I've experienced a similar issue, which started to accure after I've updated my PC using windows update. I've then found this thread in the intel community forum. As it seems there is a bug in a driver for Intel UHD Graphics 4600, which is used by at least some of the CPUs which are used inside you notebook model according to my research.
For my CPU there have been no newer drivers solving this issue, but as a workaround I've been able to find a older version of the driver without the bug on the Intel support site. Note that you may should disable automatic driver updates as described here to avoid having the same issue again if this proves to be the problem.
I found out that it is truly impossible to have multiple docker files in one folder, but there is a workaround. I have created a separate subfolders with each having its own dockerfile for either windows or linux in it and nothing else. In the YAML file for the pipeline that creates those docker images, I have added a step before docker compose, that moves the dockerfile out of the subfolder and into the main folder where the relevant code files are. It is possible to have only one YAML file and create an IF statement that will select the correct dockerfile based on parameters of the pipeline run.
it seems that the reason why Docker uses much more memory is that it does not allow the operating system to use virtual memory (swap memory). What I didn't see in the original debugging, is that the python program consumes memory of several GB, but windows has automatically moved another several GB of memory to virtual memory to save RAM space. This is however not enabled in docker environment, so the only RAM available to the docker is the one that is specified in the set up phase.
When starting docker a bridge network is automatically started, this network has its own ip and you have to check what is the ip of that network. If you are running it locally and used localhost, the bridge network's adress might be different from localhost and that might be why you weren't able to connect. However, using the containers' name is a much safer way to communicate between servers, docker will choose the correct address.
First of all, while there is more or less one stable option for Java, there exist many different libraries for JavaScript and some of them don't interact very well with Docker. As such, I recommend using the socket.io-client client library to implement the socket client.
Docker actually allows you to address your components via the names of their containers. So instead of using localhost or other IP addresses to connect to your container, you should specify the name of the container, for example use the address ws://socketserver:4001
if the name of the container that hosts the server is socketserver and it runs on port 4001. Furthermore, in your docker-compose file, you should make sure that there are no conflicts between exposed ports, and that the relevant port on the server is exposed. After that, your components should be able to interact with each other without any issues.