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Similar to f0c3db4 but for the other databricks operator, built on: #69193
Why
With durable execution, on retry the operator inspects the stored run's state to decide whether to reconnect or resubmit. BLOCKED (gated by concurrency/dependencies) and WAITING_FOR_RETRY are in-flight states, but is_job_active didn't list them, so a durable retry treated a run that was merely waiting as not-active and submitted a duplicate. Both states are now recognized as active, so the retry reconnects and waits.
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For significant user-facing changes create newsfragment: {pr_number}.significant.rst, in airflow-core/newsfragments. You can add this file in a follow-up commit after the PR is created so you know the PR number.
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Nice catch, adding BLOCKED and WAITING_FOR_RETRY makes is_job_active exactly the complement of RunState.is_terminal, so every in-flight state reconnects and polls to completion.
Coverage on both the parametrized cases and the end-to-end BLOCKED reconnect looks good.
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Was generative AI tooling used to co-author this PR?
Similar to f0c3db4 but for the other databricks operator, built on: #69193
Why
With durable execution, on retry the operator inspects the stored run's state to decide whether to reconnect or resubmit.
BLOCKED(gated by concurrency/dependencies) andWAITING_FOR_RETRYare in-flight states, butis_job_activedidn't list them, so a durable retry treated a run that was merely waiting as not-active and submitted a duplicate. Both states are now recognized as active, so the retry reconnects and waits.{pr_number}.significant.rst, in airflow-core/newsfragments. You can add this file in a follow-up commit after the PR is created so you know the PR number.