Improve testing harness to separate DB and non-DB tests #35160
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This PR marks DB tests as such and allows to split execution of the tests in CI to run the DB tests with the various database while the non-db tests - without the DB in a separate run. In order to do that, the code to select which tests to run has been moved from `entrypoint_ci.sh` bash to breeze's Python code, which is generally much nicer to maintain and common for both "DB" and "non-DB" tests. This will have the nice side effect that it will be easier in the future to manage different test types and contain some specific flaky test types. This change also adds possibility to isolate some of the test types when parallel DB tests are run and adds new test type PythonOperator carved out Operator type. This test is best run in isolation becasue creating and destroing virtualenvs in Docker while running in parallel to other tests is very slow for some reason and leads to flaky tests. Python operator tests are therefore separated out from Operators and treated separately as isolated tests. This will help not only with speed but also with stability of the test suite.
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This PR is separated out from #35160 in order to make the big refactor and splitting out our test harness to DB and Non-DB tests far more easy to review. This change contains purely adding db_tests markers to the tests that need them, and as such it requires very litte effort to review. Once this one is merged hoever, the #35160 will become way smaller in terms of number of files to review, which will make it far easier to review using GitHub Interface. (cherry picked from commit 1aa91a4)
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This PR marks DB tests as such and allows to split execution of the tests in CI to run the DB tests with the various database while the non-db tests - without the DB in a separate run. In order to do that, the code to select which tests to run has been moved from `entrypoint_ci.sh` bash to breeze's Python code, which is generally much nicer to maintain and common for both "DB" and "non-DB" tests. This will have the nice side effect that it will be easier in the future to manage different test types and contain some specific flaky test types. This change also adds possibility to isolate some of the test types when parallel DB tests are run and adds new test type PythonOperator carved out Operator type. This test is best run in isolation becasue creating and destroing virtualenvs in Docker while running in parallel to other tests is very slow for some reason and leads to flaky tests. Python operator tests are therefore separated out from Operators and treated separately as isolated tests. This will help not only with speed but also with stability of the test suite. (cherry picked from commit a7e76ba)
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Turns out that some of the layers in our PROD image got invalidated because AIRFLOW_CONSTRAINTS_MODE used to build the cache for PROD image is "constraints" by default, while building images in "build-images" workflow for regular PRs and canary build uses "constraints-source-providers". The former is fine as default for PROD image (as oppose to CI image we build PROD image from released PyPI packages by default) but the latter is "proper" for the CI cache, because there, the image is built out of local packages prepared from sources. Turns out that the CONSTRAINT_MODE parameter had a profound impact on caching - because it was set before the "install_packages_from_branch_tip" step and - in fact - even before "install database clients" step, which caused our cache to only work for the "base OS dependencies" - installing database clients and installing airflow from branch tip (which works great for CI image) had always been done in PRs because the layers in cache with constraints env invalidated all subsequent layers. This had no big impact before when testing usually took much longer time - but since the testing has been vastly improved in #35160, now PROD image building continues running even after test complete and becomes the next frontier of optimization. This PR optimizes PROD image building in two ways: * caching is prepared with "source_providers" constraint mode, same as regular build * the AIRFLOW_CONSTRAINT_MODE and related arguments are moved after installing database clients, so that this parameter does not impact their caching.
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Turns out that some of the layers in our PROD image got invalidated because AIRFLOW_CONSTRAINTS_MODE used to build the cache for PROD image is "constraints" by default, while building images in "build-images" workflow for regular PRs and canary build uses "constraints-source-providers". The former is fine as default for PROD image (as oppose to CI image we build PROD image from released PyPI packages by default) but the latter is "proper" for the CI cache, because there, the image is built out of local packages prepared from sources. Turns out that the CONSTRAINT_MODE parameter had a profound impact on caching - because it was set before the "install_packages_from_branch_tip" step and - in fact - even before "install database clients" step, which caused our cache to only work for the "base OS dependencies" - installing database clients and installing airflow from branch tip (which works great for CI image) had always been done in PRs because the layers in cache with constraints env invalidated all subsequent layers. This had no big impact before when testing usually took much longer time - but since the testing has been vastly improved in #35160, now PROD image building continues running even after test complete and becomes the next frontier of optimization. This PR optimizes PROD image building in two ways: * caching is prepared with "source_providers" constraint mode, same as regular build * the AIRFLOW_CONSTRAINT_MODE and related arguments are moved after installing database clients, so that this parameter does not impact their caching.
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Nov 10, 2023
This PR marks DB tests as such and allows to split execution of the tests in CI to run the DB tests with the various database while the non-db tests - without the DB in a separate run. In order to do that, the code to select which tests to run has been moved from `entrypoint_ci.sh` bash to breeze's Python code, which is generally much nicer to maintain and common for both "DB" and "non-DB" tests. This will have the nice side effect that it will be easier in the future to manage different test types and contain some specific flaky test types. This change also adds possibility to isolate some of the test types when parallel DB tests are run and adds new test type PythonOperator carved out Operator type. This test is best run in isolation becasue creating and destroing virtualenvs in Docker while running in parallel to other tests is very slow for some reason and leads to flaky tests. Python operator tests are therefore separated out from Operators and treated separately as isolated tests. This will help not only with speed but also with stability of the test suite.
romsharon98
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Nov 10, 2023
Turns out that some of the layers in our PROD image got invalidated because AIRFLOW_CONSTRAINTS_MODE used to build the cache for PROD image is "constraints" by default, while building images in "build-images" workflow for regular PRs and canary build uses "constraints-source-providers". The former is fine as default for PROD image (as oppose to CI image we build PROD image from released PyPI packages by default) but the latter is "proper" for the CI cache, because there, the image is built out of local packages prepared from sources. Turns out that the CONSTRAINT_MODE parameter had a profound impact on caching - because it was set before the "install_packages_from_branch_tip" step and - in fact - even before "install database clients" step, which caused our cache to only work for the "base OS dependencies" - installing database clients and installing airflow from branch tip (which works great for CI image) had always been done in PRs because the layers in cache with constraints env invalidated all subsequent layers. This had no big impact before when testing usually took much longer time - but since the testing has been vastly improved in apache#35160, now PROD image building continues running even after test complete and becomes the next frontier of optimization. This PR optimizes PROD image building in two ways: * caching is prepared with "source_providers" constraint mode, same as regular build * the AIRFLOW_CONSTRAINT_MODE and related arguments are moved after installing database clients, so that this parameter does not impact their caching.
potiuk
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Dec 22, 2023
When the DB/NonDB tests were introduced (apache#35160) new test types have been added (separating various Python test types from generic Operator test type). However we have not added matching of the python operator and test files into the right selective unit test type. This caused that when only `operators/python.py` and `tests/test_python` were changed, then `Operators` test type was run but the specific Python * test types were not run. This PR fixes it for current test type (including also separated Serialization test type) and for the future - instead of matching selected test type we match all of them except the few that we now are "special" ("Always, Core, Other, PlainAsserts").
potiuk
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Dec 22, 2023
…#36372) When the DB/NonDB tests were introduced (#35160) new test types have been added (separating various Python test types from generic Operator test type). However we have not added matching of the python operator and test files into the right selective unit test type. This caused that when only `operators/python.py` and `tests/test_python` were changed, then `Operators` test type was run but the specific Python * test types were not run. This PR fixes it for current test type (including also separated Serialization test type) and for the future - instead of matching selected test type we match all of them except the few that we now are "special" ("Always, Core, Other, PlainAsserts").
potiuk
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Dec 30, 2023
…#36372) When the DB/NonDB tests were introduced (#35160) new test types have been added (separating various Python test types from generic Operator test type). However we have not added matching of the python operator and test files into the right selective unit test type. This caused that when only `operators/python.py` and `tests/test_python` were changed, then `Operators` test type was run but the specific Python * test types were not run. This PR fixes it for current test type (including also separated Serialization test type) and for the future - instead of matching selected test type we match all of them except the few that we now are "special" ("Always, Core, Other, PlainAsserts"). (cherry picked from commit b0db1f9)
abhishekbhakat
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Mar 5, 2024
…apache#36372) When the DB/NonDB tests were introduced (apache#35160) new test types have been added (separating various Python test types from generic Operator test type). However we have not added matching of the python operator and test files into the right selective unit test type. This caused that when only `operators/python.py` and `tests/test_python` were changed, then `Operators` test type was run but the specific Python * test types were not run. This PR fixes it for current test type (including also separated Serialization test type) and for the future - instead of matching selected test type we match all of them except the few that we now are "special" ("Always, Core, Other, PlainAsserts").
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This PR marks DB tests as such and allows to split execution
of the tests in CI to run the DB tests with the various database
while the non-db tests - without the DB in a separate run.
In order to do that, the code to select which tests to run has been
moved from
entrypoint_ci.shbash to breeze's Python code, which isgenerally much nicer to maintain and common for both "DB" and
"non-DB" tests.
This will have the nice side effect that it will be easier in the
future to manage different test types and contain some specific
flaky test types.
This change also adds possibility to isolate some of the test types
when parallel DB tests are run and adds new test type
PythonOperator carved out Operator type. This test is best run in
isolation becasue creating and destroing virtualenvs in Docker while
running in parallel to other tests is very slow for some reason and
leads to flaky tests.
Python operator tests are therefore separated out from Operators and
treated separately as isolated tests.
This will help not only with speed but also with stability of the
test suite.
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