A template repository for creating your own distributed GoMR jobs can be found here.
In the original GoMR talk, I presented my implementation of a local mapreduce framework for Golang. In this talk, I give the same overview of mapreduce and the implementation, and show how I scale the system using Kubernetes.
Frustrated trying to run/debug MapReduce applications on my laptop, I decided to spin my own MR framework to solve the problems of efficiency, configuration, and error messages. Go is a compiled language and has inherent support for distribution in the form of channels. I took advantage of these two strengths to create GoMR.
In this talk, I give an overview of the framework, the programming style I used to create it, and an evaluation of the framework against a current state of the art system, Apache Spark.
I show how I leveraged Kubernetes, a container orchestration system, to scale GoMR to many machines. As much as possible, GoMR off-loads the work of a control plane onto Kubernetes.
I hope this work will encourage a move away from the cumbersome JVM, and spark a next-generation of efficient distributed compute frameworks.