Johan LEROY
19ff152445
DAG et BaseOperator viennent d'airflow.sdk, BashOperator du provider standard (airflow.operators.bash n'est plus qu'un alias déprécié). DagBag s'importe depuis airflow.dag_processing et ne prend plus include_examples, les exemples étant déjà coupés par la configuration de conftest.py. Les timetables n'exposent plus summary : la planification se lit sur dag.schedule (None) et timetable.expression (cron normalisé).
142 lines
5.7 KiB
Python
142 lines
5.7 KiB
Python
"""Tests d'integrite des DAGs : s'importent sans erreur, structure attendue. Pas d'execution
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reelle des taches (ca reclamerait le conteneur avec `uv`/`enervision_ml`), juste la definition."""
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from datetime import timedelta
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from pathlib import Path
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import pytest
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from airflow.dag_processing.dagbag import DagBag
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from airflow.sdk import BaseOperator
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DAGS_FOLDER = Path(__file__).resolve().parent.parent / "dags"
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DAG_IDS = ["ml_train", "ml_score", "alertes"]
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TACHES = [
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("ml_train", "train"),
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("ml_score", "score"),
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("alertes", "detection"),
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("alertes", "recommandations"),
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]
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@pytest.fixture(scope="module")
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def dagbag() -> DagBag:
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return DagBag(dag_folder=str(DAGS_FOLDER))
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def test_dags_folder_has_no_import_error(dagbag: DagBag) -> None:
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assert dagbag.import_errors == {}
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def test_every_expected_dag_is_discovered(dagbag: DagBag) -> None:
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assert set(dagbag.dag_ids) == set(DAG_IDS)
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def test_ml_train_has_no_schedule(dagbag: DagBag) -> None:
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assert dagbag.dags["ml_train"].schedule is None
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def test_ml_score_runs_every_hour(dagbag: DagBag) -> None:
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# `@hourly` est un alias Airflow pour ce cron, c'est sous cette forme que la timetable le rend.
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assert dagbag.dags["ml_score"].timetable.expression == "0 * * * *"
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def test_alertes_runs_after_the_hourly_scoring(dagbag: DagBag) -> None:
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# Le decalage n'est pas cosmetique : la regle `anomaly` compare une lecture a la `prediction`
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# du meme instant, que `ml_score` ecrit a l'heure pile.
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assert dagbag.dags["alertes"].timetable.expression == "15 * * * *"
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def test_ml_train_task_calls_the_training_module(dagbag: DagBag) -> None:
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tache = dagbag.dags["ml_train"].get_task("train")
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assert "enervision_ml.train" in tache.bash_command
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def test_ml_score_task_calls_the_scoring_module(dagbag: DagBag) -> None:
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tache = dagbag.dags["ml_score"].get_task("score")
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assert "enervision_ml.score" in tache.bash_command
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def test_alertes_detection_task_calls_the_backend_detection(dagbag: DagBag) -> None:
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tache = dagbag.dags["alertes"].get_task("detection")
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assert "app.detection.internal_alerts" in tache.bash_command
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def test_alertes_recommendation_task_calls_the_backend_cli(dagbag: DagBag) -> None:
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tache = dagbag.dags["alertes"].get_task("recommandations")
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assert "app.cli generate-recommendations" in tache.bash_command
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@pytest.mark.parametrize("task_id", ["detection", "recommandations"])
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def test_alertes_tasks_run_in_the_backend_environment(dagbag: DagBag, task_id: str) -> None:
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# Le backend a son propre venv dans l'image, distinct de celui de ml/ (ADR 0008).
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assert "/opt/backend" in dagbag.dags["alertes"].get_task(task_id).bash_command
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def test_alertes_generates_recommendations_after_detecting(dagbag: DagBag) -> None:
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# `recommendation.alert_id` est une cle etrangere `NOT NULL` : la generation n'a rien a lire
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# tant que la detection n'a pas ecrit.
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assert dagbag.dags["alertes"].get_task("detection").downstream_task_ids == {"recommandations"}
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def test_ml_score_reuses_the_model_path_written_by_ml_train(dagbag: DagBag) -> None:
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entrainement = dagbag.dags["ml_train"].get_task("train").bash_command
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scoring = dagbag.dags["ml_score"].get_task("score").bash_command
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chemin_modele = "/opt/ml/state/models/lightgbm-consumption.txt"
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assert chemin_modele in entrainement
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assert chemin_modele in scoring
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@pytest.mark.parametrize("dag_id", DAG_IDS)
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def test_no_two_runs_of_a_dag_overlap(dagbag: DagBag, dag_id: str) -> None:
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# Deux entrainements ecriraient le meme fichier modele, deux scorings inseriraient en meme
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# temps dans `prediction`, deux detections analyseraient la meme fenetre.
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assert dagbag.dags[dag_id].max_active_runs == 1
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@pytest.mark.parametrize(("dag_id", "task_id"), TACHES)
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def test_every_task_has_an_execution_timeout(dagbag: DagBag, dag_id: str, task_id: str) -> None:
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# Sans plafond, une connexion pendue immobilise un slot du scheduler indefiniment.
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assert dagbag.dags[dag_id].get_task(task_id).execution_timeout is not None
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def test_ml_score_execution_timeout_stays_below_its_hourly_step(dagbag: DagBag) -> None:
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timeout = dagbag.dags["ml_score"].get_task("score").execution_timeout
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assert timeout is not None
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assert timeout < timedelta(hours=1)
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def duree_au_pire(tache: BaseOperator) -> timedelta:
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# `execution_timeout` plafonne une tentative, pas la tache : deux reprises occupent trois
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# plafonds et deux delais d'attente.
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assert tache.execution_timeout is not None
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return (tache.retries + 1) * tache.execution_timeout + tache.retries * tache.retry_delay
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def test_alertes_worst_case_stays_below_its_hourly_step(dagbag: DagBag) -> None:
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# Les deux taches s'enchainent : c'est leur somme, reprises comprises, qui doit tenir dans le
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# pas horaire, sinon `max_active_runs=1` fait attendre l'execution suivante.
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taches = [
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dagbag.dags["alertes"].get_task(task_id) for task_id in ("detection", "recommandations")
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]
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assert sum((duree_au_pire(tache) for tache in taches), timedelta()) < timedelta(hours=1)
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def test_ml_score_retries_after_a_transient_failure(dagbag: DagBag) -> None:
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assert dagbag.dags["ml_score"].get_task("score").retries >= 1
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@pytest.mark.parametrize("task_id", ["detection", "recommandations"])
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def test_alertes_retries_after_a_transient_failure(dagbag: DagBag, task_id: str) -> None:
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# Les deux commandes sont idempotentes en base, une reprise ne duplique rien.
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assert dagbag.dags["alertes"].get_task(task_id).retries >= 1
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@pytest.mark.parametrize(("dag_id", "task_id"), TACHES)
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def test_tasks_never_resync_the_baked_environment(
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dagbag: DagBag, dag_id: str, task_id: str
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) -> None:
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# Sans `--no-sync`, `uv run` reconstruit le projet a chaque execution.
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assert "--no-sync" in dagbag.dags[dag_id].get_task(task_id).bash_command
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