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Creating a Power BI demo environment with SQL Server 2019 on Linux in a Docker Container as the data source. — December 5, 2018

Creating a Power BI demo environment with SQL Server 2019 on Linux in a Docker Container as the data source.

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.

Prerequisites:

Setup

Once you have Power BI installed you will have to enable Hyper-V windows feature before installing Docker for Windows. If you don’t, the docker install will ask you if you wish to enable Hyper-V but in my experience this has rarely (ie. never) worked well – could just be me but I play it safe and enable it before installing. Running Linux containers on Windows 10 inevitably means that the Container will run a Isolated / Hyper-V Container which basically runs a cut down, lightweight Hyper-V VM.

Once DfWCE is installed, you will have to modify the Memory settings to be at least 3250MB. By default this is 2048MB but this isn’t enough to run SQL Server On Linux Containers and needs to be amended.

 

Make sure this is min 3250MB

Once configured, we’re now ready to create our SQL Server 2019 CTP2.1 on Ubuntu. As we are going to be creating a container with a database that we can use the data from (ie. AdventureWorks2016CTP3), we’re first going to create a data volume and copy the bak file (from C:\Docker\SQLServer\Backups) to it so it is available to restore:

#Create dummy container to define and copy backup file
docker container create --name dummycontainer -v sqldatavolume:/sqlserver/data/ sqlgeordie/sqlonlinuxdemo:demo

#Check that the container exists
docker ps -a --format $psformat

#Copy AdventureWorks or whatever you like ;)
docker cp C:\Docker\SQLServer\Backups\AdventureWorks2016CTP3.bak dummycontainer:/sqlserver/data/AdventureWorks2016CTP3.bak

#Check what volumes you have
docker volume ls

#Don't need the dummycontainer anymore so get rid
docker rm dummycontainer

NOTE: We have “created” a container but don’t need to run it so the status will indicate that it is “Created” and not the up-time:

You may be asking why I’ve created a datavolume as opposed to just bind mounting my local folder to the container – good question. There are many blog posts out there explaining the pros/cons of both but for me the biggest reason is that mounting a host folder goes against everything Containers are about – portability. The container shouldn’t care about where it is or anything outside of it, bind mounting means that wherever this container is created, you will need that host folder setup and available. Using a data volume means we can reuse this for other containers, even on another host if we were in an environment that has access.

Have a read here and here for more information.

Now we have our data volume with a copy of AdventureWorks2016CTP3.bak, we can proceed to creating the container:

#Create SQLLinuxLocalPersist container
docker run  -e 'ACCEPT_EULA=Y' -e 'SA_PASSWORD=P@ssword1' `
--name SQLLinuxLocalPersist -d `
-p 1433:1433 `
-v sqldatavolume:/sqlserver/data `
mcr.microsoft.com/mssql/server:2019-CTP2.1-ubuntu

#Check it is running
docker ps -a

All going well, your container should be created and running:

The next step is to create the database from the backup, we will do this manually via Azure Data Studio (Other SQL Server Management GUI’s are available).

Simply connect to our locally created container. I have used the default SQL Server port of 1433 so we can use “Localhost”. As windows authentication is not currently supported, we have to use a SQL Login, in this example I’m using “sa”:

Next, we want to restore the DB:

You should now have a restored version of AdventureWorks2016CTP3 ready to use and abuse in Power BI. Simply setup a connection to (again) Localhost, no port mapping is required in this instance but if you had a different port to 1433 you’d have to use something like: Localhost,15105.

Once connected, you can play around with the data til your hearts content 🙂

There we have it, a fully working Power BI demo / sandbox environment ready for you to play around with. The longest aspect of this setup will be the downloading/installing of Power BI Desktop and DfWCE. Once setup, the scripting of creating the container can be done in a minute or two -AMAZING!!!!!

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