uranusjr commented on code in PR #65956:
URL: https://github.com/apache/airflow/pull/65956#discussion_r3159365035


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java-sdk/adr/0003-workload-execution.md:
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+
+# ADR-0003: Workload Execution — Language-Specific Task Execution
+
+## Status
+
+Accepted
+
+## Context
+
+Airflow's standard task runner executes Python callables. To support tasks 
written in other languages, the pipeline needs an extension point where a 
language-specific coordinator can intercept the execution, delegate to an 
external runtime process, and bridge the Task SDK protocol so the external 
process can access Airflow services (connections, variables, XCom) during 
execution.
+
+This ADR details the task execution side of the coordinator architecture 
described in [ADR-0001](0001-java-sdk-airflow-integration.md). It starts with 
the generic model — the abstract contracts and expected behavior that any 
language must implement — then walks through Java as a concrete example.
+
+## Decision
+
+### Extension Point: `BaseCoordinator`
+
+The same `BaseCoordinator` base class that handles DAG parsing also handles 
task execution. It is registered in `provider.yaml` under `coordinators`. For 
task execution, a subclass must implement:
+
+| Method | Signature | Responsibility |
+|---|---|---|
+| `task_execution_cmd` | `(what, dag_rel_path, bundle_info, comm_addr, 
logs_addr) -> list[str]` | Return the full command to launch the language 
runtime for task execution. `comm_addr` and `logs_addr` are `host:port` strings 
the process must connect to. |
+
+The base class provides `run_task_execution()` as a concrete method that 
handles all TCP/process plumbing automatically (same pattern as 
`run_dag_parsing()` for the DAG parsing side).
+
+**Parameters passed to `run_task_execution()`:**
+
+| Parameter | Type | Description |
+|---|---|---|
+| `what` | `TaskInstance` | The task instance to execute (id, dag_id, task_id, 
run_id, try_number, etc.) |
+| `dag_rel_path` | `str \| PathLike` | Relative path to the DAG file / bundle 
within the bundle root |
+| `bundle_info` | `BundleInfo` | Bundle name and version |
+| `startup_details` | `StartupDetails` | Full startup context (task instance, 
DAG rel path, bundle info, run context, start date) — already consumed from fd 
0 |
+
+### Registration
+
+The same `coordinators` entry in `provider.yaml` covers both DAG parsing and 
task execution — no separate registration needed:
+
+```yaml
+coordinators:
+  - airflow.providers.sdk.<lang>.coordinator.<LangCoordinator>
+```
+
+### Discovery: `_resolve_runtime_entrypoint()`
+
+When `task_runner.main()` starts, before any Python task execution:
+
+```
+task_runner.main()

Review Comment:
   Since we use the existing executor mechanism in Airflow, the idea of a 
worker varies depending on the executor in use. LocalExecutor for example does 
not really have a long-running worker concept; CeleryExecutor would require you 
to have at least the same number of worker processes as you want parallelism.
   
   I think another way to put it is this design does not really go into how 
workers are managed. It simply assumes you have JRE set up to run Java, the 
same way as Python and needed provider packages are assumed when you run Python 
tasks.
   
   (This brings up the interesting case of Kubernetes, where we can have 
different images for different languages, but currently the design requires you 
to have an image that can handle both. This is a possible future improvement 
work.)



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