Scale Quickly with DataStax Enterprise on Google Container Engine

Datetime:2016-08-23 00:39:38          Topic:          Share

Containers are very effective for managing stateless applications.  In a containerized environment, web tiers can be scaled quickly and failed containers can be quickly replaced. Tools like Kubernetes automate that management.   Google Container Engine (GKE) further simplifies the process by providing a great integration between Kubernetes and a top tier cloud provider like Google Cloud Platform (GCP) .  Simplified management means less time spent managing machines, resulting in a lower total cost of ownership (TCO) for containerized environments than traditional virtualized environments.

Managing stateful workloads is more complex.  In a stateful system, replacing failed nodes with new nodes will result in an unacceptable loss of data.  One example of a stateful workload is a database.  In particular, this blog post focuses on DataStax Enterprise (DSE) , a highly scalable distributed database built on Apache Cassandra ™.

Rather than separating out the web tier and the database tier, integration with GKE allows a user to deploy their entire application, including the data store in GKE and then manage its full lifecycle using Kubernetes.  This simplifies administration, improves reliability and provides a more holistic framework for building applications than otherwise available.  All this leads to a lower cost of ownership and improved time to market.

Deploying a DSE cluster

DataStax and Google have been working closely to build an integration between DSE and GKE.  This integration is available on GitHub at: .

Deploying a cluster is really simple.  Details instructions are given here .  At a slightly higher level, all you need to do is run the script.

Once a cluster is deployed, you can login to DataStax OpsCenter and view the DSE nodes running:

Wrapping Up…

The integration described here is a fast way to get started using DataStax Enterprise on Google Container Engine.  At this point, the integration is more demo grade than production.  But, we’re actively working on improving it!

We welcome your thoughts and contributions for this project.  We’re tracking issues on GitHub here: .

Additionally, feel free to reach out to or on Twitter @benofben.