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Appendix: General Docker Procedures

The sections below outline learnings from a beginners use of Docker (me). If they save you time, we're both happy.

Docker Installation

It's simple on a Mac, running natively. Other configurations may cause drama:

  • Virtualization - under virtualization (e.g., VMWare Fusion - running windows under Mac), it is much slower.
  • Bootcamp - I was not able to make it work -- Windows thought the firmware did not support virtualization (on a large Intel-based Macbook Pro)

On the Fusion Windows, it seemed that I needed Windows Pro (not Home). There are various sites that discuss Windows Home. I was not willing to fiddle with that, so I just went Pro, which worked well.


Creating Containerized API Logic Server Projects for VSCode

When you use API Logic Server to create projects, the resultant projects can run with a venv (locally installed Python), or in a Docker container.

To make this work, ApiLogicServer create builds the following files in your project:


Preparing a Python Image (for API Logic Server)

Recall that an image is something you can store on Docker Hub so others can download and run. It's a good idea for project to have a repository of docker images, such as ApiLogicServer, test databases, etc.

The running thing is called a container. They can but typically do not utilize local storage, instead accessing external files through mounts, and external systems (databases, APIs) via docker networks and ports.

I had to prepare a Docker image for ApiLogicServer (providing Python, API Logic Server CLI and runtime libraries). That requires a Dockerfile, where I also keep my notes.

The process was straight-forward using the noted links... until pyodbc was added for Sql Server. That added 500MB, and was quite complicated.


Preparing a Database Image (for self-contained databases)

In addition to the ApiLogicServer image, I wanted folks to be able to access a dockerized MySQL database. Further, I wanted this to be self-contained to avoid creating files on folks' hard drives.

I therefore needed to:

  1. acquire a self-contained MySQL image (again, that's not the default - the default is data persisted to a volume), and
  2. update this database with test data
  3. save this altered container as an image (docker commit...)

I used this Dockerfile which again includes my notes.


SQL Server Docker creation

It was prepared as described in this Dockerfile.

For JDBC tools, specify: jdbc:sqlserver://localhost:1433;database= NORTHWND