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.
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.
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.
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.
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.
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 downloadmssqlctl, you need Python’s package management systempipinstalled. 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.
Anthony Nocentino shows us how to install Kubernetes on-prem:
Kubernetes is a distributed system, you will be creating a cluster which will have a master node that is in charge of all operations in your cluster. In this walkthrough we’ll create three workers which will run our applications. This cluster topology is, by no means, production ready. If you’re looking for production cluster builds check out Kubernetes documentation. Here and here. The primary components that need high availability in a Kubernetes cluster are the API Server which controls the state of the cluster and the etcddatabase which stores the persistent state of the cluster. You can learn more about Kubernetes cluster components here.
In our demonstration here, the master is where the API Server, etcd, and the other control plan functions will live. The workers, will be joined to the cluster and run our application workloads.
This is an area I need to focus on, given my almost total lack of knowledge in the world of container orchestration.
Chris Adkin gives us a rundown on Kubernetes:
With the announcement of SQL Server 2019 big data clusters at Ignite, Kubernetes (often abbreviated to K8s) now stands front and center as part of Microsoft’s data platform vision. The obvious inference being that this is something that the Microsoft data platform community is going to show an increased interest in. The post aims to provide some context around:
why container orchestration is required
how Kubernetes is architected
the basics of working with Kubernetes
and why embracing open source software should be approached in an eyes wide open manner
Kubernetes is another technology which is useful to learn and can be helpful down the line.
Elton Stoneman shares a few useful aliases in Powershell for managing Docker containers:
Docker PowerShell Alias #2 –
drmfRemoves all containers, whether they’re running or not. Useful when you want to reset your running containers and get back to zero:
function Remove-AllContainers { docker container rm -f $(docker container ls -aq) } Set-Alias drmf Remove-AllContainersUse with caution
Elton shares several more at the link and also includes a link to a Github gist with them all.
First, if you are running most of your other applications and microservices on Kubernetes, it becomes the organizational path of least resistance. This is just like how organizations who standardized on VMs have found it very difficult to allocate physical machines with local disks for Kafka.
I see situations with larger organizations where deploying Kafka outside of Kubernetes causes significant organizational headache that involves many approvals. When this is the case, I usually say that this isn’t a good hill to die on. It is possible to run Kafka on Kubernetes, so just do it. You’ll get your environment allocated faster and will be able to use your time to do productive work rather than fight an organizational battle.
And if things go wrong, you’ll get much better service from your internal infrastructure teams, because you’ll be running in an environment that is familiar to them.
Read on for more benefits as well as a few drawbacks.
Kevin Feasel
2018-12-06
Containers, Power BI
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