Optimize Airflow images for cross-version caching #21217
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Airflow produces images for multiple versions. Sometimes we release several images for different versions close to one another when base Python image is the same. In those cases it would be great if images for different Airflow versions shared as much as possible of image layers - the "base" dependencies usually do not change between versions so two images with the same Python version could only start differring at the moment we start to install Airflow for the first time. This change moves AIRFLOW_VERSION ARG declaration and ENV variable as late as possible, so that earlier layers in the Docker images are the same for different Airflow image versions as long as the base Python image is the same. This should decrease significantly used space if someone switches between two Airflow versions in the same system (for testing or when doing migration). It also should speed up cache refreshing and rebuilding and decrease overall size of the cache used by multiple Airflow Images (especially when we are close to release (like we are now with Airlfow 2.2.4)
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eladkal
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The PR most likely needs to run full matrix of tests because it modifies parts of the core of Airflow. However, committers might decide to merge it quickly and take the risk. If they don't merge it quickly - please rebase it to the latest main at your convenience, or amend the last commit of the PR, and push it with --force-with-lease. |
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Airflow produces images for multiple versions. Sometimes we
release several images for different versions close to one another
when base Python image is the same. In those cases it would be
great if images for different Airflow versions shared as much
as possible of image layers - the "base" dependencies usually
do not change between versions so two images with the same Python
version could only start differring at the moment we start to
install Airflow for the first time.
This change moves AIRFLOW_VERSION ARG declaration and ENV variable
as late as possible, so that earlier layers in the Docker images are
the same for different Airflow image versions as long as the base
Python image is the same.
This should decrease significantly used space if someone switches
between two Airflow versions in the same system (for testing
or when doing migration). It also should speed up cache
refreshing and rebuilding and decrease overall size of the cache
used by multiple Airflow Images (especially when we are close
to release (like we are now with Airlfow 2.2.4)
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