Title: Deploying a MongoDB Collection Generator on Kubernetes

In a recent guide, developers walked through the process of creating a utility to generate 100 MongoDB collections, each populated with 1 million random documents, and deploying it on Kubernetes. The steps involved setting up a Kubernetes environment, defining a persistent volume for MongoDB data, and deploying the job in a dedicated namespace.

The process began by ensuring a Kubernetes cluster was available and configuring kubectl to connect to it. A namespace called "my-lab" was then created to keep the deployment isolated. A persistent volume (PV) was defined, followed by applying the PV and defining a persistent volume claim (PVC).

Next, a MongoDB deployment and service were defined, applied, and verified. The deployment was scaled down and back up to ensure data persisted. A Python script was written to create collections and populate them with random documents, which was then containerized using a Dockerfile.

The generated image was pushed to a container registry, and a job was defined in mongo-populator-job.yaml to run the collection generation script. The job was applied, and after completion, the data was examined in MongoDB. Each collection contained 1 million documents, confirming the success of the data generation job.

This comprehensive guide provides a clear outline for deploying a MongoDB collection generator on Kubernetes, demonstrating the versatility of this container orchestration platform.

Source: https://dev.to/dm8ry/deploying-a-mongodb-collection-generator-on-kubernetes-5688