jason810496 opened a new pull request, #70805:
URL: https://github.com/apache/airflow/pull/70805
## Why
Language-SDK subprocess coordinators (Java, Executable, Node) required a
hand-managed filesystem root (`jars_root` / `executables_root` /
`bundles_root`) for compiled artifacts. Users should be able to deploy those
artifacts by existing DagBundle mechanism as well.
## How
- Add an optional `dag_bundle_name` kwarg on the `SubprocessCoordinator`
base, giving three ways to locate artifacts:
- explicit root set: scan that path (unchanged legacy behavior).
- `dag_bundle_name` set: scan that configured bundle's path at its latest
version.
- neither set: scan the task's own bundle, pinned to the run's version
(artifacts co-located with the Python stub Dag).
- Keep the subclass `_build_execute_task_command` signature unchanged:
`execute_task` stashes the task's `bundle_info` for the duration of the call,
and `_scan_roots` resolves the scan root on demand.
- Reuse the Dag-bundle lifecycle through `task_runner.initialize_ti_bundle`
(extracted from `parse`) for resolution.
- Validate at construction that an explicit root and `dag_bundle_name` are
not both set, and that a named bundle is configured via a new lightweight
`DagBundlesManager.is_bundle_configured` that checks configuration without
constructing the bundle instance.
## Notes
- Version semantics are intentional: a co-located bundle (no
`dag_bundle_name`) is pinned to the run's `bundle_info.version` because it is
the same artifact the run was created from, while a separately named artifact
bundle tracks its own latest release independently of any run.
---
##### Was generative AI tooling used to co-author this PR?
- [x] Yes, with help of Claude Code (Opus 4.8) following [the
guidelines](https://github.com/apache/airflow/blob/main/contributing-docs/05_pull_requests.rst#gen-ai-assisted-contributions)
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