The Ultimate Kubernetes Homelab Guide: From Zero to Production Cluster On-Premises

Whether you’re looking for a more powerful development environment or a production-grade Kubernetes cluster for experiments, this guide provides end-to-end deployment and configuration instructions to get the cluster up and running. The first part of this guide covers the planning and provisioning of the infrastructure with Proxmox and Terraform. The second part is dedicated to installing Kubernetes and essential software such as Calico for networking, OpenEBS for volume provisioning, and MetalLB for network load balancing.

Kubeflow Training Operators and Istio: solving the proxy sidecar lifecycle problem for AI/ML workloads

With Kubeflow gaining traction in the community and its early adoption in enterprises, security and observability concerns become more and more important. Many organizations that are running AI/ML workloads, operate with sensitive personal or financial data and have stricter requirements for data encryption, traceability, and access control. Quite often, we can see the use of the Istio service mesh for solving these problems and gaining other benefits of the rich functionality it provides.

Spark JobServer: from Spark Standalone to Mesos, Marathon and Docker

After several years of running Spark JobServer workloads, the need for better availability and multi-tenancy emerged across several projects author was involved in. This blog post covers design decisions made to provide higher availability and fault tolerance of JobServer installations, multi-tenancy for Spark workloads, scalability and failure recovery automation, and software choices made in order to reach these goals. Spark JobServer Spark JobServer is widely used across a variety of reporting and aggregating systems.

Resource Allocation in Mesos: Dominant Resource Fairness

Apache Mesos provides a unique approach to cluster resource management called two-level scheduling: instead of storing information about available cluster resources in a centralized manner it operates with a notion of resource offers which slave nodes advertise to running frameworks via Mesos master, thus keeping the whole system architecture concise and scalable. Master’s allocation module is responsible for making the decisions about which application should receive the next resource offer and it relies on Dominant Resource Fairness(DRF) algorithm for making these decisions.

Apache Spark: core concepts, architecture and internals

This post covers core concepts of Apache Spark such as RDD, DAG, execution workflow, forming stages of tasks, and shuffle implementation and also describes the architecture and main components of Spark Driver. There’s a github.com/datastrophic/spark-workshop project created alongside this post which contains Spark Applications examples and dockerized Hadoop environment to play with. Slides are also available at slideshare. Intro Spark is a generalized framework for distributed data processing providing functional API for manipulating data at scale, in-memory data caching, and reuse across computations.