodes.
For learning, you can definitely virtualise it all on a single box - but you'll have a better time with discrete physical machines.
Also, Ceph does prefer physical access to disks (similar to ZFS).
And you do need decent networking connectivity - I think that's the main thing people think of, when they think of high hardware requirements for Ceph. Ideally 10Gbe at the minimum - although more if you want higher performance - there can be a lot of network traffic, particularly with things like backfill. (25Gbps if you can find that gear cheap for homelab - 50Gbps is a technological dead-end. 100Gbps works well).
But honestly, for a homelab, a cheap mini PC or NUC with 10Gbe will work fine, and you should get acceptable performance, and it'll be good for learning.
You can install Ceph directly on bare-metal, or if you want to do the homelab k8s route, you can use Rook (https://rook.io/).
Hope this helps, and good luck! Let me know if you have any other questions.
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Running stateful workloads on Kubernetes with Rook Ceph
Another option is to leverage a Kubernetes-native distributed storage solution such as Rook Ceph as the storage backend for stateful components running on Kubernetes. This has the benefit of simplifying application configuration while addressing business requirements for data backup and recovery such as the ability to take volume snapshots at a regular interval and perform application-level data recovery in case of a disaster.
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People who run Nextcloud in Docker: Where do you store your data/files? In a Docker volume, or on a remote server/NAS?
This is beyond your question but might help someone else: I switch from docker-compose to kubernetes for my home lab a while ago. The storage solution I've settled on is Rook. It was a bit of up-front work learning how to get it up but now that it's done my storage is automatically managed by Ceph. I can swap out drives and Ceph basically takes care of everything itself.
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Rook/Ceph with VM nodes on research cluster?
The stumbling point I am at is I want to use rook.io(Ceph) as my storage solution for the cluster. The Ceph prerequisites are one of the following:
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Asking for recommendation on remote Kubernetes storage for a small cluster and databases
Have you looked at Rook?
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Want advice on planned evolution: k3os/Longhorn --> Talos/Ceph, plus Consul and Vault
I've briefly run ceph in an external mode, you can actually use a rook deployment to manage it (sort of). Here is the documentation for doing that. For me it didn't pass my testing phase because I need better networking equipment before I can try that.
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ATARI is still alive: Atari Partition of Fear
This article explains the data corruption issue happened in Rook in 2021. The root cause lies in an unexpected place and can also occurs in all Ceph environment. It's interesting that Rook had started to encounter this problem recently even though this problem has existed for a long time. It's due to a series of coincidences. I wrote this article because the word "Atari" used in a non-historical context in 2021.
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How to Deploy and Scale Strapi on a Kubernetes Cluster 2/2
Rook (this is a nice article for Rook NFS)
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Running on-premise k8s with a small team: possible or potential nightmare?
Storage: Favor any distributed storage you know to start with for Persistent Volumes: Ceph maybe via rook.io, Longhorn if you go rancher etc
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My completely automated Homelab featuring Kubernetes
I've dealt with a lot of issues that are very close to just unplugging a node. Unfortunately on node lost, my stateful workloads using rook-ceph block storage won't migrate over to another node automatically due to an issue with rook. Stateless apps (ingress nginx, etc..) not using rook-ceph block failover to another node just fine. I've kind of accepted this for now and I know Longhorn has a feature that makes this work but I find rook-ceph to be more stable for my workloads.
keda
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Ask HN: What's the right way to scale K8s for GPU workloads?
It seems you want something like KEDA (https://keda.sh)
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Tortoise: Shell-Shockingly-Good Kubernetes Autoscaling
Most just utilize out of the box macro resources available in HPA.
For more advanced use cases there is keda - https://keda.sh/
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Root Cause Chronicles: Quivering Queue
Thankfully KEDA operator was already part of the cluster, and all Robin had to do was create a ScaledObject manifest targeting the Dispatch ScaleUp event, based on the rabbitmq_global_messages_received_total metric from Prometheus.
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Five tools to add to your K8s cluster
Keda
- K8s latencies in chained services - Using RL?
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Best Kubernetes DevOps Tools: A Comprehensive Guide
KEDA introduces event-driven scaling to Kubernetes workloads. It integrates with Kubernetes Horizontal Pod Autoscalers and can scale pods based on external metrics from services like databases and message queues (Kafka, RabbitMQ, MongoDB).
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Auto-scaling DynamoDB Streams applications on Kubernetes
# update version 2.8.2 if required kubectl apply -f https://github.com/kedacore/keda/releases/download/v2.8.2/keda-2.8.2.yaml
- KEDA
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What is the difference in production for scale to zero usecases - Keda vs Lambda ?
This is traditionally a AWS Lambda usecase - or an OpenFaas kind of usecase. But very recently i discovered https://keda.sh/ and it seems it is specifically meant for this in a kubernetes environment.
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Ingesting Data into OpenSearch using Apache Kafka and Go
If you deploy the application to Amazon EKS, you can also consider using KEDA to auto-scale your consumer application based on the number of messages in the MSK topic.
What are some alternatives?
longhorn - Cloud-Native distributed storage built on and for Kubernetes
k8s-prometheus-adapter - An implementation of the custom.metrics.k8s.io API using Prometheus
ceph-csi - CSI driver for Ceph
argo - Workflow Engine for Kubernetes
velero - Backup and migrate Kubernetes applications and their persistent volumes
istio - Connect, secure, control, and observe services.
Nginx Proxy Manager - Docker container for managing Nginx proxy hosts with a simple, powerful interface
karpenter-provider-aws - Karpenter is a Kubernetes Node Autoscaler built for flexibility, performance, and simplicity.
Ceph - Ceph is a distributed object, block, and file storage platform
helm - The Kubernetes Package Manager
hub-feedback - Feedback and bug reports for the Docker Hub
http-add-on - Add-on for KEDA to scale HTTP workloads