I've written my first Kubernetes on Apache Spark blog post in 2018 with a try to answer the question, what Kubernetes can bring to Apache Spark? Four years later this resource manager is a mature Spark component, but a new question has arisen in my head. Should I stay on YARN or switch to Kubernetes?
I had the idea for this blog post when I was preparing the "What's new in Apache Spark..." series. At that time, I was writing about Kubernetes in the context of Apache Spark but needed to "google" a lot of things aside - mostly the Kubernetes API terms.
The idea of writing this blog post came to me when I was analyzing Kubernetes changes in Apache Spark 3.1.1. Starting from this version we can use stage level scheduling, so far available only for YARN. Even though it's probably a very low level feature, it intrigued me enough to write a few words here!
After several months spent as an "experimental" feature in Apache Spark, Kubernetes was officially promoted to a Generally Available scheduler in the 3.1 release! In this blog post, we'll discover the last changes made before this promotion.
Containers are with us, data engineers, for several years. The concept was already introduced on YARN but the technology that really made them popular was Docker. In this post I will focus on its recommended practices to make our Apache Spark images better.
When I discovered microk8s I was delighted! An easy installation in very few steps and you can start to play with Kubernetes locally (tried on Ubuntu 16). However, running Apache Spark 2.4.4 on top of microk8s is not an easy piece of cake. In this post I will show you 4 different problems you may encounter, and propose possible solutions.
Initialization is a very first step of almost all applications. Unsurprisingly it's also the case of Kubernetes that uses Init Containers to execute some setup operations before launching the pods.
Beginning with new tool and its CLI is never easy. Having a list of useful debugging commands is always helpful. And the rule is not different for Spark on Kubernetes project.
Last years are the symbol of popularization of Kubernetes. Thanks to its replication and scalability properties it's more and more often used in distributed architectures. Apache Spark, through a special group of work, integrates Kubernetes steadily. In current (2.3.1) version this new method to schedule jobs is integrated in the project as experimental feature.
Commercial version of Apache Spark distributed by Databricks offers a serverless and auto-scalable approach for the applications written in this framework. Among the time some other companies tried to provide similar alternatives, going even to put Apache Spark pipelines into AWS Lambda functions. But with the version 2.3.0 another alternative appears as a solution for scalability and elasticity overhead - Kubernetes.
