zfs-localpv VS prometheus

I use it in Kubernetes via https://github.com/openebs/zfs-localpv

The PersistentVolume API is a nice way to divvy up a shared resource across different teams, and using ZFS for that gives us the snapshotting, deduplication, and compression for free. For our workloads, it benchmarked faster than XFS so it was a no-brainer.

Last few months I experimented more and more with all OpenEBS solutions that fit small Kubernetes cluster, using MicroK8S and Hetzner Cloud for a real experience.

I would investigate https://openebs.io/ https://portworx.com/ https://longhorn.io/ if you are forced to you can mount ISCSI on the kublet and feed it to one of those solutions. Keep in mind most of the big guys buy some sort of managed solution that you can point a CSI like trident https://netapp-trident.readthedocs.io

What are some cool projects to self hosted on a home Raspberry Pi (64 bit) Kubernetes cluster (Helm charts). arm64 support is a must. A lot of projects only build amd64 Docker containers which don't run on my cluster.

I currently run:

   - obenebs (provides abstraction for using local k8s worker disks as PVC mounts when running on-prem) -- https://openebs.io/

What do you use to provision Kubernetes persistent volumes on bare metal? I’m looking at open-ebs (https://openebs.io/).

Also, when you bump the image tag in a git commit for a given helm chart, how does that get deployed? Is it automatic, or do you manually run helm upgrade commands?

ListItem( 'Kubernetes', 'https://kubernetes.io/', 'Container Engines and Orchestration', """Kubernetes is an open-source container-orchestration system for automating computer application deployment, scaling, and management.""" ), ListItem( 'Podman', 'https://podman.io/', 'Container Engines and Orchestration', """Podman is a daemonless, open source, Linux native tool designed to make it easy to find, run, build, share and deploy applications using Open Containers Initiative (OCI) Containers and Container Images.""" ), # Data Storage :: Block Storage ListItem( 'Amazon EBS', 'https://aws.amazon.com/ebs/', 'Data Storage :: Block Storage', """Amazon Elastic Block Store (Amazon EBS) is an easy-to-use, scalable, high-performance block-storage service designed for Amazon Elastic Compute Cloud (Amazon EC2).""" ), ListItem( 'OpenEBS', 'https://openebs.io/', 'Data Storage :: Block Storage', """OpenESB is a Java-based open-source enterprise service bus. It allows you to integrate legacy systems, external and internal partners and new development in your Business Process.""" ), # Data Storage :: Cluster Storage ListItem( 'Ceph', 'https://ceph.io/en/', 'Data Storage :: Cluster Storage', """Ceph is an open-source software storage platform, implements object storage on a single distributed computer cluster, and provides 3-in-1 interfaces for object-, block- and file-level storage.""" ), ListItem( 'Hadoop Distributed File System', 'https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html', 'Data Storage :: Cluster Storage', """The Hadoop Distributed File System ( HDFS ) is a distributed file system designed to run on commodity hardware.""" ), # Data Storage :: Object Storage ListItem( 'Amazon S3', 'https://aws.amazon.com/s3/', 'Data Storage :: Object Storage', """Amazon S3 or Amazon Simple Storage Service is a service offered by Amazon Web Services that provides scalable object storage through a web service interface.""" )

Ideas from my kubernetes experience: * Cert-Manager is very popular and almost a must-have if you terminate SSL inside the cluster * Backups using velero * A dashboard/UI is actually very helpful to quickly browse resources, client tools like k9s are fine too * Secret: Management: Bitnami Sealed Secrets is the second big project in that space * I would add Loki to aggregate Logs * Never heard of ory. Usually I see (dex)[https://dexidp.io/] or keycloak used for Authentication * I like to run OpenEBS as in-cluster storage. * Istio isn't compatible with the upcomming ServiceMeshInterface (i think), so the trend seem to go toward Linkerd * Some Operator to deploy your favorite Database, is also a nice learning exercise.

OpenEBS ZFS LocalPV (declared stable), used widely for production workloads that need direct and resilient storage has added new capabilities like:

So I'm in the process of converting every node in my cluster to ZFS (RAID1 - a single mirror zpool) (powered by OpenEBS's ZFS LocalPV for the k8s heads out there). I'm going to try running Ceph w/ bluestore but doing something that would normally make absolutely no sense -- disabling the checksumming features of Ceph.

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.