Category: Containers

Deploying SQL Server 2019 Big Data Clusters With Kubernetes

Published 2018-12-19 by Kevin Feasel

Chris Adkin has the start of a new series:

Minikube is a good learning tool and Microsoft provides instructions for deploying a big data cluster to this ‘Platform’. However, its single node nature and the fact that application pods run on the master node means that this does not reflect a cluster that anyone would run in production. Kubernetes-as-a-service is probably by far the easiest option for spinning a cluster up, however it relies on an Aws, Azure or Google Cloud Platform account, hence there is a $ cost associated with this. This leaves a vanilla deployment of Kubernetes on premises. Based on the assumption that most people will have access to Windows server version 2008 or above, a relatively cheap and way of deploying a Kubernetes cluster is via Linux virtual machines running on Hyper-V. This blog post will provide step by step instructions for creating the virtual machines to act as the master and worker nodes in the cluster.

This is going on my “try this out when I have time” list. So expect a full report sometime in the year 2023.

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Deploying An R Service To Azure Kubernetes Service

Published 2018-12-14 by Kevin Feasel

Hong Ooi shows us how we can use Azure Container Registry and Azure Kubernetes Service to deploy an R model via Plumber:

If you run this code, you should see a lot of output indicating that R is downloading, compiling and installing randomForest, and finally that the image is being pushed to Azure. (You will see this output even if your machine already has the randomForest package installed. This is because the package is being installed to the R session inside the container, which is distinct from the one running the code shown here.)
All docker calls in AzureContainers, like the one to build the image, return the actual docker commandline as the cmdline attribute of the (invisible) returned value. In this case, the commandline is docker build -t bos_rf . Similarly, the push() method actually involves two Docker calls, one to retag the image, and the second to do the actual pushing; the returned value in this case will be a 2-component list with the command lines being docker tag bos_rf deployreg.azurecr.io/bos_rf and docker push deployreg.azurecr.io/bos_rf.

I love this confluence of technologies and at the same time get a “descent into madness” feeling from the sheer number of worlds colliding.

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Using Replication With SQL Server In Containers

Published 2018-12-14 by Kevin Feasel

Andrew Pruski shows us how we can build up snapshot replication with SQL Server in containers:

Last week I saw a thread on twitter about how to get replication setup for SQL Server running in a container. Now I know very little about replication, it’s not an area of SQL that I’ve had a lot of exposure to but I’m always up for figuring stuff out (especially when it comes to SQL in containers).
So let’s run through how to set it up here.
First, create a dockerfile to build an image from the SQL Server 2019 CTP 2.2 image with the SQL Server Agent enabled: –

Now that Andrew is a replication expert…

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Attaching Databases Via Dockerfile

Published 2018-12-12 by Kevin Feasel

Andrew Pruski shares a better technique for attaching database files held outside of a Docker container:

Now this works a treat. It waits ten seconds for the SQL instance to come up within the container and then runs the sqlcmd script below.
The problem with this is, it’s a bit of a hack. The HEALTHCHECK command isn’t designed to run once, it’ll carry on running every 10 seconds once the container comes up…not great.
So, what’s the better way of doing it?

Andrew gives us a clear explanation of what’s going on and gives a shout out to Bob Ward’s SQL Server on Linux book.

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Connecting Power BI To Dockerized SQL Server

Published 2018-12-06 by Kevin Feasel

Chris Taylor shows us how to build a SQL Server on Linux Docker container and use it to supply data to a Power BI dashboard:

I (and many others) have done a series of docker blog posts over the last couple of years but they’ve all tended to evolve around spinning up a SQL Server 2017+ container for testing or demo purposes. This is only really the start, think of the bigger picture here, once you have your database environment the world is your oyster.

This blog post will show how we can use SQL Server 2019 CTP2.1 running on Linux (Ubuntu) in a docker container as our data source for a Power BI environment in next to no time!

These steps show a very manual process for completing this setup, if it is something you are looking to do frequently then I suggest creating a Dockerfile and/or yml file and use docker-compose. This way you can have all your setup in one file and it will be a single statement to get your SQL Server 2019 environment up and running.

Read on for the demo.

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Running SQL Server 2019 In A Docker Container

Published 2018-12-03 by Kevin Feasel

Cathrine Wilhelmsen shows us how to set up a Docker container running SQL Server 2019 on Linux:

In this post, I share my approach and code snippets for:

Installing Docker
Getting SQL Server 2019
Running SQL Server 2019 in a Docker Container
Restoring Demo Databases (AdventureWorks and WideWorldImporters)

If your hardware supports Docker, this is a great way of getting some experience with a new version of SQL Server without the mess of cleaning up after a CTP or affecting your current dev environment.

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Running Windows On Kubernetes

Published 2018-11-26 by Kevin Feasel

Chris Adkin shows us how you can run the Windows OS on Kubernetes (with some limitations):

With the caveat that this information is correct at the time of writing, the following points should be noted:

The control plane and worker nodes should always be on the same release of Kubernetes.

The control plane can only run on Linux

The minimum version of Windows 2016 RTM is required for worker nodes, but version 1709 is preferred.

A full list of restriction can be found here.

But here is something particularly significant for anyone wishing to deploy highly available SQL Server infrastructures to Kubernetes via availability groups:

Read on for that particularly significant limitation; it’s a doozy.

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Deploying SQL Server To Minikube

Published 2018-11-20 by Kevin Feasel

Chris Adkin continues a series on Kubernetes with a look at Minikube:

Once the cluster is up, lets deploy a stand alone SQL Server instance to it, availability groups will be covered at a later date. To do this several different types of object will need to be created:

Storage objects; persistent volume claims, these will lead to the automatic creation of a persistent volumes.
A secret to hold the password for the sa account.
A deployment, this embodies the SQL Server instance.
A service, this provides the means of accessing the instance via an ip address and port from outside the cluster.

Click through for a demo.

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Deploying SQL Server Availability Groups Via Kubernetes

Published 2018-11-14 by Kevin Feasel

Anthony Nocentino continues his series on Kubernetes:

In this blog post, we’re going to work on deploying a SQL Server Availability Group in a Kubernetes Cluster in on-premises virtual machines. I’m going to walk you through the process as it’s documented by Microsoft at this link here. This document is very good but only shows you how to do it in Azure, we’re going to do it in VMs. I’m going to follow Microsoft’s documentation as much as possible, deviating only when necessary for on-premises deployments. I’m also going to explain the key Kubernetes concepts that you need to know to understand how all these pieces together. This is a long one, buckle up.

He is not kidding about the length of the post.

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Creating A SQL Server 2019 Big Data Cluster On Azure

Published 2018-11-12 by Kevin Feasel

Niels Berglund walks us through the setup for SQL Server 2019 Big Data Clusters:

If you, like me, are a SQL Server guy, you are probably quite familiar with installing SQL Server instances by mounting an ISO file, and running setup. Well, you can forget all that when you deploy a SQL Server 2019 Big Data Cluster. The setup is all done via Python utilities, and various Docker images pulled from a private repository. So, you need Python3. On my box I have Python 3.5, and – according to Microsoft – version 3.7 also works. Make you that you have your Python installation on the path.

When you deploy you use a Python utility: mssqlctl. To download mssqlctl, you need Python’s package management system pip installed. During installation you also need a Python HTTP library: Requests. If you do not have it you need to install it:
python -m pip install requests

This isn’t available to the general public quite yet, but when it is publicly available (or if you are part of the Early Access Program), the instructions are nice and clear.

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