We like to think that Google Cloud Platform is one of the best places to run high-performance, highly-available database deployments and MongoDB is no exception. In particular, with an array of standard and customizable machine types, blazing fast persistent disks, and a high-performance global network, Compute Engine is a great option for MongoDB deployments, which can then be combined with managed big data services like BigQuery, Cloud Dataproc, and Cloud Dataflow to support all manner of modern data workloads.
There are a number of ways to deploy MongoDB on Google Cloud Platform, including (but not limited to):
- Creating Compute Engine instances and manually installing/configuring MongoDB
- Using Cloud Launcher to quickly create and test drive a MongoDB replica set
- Provisioning Compute Engine instances and using MongoDB Cloud Manager to install, configure, and manage MongoDB deployments
Today we’re taking things one step further and introducing updated documentation and Cloud Deployment Manager templates to bootstrap MongoDB deployments using MongoDB Cloud Manager. Using the templates, you can quickly deploy multiple Compute Engine instances, each with an attached persistent SSD, that will download and install the MongoDB Cloud Manager agent on startup. Once the setup process is complete, you can head over to MongoDB Cloud Manager and deploy, upgrade, backup, monitor, and manage your cluster easily from a single interface.
By default, the Deployment Manager templates are set to launch three Compute Engine instances for a replica set, but they could just as easily be updated to launch more instances if you’re interested in deploying a sharded cluster.
Check out the documentation and sample templates to get started deploying MongoDB on Google Cloud Platform. Feedback is welcome and appreciated; comment here, submit a pull request, create an issue, or find me on Twitter @crcsmnky and let me know how I can help.
About the Author - Sandeep Parikh
Sandeep heads the Solutions Architecture, Americas East team for Google Cloud Platform. He develops solutions and architectural patterns around containers (Kubernetes, Container Engine) and data infrastructure (Pub/Sub, Dataflow, Spark, BigQuery, etc.).
Sandeep's formal background is in software engineering and he has worked for several companies over the last 15 years, including Apple and MongoDB, as well as several startups. Among others, he has developed reference architectures for complex Big Data deployments and analytical pipelines as well as software systems to analyze social networks, document similarity and text sentiment.