How do I cache steps in GitHub actions? - github

Question

Say I have a GitHub actions workflow with 2 steps.
Download and compile my application's dependencies.
Compile and test my application
My dependencies rarely change and the compiled dependencies can be safely cached until I next change the lock-file that specifies their versions.
Is a way to save the result of the first step so that in future workflow can skip over that step?

Answer 1

Most use-cases are covered by existing actions, for example:
actions/setup-node for JS
docker/build-push-action for Docker
Custom caching is supported via the cache action. It works across both jobs and workflows within a repository. See also: GitHub docs and Examples.
Consider the following example:
name: GitHub Actions Workflow with NPM cache
on: push
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout#v3
- name: Cache NPM dependencies
uses: actions/cache#v3
with:
path: ~/.npm
key: ${{ runner.OS }}-npm-cache-${{ hashFiles('**/package-lock.json') }}
restore-keys: |
${{ runner.OS }}-npm-cache-
- name: Install NPM dependencies
run: npm install
How caching works step-by-step:
At the Cache NPM dependencies step, the action will check if there's an existing cache for the current key
If no cache is found, it will check spatial matches using restore-keys. In this case, if package-lock.json changes, it will fall back to a previous cache. It is useful to prefix keys and restore keys with the OS and name of the cache, as it shouldn't load files for a different type of cache or OS.
If any cache is found, it will load the files to path
The CI continues to the next step and can use the filed loaded from the cache. In this case, npm install will use the files in ~/.npm to save downloading them over the network (note that for NPM, caching node_modules directly is not recommended).
At the end of the CI run a post-action is executed to save the updated cache in case the key changes. This is not explicitly defined in the workflow, rather it is built into the cache action to take care of both loading and saving the cache.
You can also build your own reusable caching logic with #actions/cache such as:
1-liner NPM cache
1-liner Yarn cache
Old answer:
Native caching is not currently possible, expected to be implemented by mid-November 2019.
You can use artifacts (1, 2) to move directories between jobs (within 1 workflow) as proposed on the GH Community board. This, however, doesn't work across workflows.

Answer 2

The cache action can only cache the contents of a folder. So if there is such a folder, you may win some time by caching it.
For instance, if you use some imaginary package-installer (like Python's pip or virtualenv, or NodeJS' npm, or anything else that puts its files into a folder), you can win some time by doing it like this:
- uses: actions/cache#v2
id: cache-packages # give it a name for checking the cache hit-or-not
with:
path: ./packages/ # what we cache: the folder
key: ${{ runner.os }}-packages-${{ hashFiles('**/packages*.txt') }}
restore-keys: |
${{ runner.os }}-packages-
- run: package-installer packages.txt
if: steps.cache-packages.outputs.cache-hit != 'true'
So what's important here:
We give this step a name, cache-packages
Later, we use this name for conditional execution: if, steps.cache-packages.outputs.cache-hit != 'true'
Give the cache action a path to the folder you want to cache: ./packages/
Cache key: something that depends on the hash of your input files. That is, if any packages.txt file changes, the cache will be rebuilt.
The second step, package installer, will only be run if there was no cache
For users of virtualenv: if you need to activate some shell environment, you have to do it in every step. Like this:
- run: . ./environment/activate && command

Answer 3

My dependencies rarely change and the compiled dependencies can be safely cached until I next change the lock-file that specifies their versions. Is a way to save the result of the first step so that in future workflow can skip over that step?
The first step being:
Download and compile my application's dependencies.
GitHub Actions themselves will not do this for you. The only advice I can give you is that you adhere to Docker best practices in order to ensure that if Actions do make use of docker caching, your image could be re-used instead of rebuilt. See: https://docs.docker.com/develop/develop-images/dockerfile_best-practices/#leverage-build-cache
When building an image, Docker steps through the instructions in your Dockerfile, executing each in the order specified. As each instruction is examined, Docker looks for an existing image in its cache that it can reuse, rather than creating a new (duplicate) image.
This also implies that the underlying system of GitHub Actions can/will leverage the Docker caching.
However things like compilation, Docker won't be able to use the cache mechanism, so I suggest you think very well if this is something you desperately need. The alternative is to download the compiled/processed files from an artifact store (Nexus, NPM, MavenCentral) to skip that step. You do have to weight the benefits vs the complexity you are adding to your build on this.

Answer 4

This is now natively supported using: https://help.github.com/en/actions/automating-your-workflow-with-github-actions/caching-dependencies-to-speed-up-workflows.
This is achieved by using the new cache action: https://github.com/actions/cache

Answer 5

If you are using Docker in your WorkFlows, as #peterevans answered, GitHub now supports caching through the cache action, but it has its limitations.
For that reason, you might find useful this action to bypass GitHub's action limitations.
Disclaimer: I created the action to support cache before GitHub did it officially, and I still use it because of its simplicity and flexibility.

Answer 6

I'll summarize the two options:
Caching
Docker
Caching
You can add a command in your workflow to cache directories. When that step is reached, it'll check if the directory that you specified was previously saved. If so, it'll grab it. If not, it won't. Then in further steps you write checks to see if the cached data is present. For example, say you are compiling some dependency that is large and doesn't change much. You could add a cache step at the beginning of your workflow, then a step to build the contents of the directory if they aren't there. The first time that you run it won't find the files but subsequently it will and your workflow will run faster.
Behind the scenes, GitHub is uploading a zip of your directory to github's own AWS storage. They purge anything older than a week or if you hit a 2GB limit.
Some drawbacks with this technique is that it saves just directories. So if you installed into /usr/bin, you'll have to cache that! That would be awkward. You should instead install into $home/.local and use echo set-env to add that to your path.
Docker
Docker is a little more complex and it means that you have to have a dockerhub account and manage two things now. But it's way more powerful. Instead of saving just a directory, you'll save an entire computer! What you'll do is make a Dockerfile that will have in it all your dependencies, like apt-get and python pip lines or even long compilation. Then you'll build that docker image and publish it on dockerhub. Finally, you'll have your tests set to run on that new docker image, instead of on eg, ubuntu-latest. And from now on, instead of installing dependencies, it'll just download the image.
You can automate this further by storing that Dockerfile in the same GitHub repo as the project and then write a job with steps that will download the latest docker image, rebuild if necessary just the changed steps, and then upload to dockerhub. And then a job which "needs" that one and uses the image. That way your workflow will both update the docker image if needed and also use it.
The downsides is that your deps will be in one file, the Dockerfile, and the tests in the workflow, so it's not all together. Also, if the time to download the image is more than the time to build the dependencies, this is a poor choice.
I think that each one has upsides and downsides. Caching is only good for really simple stuff, like compiling into .local. If you need something more extensive, Docker is the most powerful.