First q - nope! When people refer to the executor, they (should) only mean "the mechanism of which will be the acting environment for your pipeline commands to run in". If you use a shell-executor, it could be performed by the docker install or the binary install of the runner, and same for the docker-executor 2. Performance can be a bit of an issue - the trade off with dockerized builds are that 1 computer/VM can run multiple, simultaneous, totally isolated pipelines, whereas a shell runner can only safely run 1. While performance might drop off, there's the gain of not having to have several machines/vm's created (much more overhead from a full-os vs just a container). For context, I see network latency increase when I use a docker-windows executor running 5 different queued pipeline tasks on this 1 machine (16gb Ram, 8 CPU) and some CPU drop off, but if I were to spin up 5 different machines to handle this same work load (5 pipeline tasks queued at the same time, I'm probably not fully utilizing the resources but I definitely am hogging more resources that could be leveraged. Now taking into consideration that you have both a shell & a docker executor, it would depend on what kind of code you're pipeline is working with and how a build consumes resources - if you are building a java project, it would be hard to see a performance impact unless it's true massive, but for something like NodeJS with tons of packages, it would definitely cause impact if the shell runner executed a job while the docker executor took a few jobs as well, if say, using that same 16gb RAM & 8 Core machine. For some organization, I've kept shell runners on their own machine, because if someone edits their gitlab ci file to do something that affects the shell runner permanently (like "sudo apt upgrade", or something that causes a variable to linger beyond the build that affects the next build) then I have to worry about fixing that issue. There are good use cases for the shell runners though, but the docker executors provide the quickest way to scale pipelines to 100+ repositories, averaging 10 builds an hour during work hours (20 min pipeline execution). In some sort of motivational stance, if you have the extra machine power waiting around, I think you should be adding more features into your pipeline, which would then show you the need for the simultaneous task execution. Gitlab 's SAST jobs, coverage testing, linting, or even live execution of code for smoke tests or things of that nature could be explored to add value to the pipelines. All those combined would create a lengthy pipeline execution time, so there's more reason to attempt to run as many as these in parallel as possible. More on reddit.com

I have ran gitlab-ci-multi-runner in a container by docker. When I execute the gitlab-ci-multi-runner register in the container,it ask Please enter the executor: docker...? If I choose docker,d... More on stackoverflow.com

You don't have to use the ssh executor in order to deploy to a different machine. In fact I would argue that you probably wouldn't want to. The executor is just for defining where the tasks in your `.gitlab-ci.yml` are run. The way I would approach this is using CI Variables for your ssh configuration properties (ssh key, hostname, etc.), then define task(s) to deploy whatever artifacts you need to the target machine. What this approach gives you is the ability to segregate you CI tasks from your production machine. More on reddit.com

I don't want to run my code, I just want to compile it The executor executes your build script, compiling is the stuff you would put in your build script. So no, it's not really for running your actual application. Docker is useful for this because it's isolated, you could also run it directly on your machine but setting up build environments manually sucks. It's better to invest a bit of time to find / create docker images you can use for your builds. For popular tools, many have official docker images anyway. It's not a stupid restriction, I have set up both runners using the shell (or whatever it's called exactly) and docker and both were painless & fast to set up and use. More on reddit.com