I favour Cloud Run over Kubernetes clusters, since its simpler to deploy and maintain apps, and you don’t need to pay at least $100 a month for a Kubernetes cluster.įor R APIs such as plumber, Cloud Run is my recommended solution, since it can scale so well and the cost is good.
Cloud Functions only works with the supported languages, such as Python. Its like Google Cloud Functions or AWS Lambda functions, but unlike those Cloud Run works with any language since its using a Docker container that can carry any language that supports HTTP. One of its most attractive features is the scaling, as you pay zero when your app has no visits, but as demand increases it can flexibly serve up your app to billions of users. Why Shiny on Cloud Run?Īs mentioned in my R at scale on Google Cloud Platform post, Cloud Run is a container-as-a-service which lets you deploy Docker containers to the web without needing to worry about the infrastructure.
It will only download files to your folder that don’t exist, so local changes won’t be overwritten if they already exist.There are some references on how to deploy Shiny apps to Cloud Run around the web and in various bits of my package documentation, but its a cool service so I thought it worth pulling out and having a blog post to refer to. Once done, when you quit the R session it will save your work to its own new folder, that when you stop/start a Docker container with RStudio within and create a project with the same name, will automatically load. Thus, you can save an RStudio project via your local computer, then launch an RStudio server in the cloud with the loaddir: argument set to that directory name to load the files onto your cloud server. yaml tells googleCloudStorageR which bucket to save the folder to, or if not present an environment argument GCS_SESSION_BUCKET - this is used on first load when no. Rprofile file that will save the projects workspace data to its own bucket, if they have a _gcssave.yaml file in the folder, or if the directory matches one already saved. This Dockerbuild puts the functions into a custom.
The functions can store data to Google’s dedicated store via googleCloudStorageRs gcs_first and gcs_last functions. This build includes the newest version of googleCloudStorageR and googleComputeEngineR which have had functions added to help with the workflow above.
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