Docker And R

Mara Averick has some resources to help you get started with running R in a Docker container:

liftr 📦 by Nan Xiao

liftr aims to solve the problem of persistent reproducible reporting. To achieve this goal, it extends the R Markdown metadata format, and uses Docker to containerize and render R Markdown documents.

Click through for those resources as well as an addictive 8-bit animated GIF.

SQL On Docker And Running SQL Queries

Rob Sewell loads SQL on Windows & Linux via Docker and shows how to run a query against it using Powershell:

In only a few seconds you have a SQL 2017 instance up and running (Take a look at Andrews blog at dbafromthecold.com for a great container series with much greater detail)

Now that we have our container we need to connect to it. We need to gather the IPAddress. We can do this using docker command docker inspect but I like to make things a little more programmatical. This works for my Windows 10 machine for Windows SQL Containers. There are some errors with other machines it appears but there is an alternative below

Read the whole thing.

SQL Server Agent On A Linux Container

Andrew Pruski shows how to get the SQL Agent service running on a Linux container:

Now, one of the benefits of attending SQL Saturdays is that you get to pick the brains of a lot of very clever people and luckily for me, Jan Van Humbeek (blog|twitter) was there.

Jan said that he had gotten the SQL Agent running in Linux containers so I asked if he could send on his code and he very kindly obliged.

So, the disclaimer for this blog post is that I didn’t write the code here, Jan did. All I’ve done is drop it into a dockerfile so that an image can be built. Thank you very much Jan!

Click through for Jan’s code and Andrew’s presentation of the process.

Reasons For Using Docker With R

Jeroen Ooms gives us a few reasons why we might want to containerize our R-based products:

The flagship of the OpenCPU system is the OpenCPU server: a mature and powerful Linux stack for embedding R in systems and applications. Because OpenCPU is completely open source we can build and ship on DockerHub. A ready-to-go linux server with both OpenCPU and RStudio can be started using the following (use port 8004 or 80):

docker run -t -p 8004:8004 opencpu/rstudio

Now simply open http://localhost:8004/ocpu/ and http://localhost:8004/rstudio/ in your browser! Login via rstudio with user: opencpu (passwd: opencpu) to build or install apps. See the readme for more info.

This is in the context of one particular product, but the reasons fit other scenarios too.  H/T R-Bloggers

New Docker Tag For SQL Server 2017

Hamish Watson notes that there are new Docker tags for SQL Server 2017:

I have been using SQL Server 2017 running on Linux for a while now (blog post pending) and use the official images from:

https://hub.docker.com/r/microsoft/mssql-server-linux/

To get the latest I used to run

docker pull microsoft/mssql-server-linux:latest

However today I noticed that the :latest tag had been removed:

Click through to see the tag you probably want to use.

Limiting Docker Container Resources

Andrew Pruski shows how to cap the resources available to a container:

What I’ve done here is use the cpus and memory switches to limit that container to a maximum of 2 CPUs and 2GB of RAM. There are other options available, more info is available here.

Simple, eh? But it does show something interesting.

I’m running Docker on my Windows 10 machine, using Linux containers. The way this works is by spinning up a Hyper-V Linux VM to run the containers (you can read more about this here).

Read on to learn more.

Docker Stop Versus Docker Kill

Andrew Pruski explains why docker kill is so much faster than docker stop:

When running demos and experimenting with containers I always clear down my environment. It’s good practice to leave a clean environment once you’ve finished working.

To do this I blow all my containers away, usually by running the docker stop command.

But there’s a quicker way to stop containers, the docker kill command.

Sending SIGTERM isn’t particularly polite and doesn’t let processes clean up, which could leave your process in an undesirable state during future runs.  But if you’re just re-deploying a container, you don’t really care about the prior state of the now-disposed container.

Using The Kubernetes Dashboard

Andrew Pruski shows how to set up and use the Kubernetes dashboard inside Azure Container Services:

But not only can existing objects be viewed, new ones can be created.

In my last post I created a single pod running SQL Server, I want to move on from that as you’d generally never just deploy one pod. Instead you would create what’s called a deployment.

The dashboard makes it really simple to create deployments. Just click Deployments on the right-hand side menu and fill out the details:

Check it out; this looks like a good way of managing Kubernetes on the small, or getting an idea of what it can do.

SQL Server In Kubernetes

Andrew Pruski shows us how to spin up SQL Server containers within Kubernetes running on Azure Container Services:

Looks pretty good to me! SQL is up and has accepted the config value within our yaml file to change the SA password. But how are we going to connect to it?

What we need to do now is define a Kubernetes service. A service is a level of abstraction over pods to allow connections to the containers within the pods regardless of where the pod is located in the cluster. So let’s setup a service.

Andrew does a good job of taking us through the process step by step.

Custom SQL Server Docker Images

James Anderson shows an easy way of creating a custom Docker image which relies on some other image:

The FROM statement declares that we want to lay some instructions on top of the microsoft/mssql-server-windows image. The beauty of this approach is that when I pull down a new version of the microsoft/mssql-server-windows image, my image will be updated too. The microsoft/mssql-server-windows Dockerfile does the same thing with the microsoft/windowsservercore image.

The rest of the Dockerfile sets some meta data, downloads the installer and adds the Advanced Analytics feature.

SSIS, SSAS, SSRS or any other SQL Server feature could be added to a containerised SQL Server deployment in the same way.

With this approach, you do run the risk that upstream changes will break your image, but for something like this, it’s a very useful approach.

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