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A Mini-Lab: SQL Server 2019 on Docker

Ayman El-Ghazali has a three-parter for us. Part one involves installing Docker for Desktop on Windows and creating a SQL Server container:

Recently, with the help of a colleague at work, I’ve started to dabble a little with containers. I had a customer that requested some specific code to be tested, and I realized that I didn’t have my own local instance of SQL running (always good to have a local one). I decided to try to make this process easier instead of going the traditional route of creating a Virtual Machine and also to help me learn a new technology. In these series of posts, I’m going to document my process of creating a Mini Data Lab for SQL Server on my desktop using Docker. It is intended to be for beginners and in no way is an article for best practices or production deployments.

Part two includes persistent storage and some of the other niceties of hosting a database in a container:

Let’s first take a look at the way I have my disk/folder structure laid out. Again, this is on my personal computer so it’s not a best practice for production and more suitable for development environments.

For each container, I’m creating a separate folder with the MSSQL paths that I need to put my databases, transaction log, and backup files on. Additionally, under the DockerMount folder I have a folder called sqldockershared (which I will put some shared content in later).

Part three is about configuration in existing containers and deploying a second container side-by-side:

For those that are more curious in changing other Instance level properties here is a list of configurable properties for SQL Server on Linux via Bash.

Now we have a great foundation to create another container, so let us go and do that now. The code is similar to the previously created container with the exception of the file path for the data, log, and back files and the port number. The SQLShare path will be the same so that we can run our initialization script from there.

I’m bought-in on containers. There are still some pains around containers for production databases, but “some pain” is a much better experience than a few years ago, when the answer to the question of whether you want to use containers in production for databases was “Are you mad?”