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Using Docker and containers

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offers us various advantages.

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We talked about this in the first course section already.

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Now, up to this point,

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we can see one advantage in action already.

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We don't have to uninstall

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and re-install local dependencies all the time.

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Instead, we are already using Docker for example,

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in our assignment applications,

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but also in the applications we built before that,

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in this module.

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We're using Docker to install

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the dependencies of this node app inside of the image

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and container therefore,

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and we therefore don't have to install node

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globally on our system either.

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I happen to have it installed,

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but we wouldn't need that.

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And the same for Python.

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Thanks to Docker, which we use here.

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We don't need to install Python globally on our system,

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it's in the image and container instead.

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And even if that would be all we're using Docker for,

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that would already be great.

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because it would allow us

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to have multiple projects on our computer

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with different dependencies and different versions,

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and they could co-exist side by side

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without us having to uninstall

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or re-install various tools all the time.

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We could have a Python 3.7 based app

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next to a Python 3.9 based app

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without switching python versions in between.

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So that's great already.

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Still, this is not the only reason

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why we're typically using Docker.

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We're also using it to share our images

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and containers with others.

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That could be team members

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who are working on the same project,

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or that could be servers

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on which we wanna deploy our containers.

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Now I'll dive into deployment later

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since that involves a couple of steps,

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but I wanna start with sharing images

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and containers with other persons,

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or even with ourselves on different machines right now.

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Because there's one simple thing we have to keep in mind,

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everyone who has an image,

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can create containers based on that image.

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So that of course also means

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that we don't really share containers with anyone,

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instead, we'll share images.

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Because if you have an image,

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you can run a container based on that image.

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I mean, that's all to what we did before.

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We're building up our own images

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on the Python or node images.

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So we're also pulling down these images

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from Docker hub as I showed you earlier,

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and then we're able to use them to run our own containers,

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which in the end are also based on those images,

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at least partly.

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And when it comes to sharing images,

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we've got two main ways of doing that.

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We can share the Dockerfile.

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If I give you that Dockerfile

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and the source code that belongs to this application,

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you can build your own image

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and use that to run a container.

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And that's actually how the various

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code snapshot attachments you'll find in this course

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are organized.

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If you find an attachment on one of the lectures

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it's a SIPP file, which contains a Dockerfile

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and then the code belonging to that Dockerfile.

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And then you can build that image on your own

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and run container based on it.

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Now that's one way of sharing an image.

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I simply give you the Dockerfile

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and you're good to go.

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But that's, for example

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not what's happening when we're basing this image

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on the node image.

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We're not downloading the node Dockerfile anywhere.

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I never built the node image myself.

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And just to make this super clear,

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if you Google for GitHub node Docker,

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you find the official repository for the Docker node image.

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And in there, you will actually also find Dockerfiles.

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For example, for the 14 tag,

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let's say therefore this alpine version whatever,

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there you find a Dockerfile

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which in the end sets up this node image.

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It's based on alpine which is a Linux distribution.

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So it's the operating system for this node image so to say,

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and then it has various instructions in here

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obviously more complex instructions than in our basic image

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which do configure this image to set up a node environment.

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And this is the kind of image we are basing our image on

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if we're using the from instruction.

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But again, we didn't download

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and built the Dockerfile,

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instead, we just used this image like this.

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Also earlier in this course,

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when I executed docker run node,

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referring to that official node image.

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This also works.

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It downloads the latest note image

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and then just runs it as a container.

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In this case, it stops automatically

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since I didn't add an interactive session to the image

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but that doesn't matter here.

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The key takeaway is that here we didn't get a Dockerfile,

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which we built on our own.

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Because there always is a second way of sharing images

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besides sharing a Dockerfile

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and leaving it up to the recipient to build that Dockerfile,

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you can also share a complete built image.

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So not the Dockerfile, but the finished image.

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You can share this too.

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Now, when you get a Dockerfile,

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you have to build it

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and your therefore also need all the surrounding code

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and folder structure that should go into the image.

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If you get the image, you just download the image

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and you can run a container instantly.

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You don't need to build it

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and you don't need any extra code or surrounding folders.

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So there's no build step required here.

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Everything is included in the image already.

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And therefore that's how you typically

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share images with other people.

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Now, in the course I stick to these attachments

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with the raw code so that you can compare

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that to your code as well.

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But if you share it with a team member

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or also later for deployment,

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you typically work with finished images

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and not with raw Dockerfiles.