Shared cache

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Shared cache
Earthly has the ability to share cache between different isolated CI runs and even with developers. This page goes through the available features, common use-cases and situations where shared cache is most useful.
Shared caching is made possible by storing intermediate steps of a build in a cloud-based Docker registry. This cache can then be downloaded on another machine in order to skip common parts.
Types of shared cache
Earthly makes available two types of shared caches:
​Inline cache​
​Explicit cache (advanced)
For a summary of the differences see comparison between inline and explicit cache.
Compatibility with major registry providers
Not all registries support the needed manifest formats to allow the usage of each kind of cache. Here is a compatibility matrix for many popular registries:
Registry
Supports Inline Cache
Supports Explicit Cache
Notes
AWS ECR
✅
❌
https://github.com/aws/containers-roadmap/issues/876
Google GCR
✅
❌
​
Google Artifact Registry
✅
✅
​
Azure ACR
✅
✅
​
Docker Hub
✅
✅
​
GitHub Container Registry
✅
✅
​
GitLab Container Registry
✅
✅
​
Self-Hosted registry:2
✅
✅
​
JFrog Artifactory
✅
✅
Only versions > 7.31.10 work, due to https://www.jfrog.com/jira/browse/RTFACT-26179
Inline cache
Inline caching is the easiest to configure. It essentially makes use of any image already being pushed to the registry and adds some very small metadata (a few KiB) as part of its configuration about how Earthly is able to reuse that image for future runs.
The key benefit of this approach is that you get the upload for free if you anyway push images to the registry.
How to use inline caching
In order to enable inline caching, simply add --ci in your invocation of earthly in your CI, or --use-inline-cache on individual developer's machines. If the --push command is also specified, the use of the cache will be read-write.
In CI, read-only inline cache (typically in PR builds):
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earthly --ci +some-target
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In CI, read-write inline cache (typically in master/main branch builds):
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earthly --ci --push +some-target
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On developer's computer (optional):
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earthly --use-inline-cache +some-target
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The options mentioned above are also available as environment variables. See Earthly command reference for more information.
The way this works underneath is that Earthly uses SAVE IMAGE --push declarations as source and destination for any inline cache.
In case different Docker tags are used in branch or PR builds, it is possible to use additional cache sources via SAVE IMAGE --cache-from=.... This may be useful so that PR builds are able to use the main branch cache. Here is a simple example:
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FROM ...
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...
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ARG BRANCH=master
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SAVE IMAGE --cache-from=mycompany/myimage:master --push mycompany/myimage:$BRANCH
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The --ci flag will enable, among other things, both --use-inline-cache and --save-inline-cache flags. The --use-inline-cache flag is required to enable importing existing caches, and the --save-inline-cache flag is required to enable exporting images to the remote cache.
Optimizing inline cache performance
Inline caching is very easy to use, however it can also turn out to be ineffective for some builds. One limitation is that only the layers that end up in uploaded images are actually used. Certain intermediate layers (e.g. targets only used for compiling binaries) will not exist.
If you find that certain steps could benefit from being cached but are not, you may consider creating additional images for those steps specifically. All you need to do is add the following at the end. Use a Docker tag that is not used for anything else.
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SAVE IMAGE --push <docker-tag>
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Note however that adding more images to the build results in additional time spent uploading them. Disregard the performance of the very first upload, as a fresh push is always less performant because there is no commonality with any previous run.
Example of using inline caching
Good example uses of inline caching are the Earthly C++ and Scala samples.
In the C++ case, a lot of computation is saved as a result of the apt-get install command. Reusing the cache improves performance by a factor of 4X.
In the Scala case, time is saved from processing the dependencies, resulting in a 3X performance improvement.
In both cases, a major benefit is that we are anyway pushing the images to the cloud via the SAVE IMAGE --push commands. So there is no performance penalty on the cache upload side. The command that would be used in the CI to execute the builds together with inline caching is
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earthly --ci --push +docker
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Explicit cache (advanced)
Explicit caching requires that you dedicate a Docker tag specifically for cache storage. Unlike inline caching, this tag is not meant to be used for anything else. For this reason, uploading the cache is an added step in your runs, which may affect performance.
How to use explicit caching
To enable explicit caching, use the flag --remote-cache=... to specify the Docker tag to use as cache. Make sure that this Docker tag is not used for anything else (e.g. DO NOT use myimage:latest, in case latest is used in a critical workflow).
For example, if the Docker tag used for explicit caching is mycompany/myimage:cache, then the flag can be used as follows.
In CI, read-only inline cache (typically in PR builds):
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earthly --ci --remote-cache=mycompany/myimage:cache +some-target
Copied!
In CI, read-write inline cache (typically in master/main branch builds):
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earthly --ci --remote-cache=mycompany/myimage:cache --push +some-target
Copied!
On developer's computer (optional):
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earthly --remote-cache=mycompany/myimage:cache +some-target
Copied!
The options mentioned above are also available as environment variables. See Earthly command reference for more information.
Note
If a project has multiple CI pipelines or earthly invocations, it is recommended to use different --remote-cache Docker tags for each pipeline or invocation. This will prevent the cache from being overwritten in ways in which it makes it less effective.
Note
It is currently not possible to push both inline and explicit caches currently.
Optimizing explicit cache performance (advanced)
Explicit caching works by storing a cache containing all the layers of the final target, plus any target containing SAVE IMAGE --push .... If additional targets need to be added as part of the cache, it is possible to add SAVE IMAGE --cache-hint (no Docker tag necessary) at the end, in order to mark them for explicit caching.
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deps:
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  COPY go.mod go.sum ./
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  RUN go mod download
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  SAVE IMAGE --cache-hint
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Making use of explicit caching effectively may not always be possible. Sometimes the overhead of uploading and redownloading the cache defeats the purpose of gaining build performance. Oftentimes, multiple iterations of trial-and-error need to be attempted in order to optimize its effectiveness. Keep in mind that caching compute-heavy targets is more likely to yield results, rather than download-heavy targets.
As an additional setting available, Earthly can be instructed to save all intermediary steps as part of the explicit cache. The setting --max-remote-cache can be used to enable this. Note that this results in large uploads and is usually not very effective. An example where this feature is useful, however, is when you would like to optimize CI run times in PRs, and are willing to sacrifice CI run times in default branch builds. This can be achieved by enabling --push and --max-remote-cache on the default branch builds only.
Example of using explicit caching
A good example of using explicit caching is this integration test example. The target +project-files is perfect for introducing a cache hint via SAVE IMAGE --cache-hint. The processing that takes place as part of installing Scala and compiling the dependencies is sufficiently compute-intensive to save ~2 min from the total build time in CI. In addition, these dependencies change rarely enough that the cache can be utilized consistently.
A typical invocation of the build to make use of the explicit cache:
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earthly --ci --remote-cache=mycompany/integration-example:cache --push +all
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Comparison between inline and explicit cache
Inline and explicit caching have similar traits, but they also have a number of fundamental differences.
The key similarity is that both types of caches make use of Docker tags being pushed to an image registry in order to store the cache.
The most important difference is that inline caching relies on image uploads that are already being made. And as such, the cache may be split across multiple separate images. Every SAVE IMAGE --push command adds more cacheable targets in the form of separate images. However, in the case of explicit caching, the entire cache is stored as part of a single Docker tag and every SAVE IMAGE --cache-hint command adds more cacheable targets within the image. This final image containing all the explicit cache cannot be used for anything else. So as a user, you incur the performance cost of both the upload and the subsequent download.
Below is a summary of the different characteristics of each type of cache.
Key takeaways for inline caching
Cache is embedded within images that are already being pushed. No new layers are added to the images, only a few KiB of metadata.
Very easy to use (just add --ci to your earthly invocations in CI)
It is usually effective right away, with little modifications
Typically you incur the performance cost only for the subsequent download. Upload is for free if you are pushing images anyway
By default, caches only the images being pushed
You can add more cache via additional SAVE IMAGE --push <docker-tag> commands
Key takeaways for explicit caching
Cache is uploaded as part of a new Docker tag that should not be used for anything else
The only available choice if no images are already pushed during the build
More control over what is being cached and what is not. However it often requires some level of experimentation to get right.
Incur the performance cost for both the upload and the download
By default, caches only the layers of the target being built, and not of any other referenced targets
You can cache additional targets by adding SAVE IMAGE --cache-hint commands
When to use shared cache
There are several situations where shared caching can provide a significant performance boost. The following are only a few examples of how to get a feel for its usefulness.
Compute-heavy vs Download-heavy
In general shared cache is very useful when there is a significant computation overhead during the execution of your build. Assuming that the inputs of that computation do not change regularly, then shared caching could be a good candidate. If a time-consuming operation, however, is not compute-heavy, but rather download-heavy, then shared cache may not be as effective (it's one download versus another).
As an example of this distinction, consider the use of the apk tool shipped in alpine images. Installing packages via apk is download-heavy, but usually not very compute-heavy, and so using shared caching to offset apk download times might not be as effective. On the other hand, consider apt-get tool shipped in ubuntu images. Besides performing downloads, apt-get also performs additional post-download steps which tend to be compute-intensive. For this reason, shared caching is usually very effective here.
Similarly to the comparison between apk and apt-get, similar remarks can be made about the various language-specific dependency management tools. Some will be pure download-based (e.g. go mod download), while others will be a mix of download and computation (.e.g sbt).
An intermediate result is small and doesn't change much
An area where shared cache is particularly impactful are cases where a rare-changing prerequisite downloads many dependencies and/or performs intensive computation, but the end result is relatively small (e.g. a single binary). Passing this prerequisite over the wire as part of the shared cache is very fast (especially if the downloads required to generate it are not used anywhere else), whereas regenerating it requires a lot of work.
Monorepo and Polyrepo setups
An excellent example of the above are typical inter-project dependencies. Regardless of whether your layout is a monorepo or a polyrepo, if projects reference artifacts or images from each other, then whatever tools used to generate those artifacts or images are usually not required across projects. In such cases it is possible to prevent entire target trees of downloads and computation and simply download the final result using the shared cache.
A simple way to visualize this use-case is comparing the performance of a build that takes place behind a FROM +some-target instruction versus just using the previously built image directly. If +some-target has a SAVE IMAGE --push myimage:latest instruction, then the performance becomes almost the same to using FROM myimage:latest directly.
CIs that operate in a sandbox
Modern CIs execute in a sandbox. They start with a blank slate and need to download and regenerate everything from scratch. Examples of such CIs: GitHub Actions, Circle CI, DroneCI, GitLab CI. Such CIs benefit greatly from being able to share precomputed steps between runs.
If, however, you are using a CI which reuses the same environment (e.g. Jenkins, BuildKite - depending on how they are configured), then simply relying on the local cache is enough.
Shared cache for developers
It is possible to use cache in read-only mode for developers to speed up local development. This can be achieved by enabling read-write shared caching in CI and read-only cache for individual developers. Since all Earthly cache is kept in Docker registries, managing access to the cache can be controlled by managing access to individual Docker images.
Note however that there is small performance penalty for regularly checking the remote registry on every run.
Advanced local caching
Using Docker in Earthly
Last modified 2mo ago
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Contents
Types of shared cache
Compatibility with major registry providers
Inline cache
Explicit cache (advanced)
Comparison between inline and explicit cache
When to use shared cache
Compute-heavy vs Download-heavy
An intermediate result is small and doesn't change much
Monorepo and Polyrepo setups
CIs that operate in a sandbox
Shared cache for developers