fix(etl): fiabilise airflow-init, borne les DAGs ML et ajoute la CI Airflow

etl/airflow/tests/test_dags.py

@@ -1,6 +1,7 @@
 """Tests d'integrite des DAGs : s'importent sans erreur, structure attendue. Pas d'execution
 reelle des taches (ca reclamerait le conteneur avec `uv`/`enervision_ml`), juste la definition."""
 
+from datetime import timedelta
 from pathlib import Path
 
 import pytest
@@ -22,14 +23,12 @@ def test_every_expected_dag_is_discovered(dagbag: DagBag) -> None:
     assert set(dagbag.dag_ids) == {"ml_train", "ml_score"}
 
 
-def test_ml_train_has_no_schedule() -> None:
-    dagbag = DagBag(dag_folder=str(DAGS_FOLDER), include_examples=False)
+def test_ml_train_has_no_schedule(dagbag: DagBag) -> None:
     assert dagbag.dags["ml_train"].timetable.summary == "None"
 
 
-def test_ml_score_runs_every_hour() -> None:
+def test_ml_score_runs_every_hour(dagbag: DagBag) -> None:
     # `@hourly` est un alias Airflow pour ce cron, c'est sous cette forme que `.summary` le rend.
-    dagbag = DagBag(dag_folder=str(DAGS_FOLDER), include_examples=False)
     assert dagbag.dags["ml_score"].timetable.summary == "0 * * * *"
 
 
@@ -50,3 +49,34 @@ def test_ml_score_reuses_the_model_path_written_by_ml_train(dagbag: DagBag) -> N
 
     assert chemin_modele in entrainement
     assert chemin_modele in scoring
+
+
+@pytest.mark.parametrize("dag_id", ["ml_train", "ml_score"])
+def test_no_two_runs_of_a_dag_overlap(dagbag: DagBag, dag_id: str) -> None:
+    # Deux entrainements ecriraient le meme fichier modele, deux scorings inseriraient en meme
+    # temps dans `prediction`.
+    assert dagbag.dags[dag_id].max_active_runs == 1
+
+
+@pytest.mark.parametrize(("dag_id", "task_id"), [("ml_train", "train"), ("ml_score", "score")])
+def test_every_task_has_an_execution_timeout(dagbag: DagBag, dag_id: str, task_id: str) -> None:
+    # Sans plafond, une connexion pendue immobilise un slot du scheduler indefiniment.
+    assert dagbag.dags[dag_id].get_task(task_id).execution_timeout is not None
+
+
+def test_ml_score_execution_timeout_stays_below_its_hourly_step(dagbag: DagBag) -> None:
+    timeout = dagbag.dags["ml_score"].get_task("score").execution_timeout
+    assert timeout is not None
+    assert timeout < timedelta(hours=1)
+
+
+def test_ml_score_retries_after_a_transient_failure(dagbag: DagBag) -> None:
+    assert dagbag.dags["ml_score"].get_task("score").retries >= 1
+
+
+@pytest.mark.parametrize(("dag_id", "task_id"), [("ml_train", "train"), ("ml_score", "score")])
+def test_tasks_never_resync_the_baked_environment(
+    dagbag: DagBag, dag_id: str, task_id: str
+) -> None:
+    # Sans `--no-sync`, `uv run` reconstruit `enervision-ml` a chaque execution.
+    assert "--no-sync" in dagbag.dags[dag_id].get_task(task_id).bash_command