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Simplify caching mechanisms for CI and PROD images (regular PR) #45261
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Nice looking forward for this :) |
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- name: "Cleanup docker" | ||
run: ./scripts/ci/cleanup_docker.sh | ||
shell: bash | ||
# TODO: Currently we cannot loop through the list of python versions and have dynamic list of |
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cc: @assignUser -> will need an option to restore several keys in a single action for that. You might see some people from our team contributing to the stash action of yours :)
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Also opened a "canary" build from "apache" repo to check how caching will work #45266 I will eventually close that one and will mark "ready for review" the other one, as we cannot get this one to be green due to pull_request_target workflow failing. |
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For a long time we had used a sophisticated mechanism to speed up our CI jobs by building the images in "pull_request_target" workflow and pushing them to GitHub registry. That however had several drawbacks: * CI image was complex when it comes to layer setup (we had to pre- cache installed dependencies by installing them from branch tip * The pull_request_target is a very dangerous workflow, we had a number of security problems with it (and it's difficult to debug) * Caching of `pip` and `uv` was not used because it increased size of the image significantly This PR significantly improves the caching mechanisms for the images building of several advacements that were not possible before: * The upload-artifacts@v4 action and improved stash action developed by @assignUser and published in "apache/infrastructure-actions" allows us to store all images (8GB per run) in artifacts rather than in registry - so we can do the image build once and share it with all the jobs. * The uv speed is "enough" to allow occasional installation of Airlfow locally. This allows to utilize cache-mount and locally build uv cache, rather than rely on "remote" cache when we are building local images for breeze. The first time you build local breeze image it will take 2-5 more minutes (depending on your network speed, but because we can utilise cache mounts, every subsequent build should be very fast - even if all dependencies change. Using uv also allows to "always" reinstall airflow when you build the image even if single source file changed, because with cache it takes sub-seconds to reinstall airflow and all dependencies. * the cache mounts are not included in the image size, and since we can export and import images in CI in artifacts and we do not need to rebuild them, the images shared as compressed artifacts are relatively small (2GB) - cache of `uv` is around 4GB on top of that so sharing image built in the "build image" job with other jobs in the same workflow is fast. * we are still using registry cache for the "non-python" parts of the image - both CI and breeze image build speed benefit from using the image cache for system dependencies, database clients etc. this helps with faster rebuilds of the images for local development environment * documentation has been updated to reflect the new CI setup. The diagrams showing the workflows of ours are no longer needed as the workflows are quite straightforward when they are looked at. Fixes: apache#42999 Fixes: apache#43268
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Merged in #45266 |
For a long time we had used a sophisticated mechanism to speed up our CI jobs by building the images in "pull_request_target" workflow and pushing them to GitHub registry. That however had several drawbacks:
CI image was complex when it comes to layer setup (we had to pre- cache installed dependencies by installing them from branch tip
The pull_request_target is a very dangerous workflow, we had a number of security problems with it (and it's difficult to debug)
Caching of
pip
anduv
was not used because it increased size of the image significantlyThis PR significantly improves the caching mechanisms for the images building of several advacements that were not possible before:
The upload-artifacts@v4 action and improved stash action developed by @assignUser and published in "apache/infrastructure-actions" allows us to store all images (8GB per run) in artifacts rather than in registry - so we can do the image build once and share it with all the jobs.
The uv speed is "enough" to allow occasional installation of Airlfow locally. This allows to utilize cache-mount and locally build uv cache, rather than rely on "remote" cache when we are building local images for breeze. The first time you build local breeze image it will take 2-5 more minutes (depending on your network speed, but because we can utilise cache mounts, every subsequent build should be very fast - even if all dependencies change. Using uv also allows to "always" reinstall airflow when you build the image even if single source file changed, because with cache it takes sub-seconds to reinstall airflow and all dependencies.
the cache mounts are not included in the image size, and since we can export and import images in CI in artifacts and we do not need to rebuild them, the images shared as compressed artifacts are relatively small (2GB) - cache of
uv
is around 4GB on top of that so sharing image built in the "build image" job with other jobs in the same workflow is fast.we are still using registry cache for the "non-python" parts of the image - both CI and breeze image build speed benefit from using the image cache for system dependencies, database clients etc.
documentation has been updated to reflect the new CI setup. The diagrams showing the workflows of ours are no longer needed as the workflows are quite straightforward when they are looked at.
Fixes: #42999
Fixes: #43268
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