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#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations
import datetime
from collections import defaultdict
from collections.abc import Mapping
from functools import reduce
from typing import TYPE_CHECKING
from unittest import mock
from unittest.mock import call
import pendulum
import pytest
from sqlalchemy import select
from sqlalchemy.orm import joinedload
from airflow import settings
from airflow._shared.timezones import timezone
from airflow.callbacks.callback_requests import DagCallbackRequest, DagRunContext
from airflow.models.dag import DagModel, infer_automated_data_interval
from airflow.models.dag_version import DagVersion
from airflow.models.dagrun import DagRun, DagRunNote
from airflow.models.serialized_dag import SerializedDagModel
from airflow.models.taskinstance import TaskInstance, TaskInstanceNote, clear_task_instances
from airflow.models.taskmap import TaskMap
from airflow.models.taskreschedule import TaskReschedule
from airflow.providers.standard.operators.bash import BashOperator
from airflow.providers.standard.operators.empty import EmptyOperator
from airflow.providers.standard.operators.python import PythonOperator, ShortCircuitOperator
from airflow.sdk import DAG, BaseOperator, get_current_context, setup, task, task_group, teardown
from airflow.sdk.definitions.deadline import AsyncCallback, DeadlineAlert, DeadlineReference
from airflow.serialization.serialized_objects import LazyDeserializedDAG, SerializedDAG
from airflow.stats import Stats
from airflow.task.trigger_rule import TriggerRule
from airflow.triggers.base import StartTriggerArgs
from airflow.utils.span_status import SpanStatus
from airflow.utils.state import DagRunState, State, TaskInstanceState
from airflow.utils.thread_safe_dict import ThreadSafeDict
from airflow.utils.types import DagRunTriggeredByType, DagRunType
from tests_common.test_utils import db
from tests_common.test_utils.config import conf_vars
from tests_common.test_utils.dag import sync_dag_to_db
from tests_common.test_utils.mock_operators import MockOperator
from unit.models import DEFAULT_DATE as _DEFAULT_DATE
pytestmark = [pytest.mark.db_test, pytest.mark.need_serialized_dag]
if TYPE_CHECKING:
from sqlalchemy.orm.session import Session
TI = TaskInstance
DEFAULT_DATE = pendulum.instance(_DEFAULT_DATE)
async def empty_callback_for_deadline():
"""Used in a number of tests to confirm that Deadlines and DeadlineAlerts function correctly."""
pass
@pytest.fixture(scope="module")
def dagbag():
from airflow.dag_processing.dagbag import DagBag
return DagBag(include_examples=True)
class TestDagRun:
@pytest.fixture(autouse=True)
def setup_test_cases(self):
self._clean_db()
yield
self._clean_db()
@staticmethod
def _clean_db():
db.clear_db_runs()
db.clear_db_pools()
db.clear_db_dags()
db.clear_db_dag_bundles()
db.clear_db_variables()
db.clear_db_assets()
db.clear_db_xcom()
db.clear_db_dags()
@staticmethod
def create_dag_run(
dag: SerializedDAG,
*,
task_states: Mapping[str, TaskInstanceState] | None = None,
logical_date: datetime.datetime | None = None,
is_backfill: bool = False,
state: DagRunState = DagRunState.RUNNING,
session: Session,
):
now = timezone.utcnow()
logical_date = pendulum.instance(logical_date or now)
if is_backfill:
run_type = DagRunType.BACKFILL_JOB
data_interval = infer_automated_data_interval(dag.timetable, logical_date)
else:
run_type = DagRunType.MANUAL
data_interval = dag.timetable.infer_manual_data_interval(run_after=logical_date)
dag_run = dag.create_dagrun(
run_id=dag.timetable.generate_run_id(
run_type=run_type,
run_after=logical_date,
data_interval=data_interval,
),
run_type=run_type,
logical_date=logical_date,
data_interval=data_interval,
run_after=data_interval.end,
start_date=now,
state=state,
triggered_by=DagRunTriggeredByType.TEST,
session=session,
)
if task_states is not None:
for task_id, task_state in task_states.items():
ti = dag_run.get_task_instance(task_id)
if TYPE_CHECKING:
assert ti
ti.set_state(task_state, session)
session.flush()
return dag_run
def test_clear_task_instances_for_backfill_running_dagrun(self, dag_maker, session):
now = timezone.utcnow()
state = DagRunState.RUNNING
dag_id = "test_clear_task_instances_for_backfill_running_dagrun"
with dag_maker(dag_id=dag_id) as dag:
EmptyOperator(task_id="backfill_task_0")
self.create_dag_run(dag, logical_date=now, is_backfill=True, state=state, session=session)
qry = session.query(TI).filter(TI.dag_id == dag.dag_id).all()
clear_task_instances(qry, session)
session.flush()
dr0 = session.query(DagRun).filter(DagRun.dag_id == dag_id, DagRun.logical_date == now).first()
assert dr0.state == state
assert dr0.clear_number < 1
@pytest.mark.parametrize("state", [DagRunState.SUCCESS, DagRunState.FAILED])
def test_clear_task_instances_for_backfill_finished_dagrun(self, dag_maker, state, session):
now = timezone.utcnow()
dag_id = "test_clear_task_instances_for_backfill_finished_dagrun"
with dag_maker(dag_id=dag_id) as dag:
EmptyOperator(task_id="backfill_task_0")
self.create_dag_run(dag, logical_date=now, is_backfill=True, state=state, session=session)
qry = session.query(TI).filter(TI.dag_id == dag.dag_id).all()
clear_task_instances(qry, session)
session.flush()
dr0 = session.query(DagRun).filter(DagRun.dag_id == dag_id, DagRun.logical_date == now).first()
assert dr0.state == DagRunState.QUEUED
assert dr0.clear_number == 1
def test_dagrun_find(self, session):
now = timezone.utcnow()
dag_id1 = "test_dagrun_find_externally_triggered"
dag_run = DagRun(
dag_id=dag_id1,
run_id=dag_id1,
run_type=DagRunType.MANUAL,
logical_date=now,
start_date=now,
state=DagRunState.RUNNING,
)
session.add(dag_run)
dag_id2 = "test_dagrun_find_not_externally_triggered"
dag_run = DagRun(
dag_id=dag_id2,
run_id=dag_id2,
run_type=DagRunType.SCHEDULED,
logical_date=now,
start_date=now,
state=DagRunState.RUNNING,
)
session.add(dag_run)
session.commit()
assert len(DagRun.find(dag_id=dag_id1, run_type=DagRunType.MANUAL)) == 1
assert len(DagRun.find(run_id=dag_id1)) == 1
assert len(DagRun.find(run_id=[dag_id1, dag_id2])) == 2
assert len(DagRun.find(logical_date=[now, now])) == 2
assert len(DagRun.find(logical_date=now)) == 2
assert len(DagRun.find(dag_id=dag_id1, run_type=DagRunType.SCHEDULED)) == 0
assert len(DagRun.find(dag_id=dag_id2, run_type=DagRunType.MANUAL)) == 0
assert len(DagRun.find(dag_id=dag_id2)) == 1
def test_dagrun_find_duplicate(self, session):
now = timezone.utcnow()
dag_id = "test_dagrun_find_duplicate"
dag_run = DagRun(
dag_id=dag_id,
run_id=dag_id,
run_type=DagRunType.MANUAL,
logical_date=now,
start_date=now,
state=DagRunState.RUNNING,
)
session.add(dag_run)
session.commit()
assert DagRun.find_duplicate(dag_id=dag_id, run_id=dag_id) is not None
assert DagRun.find_duplicate(dag_id=dag_id, run_id=dag_id) is not None
assert DagRun.find_duplicate(dag_id=dag_id, run_id=None) is None
def test_dagrun_success_when_all_skipped(self, dag_maker, session):
"""
Tests that a DAG run succeeds when all tasks are skipped
"""
with dag_maker(
dag_id="test_dagrun_success_when_all_skipped",
schedule=datetime.timedelta(days=1),
start_date=timezone.datetime(2017, 1, 1),
) as dag:
dag_task1 = ShortCircuitOperator(task_id="test_short_circuit_false", python_callable=bool)
dag_task2 = EmptyOperator(task_id="test_state_skipped1")
dag_task3 = EmptyOperator(task_id="test_state_skipped2")
dag_task1.set_downstream(dag_task2)
dag_task2.set_downstream(dag_task3)
initial_task_states = {
"test_short_circuit_false": TaskInstanceState.SUCCESS,
"test_state_skipped1": TaskInstanceState.SKIPPED,
"test_state_skipped2": TaskInstanceState.SKIPPED,
}
dag_run = self.create_dag_run(dag=dag, task_states=initial_task_states, session=session)
dag_run.update_state()
assert dag_run.state == DagRunState.SUCCESS
def test_dagrun_not_stuck_in_running_when_all_tasks_instances_are_removed(self, dag_maker, session):
"""
Tests that a DAG run succeeds when all tasks are removed
"""
with dag_maker(
dag_id="test_dagrun_success_when_all_skipped",
schedule=datetime.timedelta(days=1),
start_date=timezone.datetime(2017, 1, 1),
) as dag:
dag_task1 = ShortCircuitOperator(task_id="test_short_circuit_false", python_callable=bool)
dag_task2 = EmptyOperator(task_id="test_state_skipped1")
dag_task3 = EmptyOperator(task_id="test_state_skipped2")
dag_task1.set_downstream(dag_task2)
dag_task2.set_downstream(dag_task3)
initial_task_states = {
"test_short_circuit_false": TaskInstanceState.REMOVED,
"test_state_skipped1": TaskInstanceState.REMOVED,
"test_state_skipped2": TaskInstanceState.REMOVED,
}
dag_run = self.create_dag_run(dag=dag, task_states=initial_task_states, session=session)
dag_run.update_state()
assert dag_run.state == DagRunState.SUCCESS
def test_dagrun_success_conditions(self, dag_maker, session):
# A -> B
# A -> C -> D
# ordered: B, D, C, A or D, B, C, A or D, C, B, A
with dag_maker(schedule=datetime.timedelta(days=1), session=session):
op1 = EmptyOperator(task_id="A")
op2 = EmptyOperator(task_id="B")
op3 = EmptyOperator(task_id="C")
op4 = EmptyOperator(task_id="D")
op1.set_upstream([op2, op3])
op3.set_upstream(op4)
dr = dag_maker.create_dagrun()
# op1 = root
ti_op1 = dr.get_task_instance(task_id=op1.task_id)
ti_op1.set_state(state=TaskInstanceState.SUCCESS, session=session)
ti_op2 = dr.get_task_instance(task_id=op2.task_id)
ti_op3 = dr.get_task_instance(task_id=op3.task_id)
ti_op4 = dr.get_task_instance(task_id=op4.task_id)
# root is successful, but unfinished tasks
dr.update_state()
assert dr.state == DagRunState.RUNNING
# one has failed, but root is successful
ti_op2.set_state(state=TaskInstanceState.FAILED, session=session)
ti_op3.set_state(state=TaskInstanceState.SUCCESS, session=session)
ti_op4.set_state(state=TaskInstanceState.SUCCESS, session=session)
dr.update_state()
assert dr.state == DagRunState.SUCCESS
def test_dagrun_deadlock(self, dag_maker, session):
with dag_maker(schedule=datetime.timedelta(days=1), session=session):
op1 = EmptyOperator(task_id="A")
op2 = EmptyOperator(task_id="B")
op2.trigger_rule = TriggerRule.ONE_FAILED
op2.set_upstream(op1)
dr = dag_maker.create_dagrun()
ti_op1: TI = dr.get_task_instance(task_id=op1.task_id, session=session)
ti_op2: TI = dr.get_task_instance(task_id=op2.task_id, session=session)
ti_op1.set_state(state=TaskInstanceState.SUCCESS, session=session)
ti_op2.set_state(state=None, session=session)
dr.update_state(session=session)
assert dr.state == DagRunState.RUNNING
ti_op2.set_state(state=None, session=session)
ti_op2.task.trigger_rule = "invalid"
dr.update_state(session=session)
assert dr.state == DagRunState.FAILED
def test_dagrun_no_deadlock_with_restarting(self, dag_maker, session):
with dag_maker(schedule=datetime.timedelta(days=1)):
op1 = EmptyOperator(task_id="upstream_task")
op2 = EmptyOperator(task_id="downstream_task")
op2.set_upstream(op1)
dr = dag_maker.create_dagrun()
upstream_ti = dr.get_task_instance(task_id="upstream_task")
upstream_ti.set_state(TaskInstanceState.RESTARTING, session=session)
dr.update_state()
assert dr.state == DagRunState.RUNNING
def test_dagrun_no_deadlock_with_depends_on_past(self, dag_maker, session):
with dag_maker(schedule=datetime.timedelta(days=1)):
EmptyOperator(task_id="dop", depends_on_past=True)
EmptyOperator(task_id="tc", max_active_tis_per_dag=1)
dr = dag_maker.create_dagrun(
run_id="test_dagrun_no_deadlock_1",
run_type=DagRunType.SCHEDULED,
start_date=DEFAULT_DATE,
)
next_date = DEFAULT_DATE + datetime.timedelta(days=1)
dr2 = dag_maker.create_dagrun(
run_id="test_dagrun_no_deadlock_2",
start_date=DEFAULT_DATE + datetime.timedelta(days=1),
logical_date=next_date,
)
ti1_op1 = dr.get_task_instance(task_id="dop")
dr2.get_task_instance(task_id="dop")
ti2_op1 = dr.get_task_instance(task_id="tc")
dr.get_task_instance(task_id="tc")
ti1_op1.set_state(state=TaskInstanceState.RUNNING, session=session)
dr.update_state()
dr2.update_state()
assert dr.state == DagRunState.RUNNING
assert dr2.state == DagRunState.RUNNING
ti2_op1.set_state(state=TaskInstanceState.RUNNING, session=session)
dr.update_state()
dr2.update_state()
assert dr.state == DagRunState.RUNNING
assert dr2.state == DagRunState.RUNNING
def test_dagrun_success_callback(self, dag_maker, session):
def on_success_callable(context):
assert context["dag_run"].dag_id == "test_dagrun_success_callback"
with dag_maker(
dag_id="test_dagrun_success_callback",
schedule=datetime.timedelta(days=1),
start_date=datetime.datetime(2017, 1, 1),
on_success_callback=on_success_callable,
) as dag:
dag_task1 = EmptyOperator(task_id="test_state_succeeded1")
dag_task2 = EmptyOperator(task_id="test_state_succeeded2")
dag_task1.set_downstream(dag_task2)
initial_task_states = {
"test_state_succeeded1": TaskInstanceState.SUCCESS,
"test_state_succeeded2": TaskInstanceState.SUCCESS,
}
dag_run = self.create_dag_run(dag=dag, task_states=initial_task_states, session=session)
with mock.patch.object(dag_run, "handle_dag_callback") as handle_dag_callback:
_, callback = dag_run.update_state()
assert handle_dag_callback.mock_calls == [mock.call(dag=dag, success=True, reason="success")]
assert dag_run.state == DagRunState.SUCCESS
# Callbacks are not added until handle_callback = False is passed to dag_run.update_state()
assert callback is None
def test_dagrun_failure_callback(self, dag_maker, session):
def on_failure_callable(context):
assert context["dag_run"].dag_id == "test_dagrun_failure_callback"
with dag_maker(
dag_id="test_dagrun_failure_callback",
schedule=datetime.timedelta(days=1),
start_date=datetime.datetime(2017, 1, 1),
on_failure_callback=on_failure_callable,
) as dag:
dag_task1 = EmptyOperator(task_id="test_state_succeeded1")
dag_task2 = EmptyOperator(task_id="test_state_failed2")
initial_task_states = {
"test_state_succeeded1": TaskInstanceState.SUCCESS,
"test_state_failed2": TaskInstanceState.FAILED,
}
dag_task1.set_downstream(dag_task2)
dag_run = self.create_dag_run(dag=dag, task_states=initial_task_states, session=session)
with mock.patch.object(dag_run, "handle_dag_callback") as handle_dag_callback:
_, callback = dag_run.update_state()
assert handle_dag_callback.mock_calls == [mock.call(dag=dag, success=False, reason="task_failure")]
assert dag_run.state == DagRunState.FAILED
# Callbacks are not added until handle_callback = False is passed to dag_run.update_state()
assert callback is None
def test_on_success_callback_when_task_skipped(self, session, testing_dag_bundle):
mock_on_success = mock.MagicMock()
mock_on_success.__name__ = "mock_on_success"
dag = DAG(
dag_id="test_dagrun_update_state_with_handle_callback_success",
start_date=datetime.datetime(2017, 1, 1),
on_success_callback=mock_on_success,
schedule=datetime.timedelta(days=1),
)
_ = EmptyOperator(task_id="test_state_succeeded1", dag=dag)
# Create DagModel directly with bundle_name
dag_model = DagModel(
dag_id=dag.dag_id,
bundle_name="testing",
)
session.merge(dag_model)
session.flush()
scheduler_dag = sync_dag_to_db(dag, session=session)
scheduler_dag.on_success_callback = mock_on_success
initial_task_states = {
"test_state_succeeded1": TaskInstanceState.SKIPPED,
}
dag_run = self.create_dag_run(scheduler_dag, task_states=initial_task_states, session=session)
_, _ = dag_run.update_state(execute_callbacks=True)
task = dag_run.get_task_instances()[0]
assert task.state == TaskInstanceState.SKIPPED
assert dag_run.state == DagRunState.SUCCESS
mock_on_success.assert_called_once()
def test_start_dr_spans_if_needed_new_span(self, dag_maker, session):
with dag_maker(
dag_id="test_start_dr_spans_if_needed_new_span",
schedule=datetime.timedelta(days=1),
start_date=datetime.datetime(2017, 1, 1),
) as dag:
dag_task1 = EmptyOperator(task_id="test_task1")
dag_task2 = EmptyOperator(task_id="test_task2")
dag_task1.set_downstream(dag_task2)
initial_task_states = {
"test_task1": TaskInstanceState.QUEUED,
"test_task2": TaskInstanceState.QUEUED,
}
dag_run = self.create_dag_run(dag=dag, task_states=initial_task_states, session=session)
active_spans = ThreadSafeDict()
dag_run.set_active_spans(active_spans)
tis = dag_run.get_task_instances()
assert dag_run.active_spans is not None
assert dag_run.active_spans.get("dr:" + str(dag_run.id)) is None
assert dag_run.span_status == SpanStatus.NOT_STARTED
dag_run.start_dr_spans_if_needed(tis=tis)
assert dag_run.span_status == SpanStatus.ACTIVE
assert dag_run.active_spans.get("dr:" + str(dag_run.id)) is not None
def test_start_dr_spans_if_needed_span_with_continuance(self, dag_maker, session):
with dag_maker(
dag_id="test_start_dr_spans_if_needed_span_with_continuance",
schedule=datetime.timedelta(days=1),
start_date=datetime.datetime(2017, 1, 1),
) as dag:
dag_task1 = EmptyOperator(task_id="test_task1")
dag_task2 = EmptyOperator(task_id="test_task2")
dag_task1.set_downstream(dag_task2)
initial_task_states = {
"test_task1": TaskInstanceState.RUNNING,
"test_task2": TaskInstanceState.QUEUED,
}
dag_run = self.create_dag_run(dag=dag, task_states=initial_task_states, session=session)
active_spans = ThreadSafeDict()
dag_run.set_active_spans(active_spans)
dag_run.span_status = SpanStatus.NEEDS_CONTINUANCE
tis = dag_run.get_task_instances()
first_ti = tis[0]
first_ti.span_status = SpanStatus.NEEDS_CONTINUANCE
assert dag_run.active_spans is not None
assert dag_run.active_spans.get("dr:" + str(dag_run.id)) is None
assert dag_run.active_spans.get("ti:" + first_ti.id) is None
assert dag_run.span_status == SpanStatus.NEEDS_CONTINUANCE
assert first_ti.span_status == SpanStatus.NEEDS_CONTINUANCE
dag_run.start_dr_spans_if_needed(tis=tis)
assert dag_run.span_status == SpanStatus.ACTIVE
assert first_ti.span_status == SpanStatus.ACTIVE
assert dag_run.active_spans.get("dr:" + str(dag_run.id)) is not None
assert dag_run.active_spans.get("ti:" + first_ti.id) is not None
def test_end_dr_span_if_needed(self, testing_dag_bundle, dag_maker, session):
with dag_maker(
dag_id="test_end_dr_span_if_needed",
schedule=datetime.timedelta(days=1),
start_date=datetime.datetime(2017, 1, 1),
) as dag:
dag_task1 = EmptyOperator(task_id="test_task1")
dag_task2 = EmptyOperator(task_id="test_task2")
dag_task1.set_downstream(dag_task2)
initial_task_states = {
"test_task1": TaskInstanceState.SUCCESS,
"test_task2": TaskInstanceState.SUCCESS,
}
dag_run = self.create_dag_run(dag=dag, task_states=initial_task_states, session=session)
active_spans = ThreadSafeDict()
dag_run.set_active_spans(active_spans)
from airflow.traces.tracer import Trace
dr_span = Trace.start_root_span(span_name="test_span", start_as_current=False)
active_spans.set("dr:" + str(dag_run.id), dr_span)
assert dag_run.active_spans is not None
assert dag_run.active_spans.get("dr:" + str(dag_run.id)) is not None
dag_run.end_dr_span_if_needed()
assert dag_run.span_status == SpanStatus.ENDED
assert dag_run.active_spans.get("dr:" + str(dag_run.id)) is None
def test_end_dr_span_if_needed_with_span_from_another_scheduler(
self, testing_dag_bundle, dag_maker, session
):
with dag_maker(
dag_id="test_end_dr_span_if_needed_with_span_from_another_scheduler",
schedule=datetime.timedelta(days=1),
start_date=datetime.datetime(2017, 1, 1),
) as dag:
dag_task1 = EmptyOperator(task_id="test_task1")
dag_task2 = EmptyOperator(task_id="test_task2")
dag_task1.set_downstream(dag_task2)
initial_task_states = {
"test_task1": TaskInstanceState.SUCCESS,
"test_task2": TaskInstanceState.SUCCESS,
}
dag_run = self.create_dag_run(dag=dag, task_states=initial_task_states, session=session)
active_spans = ThreadSafeDict()
dag_run.set_active_spans(active_spans)
dag_run.span_status = SpanStatus.ACTIVE
assert dag_run.active_spans is not None
assert dag_run.active_spans.get("dr:" + str(dag_run.id)) is None
dag_run.end_dr_span_if_needed()
assert dag_run.span_status == SpanStatus.SHOULD_END
def test_dagrun_update_state_with_handle_callback_success(self, testing_dag_bundle, dag_maker, session):
def on_success_callable(context):
assert context["dag_run"].dag_id == "test_dagrun_update_state_with_handle_callback_success"
relative_fileloc = "test_dagrun_update_state_with_handle_callback_success.py"
with dag_maker(
dag_id="test_dagrun_update_state_with_handle_callback_success",
schedule=datetime.timedelta(days=1),
start_date=datetime.datetime(2017, 1, 1),
on_success_callback=on_success_callable,
) as dag:
dag_task1 = EmptyOperator(task_id="test_state_succeeded1")
dag_task2 = EmptyOperator(task_id="test_state_succeeded2")
dag_task1.set_downstream(dag_task2)
dm = DagModel.get_dagmodel(dag.dag_id, session=session)
dm.relative_fileloc = relative_fileloc
session.merge(dm)
session.commit()
initial_task_states = {
"test_state_succeeded1": TaskInstanceState.SUCCESS,
"test_state_succeeded2": TaskInstanceState.SUCCESS,
}
dag.relative_fileloc = relative_fileloc
SerializedDagModel.write_dag(LazyDeserializedDAG.from_dag(dag), bundle_name="dag_maker")
session.commit()
dag_run = self.create_dag_run(dag=dag, task_states=initial_task_states, session=session)
dag_run.dag_model = dm
_, callback = dag_run.update_state(execute_callbacks=False)
assert dag_run.state == DagRunState.SUCCESS
# Callbacks are not added until handle_callback = False is passed to dag_run.update_state()
assert callback == DagCallbackRequest(
filepath=dag_run.dag.relative_fileloc,
dag_id="test_dagrun_update_state_with_handle_callback_success",
run_id=dag_run.run_id,
is_failure_callback=False,
bundle_name="dag_maker",
bundle_version=None,
context_from_server=DagRunContext(
dag_run=dag_run,
last_ti=dag_run.get_last_ti(dag, session),
),
msg="success",
)
def test_dagrun_update_state_with_handle_callback_failure(self, testing_dag_bundle, dag_maker, session):
def on_failure_callable(context):
assert context["dag_run"].dag_id == "test_dagrun_update_state_with_handle_callback_failure"
relative_fileloc = "test_dagrun_update_state_with_handle_callback_failure.py"
with dag_maker(
dag_id="test_dagrun_update_state_with_handle_callback_failure",
schedule=datetime.timedelta(days=1),
start_date=datetime.datetime(2017, 1, 1),
on_failure_callback=on_failure_callable,
) as dag:
dag_task1 = EmptyOperator(task_id="test_state_succeeded1")
dag_task2 = EmptyOperator(task_id="test_state_failed2")
dag_task1.set_downstream(dag_task2)
dm = DagModel.get_dagmodel(dag.dag_id, session=session)
dm.relative_fileloc = relative_fileloc
session.merge(dm)
session.commit()
initial_task_states = {
"test_state_succeeded1": TaskInstanceState.SUCCESS,
"test_state_failed2": TaskInstanceState.FAILED,
}
dag.relative_fileloc = relative_fileloc
SerializedDagModel.write_dag(LazyDeserializedDAG.from_dag(dag), bundle_name="dag_maker")
session.commit()
dag_run = self.create_dag_run(dag=dag, task_states=initial_task_states, session=session)
dag_run.dag_model = dm
_, callback = dag_run.update_state(execute_callbacks=False)
assert dag_run.state == DagRunState.FAILED
# Callbacks are not added until handle_callback = False is passed to dag_run.update_state()
assert callback == DagCallbackRequest(
filepath=dag.relative_fileloc,
dag_id="test_dagrun_update_state_with_handle_callback_failure",
run_id=dag_run.run_id,
is_failure_callback=True,
msg="task_failure",
bundle_name="dag_maker",
bundle_version=None,
context_from_server=DagRunContext(
dag_run=dag_run,
last_ti=dag_run.get_last_ti(dag, session),
),
)
def test_dagrun_set_state_end_date(self, dag_maker, session):
with dag_maker(schedule=datetime.timedelta(days=1), start_date=DEFAULT_DATE):
pass
dr = dag_maker.create_dagrun()
# Initial end_date should be NULL
# DagRunState.SUCCESS and DagRunState.FAILED are all ending state and should set end_date
# DagRunState.RUNNING set end_date back to NULL
session.add(dr)
session.commit()
assert dr.end_date is None
dr.set_state(DagRunState.SUCCESS)
session.merge(dr)
session.commit()
dr_database = session.query(DagRun).filter(DagRun.run_id == dr.run_id).one()
assert dr_database.end_date is not None
assert dr.end_date == dr_database.end_date
dr.set_state(DagRunState.RUNNING)
session.merge(dr)
session.commit()
dr_database = session.query(DagRun).filter(DagRun.run_id == dr.run_id).one()
assert dr_database.end_date is None
dr.set_state(DagRunState.FAILED)
session.merge(dr)
session.commit()
dr_database = session.query(DagRun).filter(DagRun.run_id == dr.run_id).one()
assert dr_database.end_date is not None
assert dr.end_date == dr_database.end_date
def test_dagrun_update_state_end_date(self, dag_maker, session):
# A -> B
with dag_maker(schedule=datetime.timedelta(days=1)):
op1 = EmptyOperator(task_id="A")
op2 = EmptyOperator(task_id="B")
op1.set_upstream(op2)
dr = dag_maker.create_dagrun()
# Initial end_date should be NULL
# DagRunState.SUCCESS and DagRunState.FAILED are all ending state and should set end_date
# DagRunState.RUNNING set end_date back to NULL
session.merge(dr)
session.commit()
assert dr.end_date is None
ti_op1 = dr.get_task_instance(task_id=op1.task_id)
ti_op1.set_state(state=TaskInstanceState.SUCCESS, session=session)
ti_op2 = dr.get_task_instance(task_id=op2.task_id)
ti_op2.set_state(state=TaskInstanceState.SUCCESS, session=session)
dr.update_state()
dr_database = session.query(DagRun).filter(DagRun.run_id == dr.run_id).one()
assert dr_database.end_date is not None
assert dr.end_date == dr_database.end_date
ti_op1.set_state(state=TaskInstanceState.RUNNING, session=session)
ti_op2.set_state(state=TaskInstanceState.RUNNING, session=session)
dr.update_state()
dr_database = session.query(DagRun).filter(DagRun.run_id == dr.run_id).one()
assert dr._state == DagRunState.RUNNING
assert dr.end_date is None
assert dr_database.end_date is None
ti_op1.set_state(state=TaskInstanceState.FAILED, session=session)
ti_op2.set_state(state=TaskInstanceState.FAILED, session=session)
dr.update_state()
dr_database = session.query(DagRun).filter(DagRun.run_id == dr.run_id).one()
assert dr_database.end_date is not None
assert dr.end_date == dr_database.end_date
def test_get_task_instance_on_empty_dagrun(self, dag_maker, session):
"""
Make sure that a proper value is returned when a dagrun has no task instances
"""
with dag_maker(
dag_id="test_get_task_instance_on_empty_dagrun",
schedule=datetime.timedelta(days=1),
start_date=timezone.datetime(2017, 1, 1),
) as dag:
ShortCircuitOperator(task_id="test_short_circuit_false", python_callable=lambda: False)
now = timezone.utcnow()
# Don't use create_dagrun since it will create the task instances too which we
# don't want
dag_run = DagRun(
dag_id=dag.dag_id,
run_id="test_get_task_instance_on_empty_dagrun",
run_type=DagRunType.MANUAL,
logical_date=now,
start_date=now,
state=DagRunState.RUNNING,
)
session.add(dag_run)
session.commit()
ti = dag_run.get_task_instance("test_short_circuit_false")
assert ti is None
def test_get_latest_runs(self, dag_maker, session):
with dag_maker(
dag_id="test_latest_runs_1", schedule=datetime.timedelta(days=1), start_date=DEFAULT_DATE
) as dag:
...
self.create_dag_run(dag, logical_date=timezone.datetime(2015, 1, 1), session=session)
self.create_dag_run(dag, logical_date=timezone.datetime(2015, 1, 2), session=session)
dagruns = DagRun.get_latest_runs(session)
session.close()
for dagrun in dagruns:
if dagrun.dag_id == "test_latest_runs_1":
assert dagrun.logical_date == timezone.datetime(2015, 1, 2)
def test_removed_task_instances_can_be_restored(self, dag_maker, session):
def create_dag():
return dag_maker(
dag_id="test_task_restoration",
schedule=datetime.timedelta(days=1),
start_date=DEFAULT_DATE,
)
with create_dag() as dag:
EmptyOperator(task_id="flaky_task", owner="test")
dagrun = self.create_dag_run(dag, session=session)
flaky_ti = dagrun.get_task_instances()[0]
assert flaky_ti.task_id == "flaky_task"
assert flaky_ti.state is None
with create_dag() as dag:
pass
dagrun.dag = dag
dag_version_id = DagVersion.get_latest_version(dag.dag_id, session=session).id
dagrun.verify_integrity(dag_version_id=dag_version_id)
flaky_ti.refresh_from_db()
assert flaky_ti.state is None
with create_dag() as dag:
EmptyOperator(task_id="flaky_task", owner="test")
dagrun.verify_integrity(dag_version_id=dag_version_id)
flaky_ti.refresh_from_db()
assert flaky_ti.state is None
def test_already_added_task_instances_can_be_ignored(self, dag_maker, session):
with dag_maker("triggered_dag", schedule=datetime.timedelta(days=1), start_date=DEFAULT_DATE) as dag:
...
dag.add_task(EmptyOperator(task_id="first_task", owner="test"))
dagrun = self.create_dag_run(dag, session=session)
first_ti = dagrun.get_task_instances()[0]
assert first_ti.task_id == "first_task"
assert first_ti.state is None
# Lets assume that the above TI was added into DB by webserver, but if scheduler
# is running the same method at the same time it would find 0 TIs for this dag
# and proceeds further to create TIs. Hence mocking DagRun.get_task_instances
# method to return an empty list of TIs.
with mock.patch.object(DagRun, "get_task_instances") as mock_gtis:
mock_gtis.return_value = []
dagrun.verify_integrity(
dag_version_id=DagVersion.get_latest_version(dag.dag_id, session=session).id
)
first_ti.refresh_from_db()
assert first_ti.state is None
@pytest.mark.parametrize("state", State.task_states)
@mock.patch.object(settings, "task_instance_mutation_hook", autospec=True)
def test_task_instance_mutation_hook(self, mock_hook, dag_maker, session, state):
def mutate_task_instance(task_instance):
if task_instance.queue == "queue1":
task_instance.queue = "queue2"
else:
task_instance.queue = "queue1"
mock_hook.side_effect = mutate_task_instance
with dag_maker(
"test_task_instance_mutation_hook",
schedule=datetime.timedelta(days=1),
start_date=DEFAULT_DATE,
) as dag:
EmptyOperator(task_id="task_to_mutate", owner="test", queue="queue1")
dagrun = self.create_dag_run(dag, session=session)
task = dagrun.get_task_instances()[0]
task.state = state
session.merge(task)
session.commit()
assert task.queue == "queue2"
dagrun.verify_integrity(dag_version_id=DagVersion.get_latest_version(dag.dag_id, session=session).id)
task = dagrun.get_task_instances()[0]
assert task.queue == "queue1"
@pytest.mark.parametrize(
"prev_ti_state, is_ti_schedulable",
[
(TaskInstanceState.SUCCESS, True),
(TaskInstanceState.SKIPPED, True),
(TaskInstanceState.RUNNING, False),
(TaskInstanceState.FAILED, False),
(None, False),
],
)
def test_depends_on_past(self, dag_maker, session, prev_ti_state, is_ti_schedulable):
# DAG tests depends_on_past dependencies
with dag_maker(
dag_id="test_depends_on_past", schedule=datetime.timedelta(days=1), session=session
) as dag:
BaseOperator(
task_id="test_dop_task",
depends_on_past=True,
)
task = dag.tasks[0]
dag_run_1: DagRun = dag_maker.create_dagrun(
logical_date=timezone.datetime(2016, 1, 1, 0, 0, 0),
run_type=DagRunType.SCHEDULED,
)
dag_run_2: DagRun = dag_maker.create_dagrun(
logical_date=timezone.datetime(2016, 1, 2, 0, 0, 0),
run_type=DagRunType.SCHEDULED,
)
prev_ti = TI(task, run_id=dag_run_1.run_id, dag_version_id=dag_run_1.created_dag_version_id)
prev_ti.refresh_from_db(session=session)
prev_ti.set_state(prev_ti_state, session=session)
session.flush()
ti = TI(task, run_id=dag_run_2.run_id, dag_version_id=dag_run_1.created_dag_version_id)
ti.refresh_from_db(session=session)
decision = dag_run_2.task_instance_scheduling_decisions(session=session)
schedulable_tis = [ti.task_id for ti in decision.schedulable_tis]
assert ("test_dop_task" in schedulable_tis) == is_ti_schedulable
@pytest.mark.parametrize(
"prev_ti_state, is_ti_schedulable",
[
(TaskInstanceState.SUCCESS, True),
(TaskInstanceState.SKIPPED, True),
(TaskInstanceState.RUNNING, False),
(TaskInstanceState.FAILED, False),
(None, False),
],
)
def test_wait_for_downstream(self, dag_maker, session, prev_ti_state, is_ti_schedulable):
dag_id = "test_wait_for_downstream"
with dag_maker(dag_id=dag_id, session=session, serialized=True) as dag:
dag_wfd_upstream = EmptyOperator(
task_id="upstream_task",
wait_for_downstream=True,
)
dag_wfd_downstream = EmptyOperator(task_id="downstream_task")
dag_wfd_upstream >> dag_wfd_downstream
upstream, downstream = dag.tasks
# For ti.set_state() to work, the DagRun has to exist,
# Otherwise ti.previous_ti returns an unpersisted TI
dag_run_1: DagRun = dag_maker.create_dagrun(
logical_date=timezone.datetime(2016, 1, 1, 0, 0, 0),
run_type=DagRunType.SCHEDULED,
)
dag_run_2: DagRun = dag_maker.create_dagrun(
logical_date=timezone.datetime(2016, 1, 2, 0, 0, 0),
run_type=DagRunType.SCHEDULED,
)
ti = dag_run_2.get_task_instance(task_id=upstream.task_id, session=session)
# Operate on serialized operator since it is Scheduler code
ti.task = dag.task_dict[ti.task_id]
prev_ti_downstream = dag_run_1.get_task_instance(task_id=downstream.task_id, session=session)
prev_ti_upstream = ti.get_previous_ti(session=session)
assert ti
assert prev_ti_upstream
assert prev_ti_downstream
prev_ti_upstream.state = TaskInstanceState.SUCCESS
prev_ti_downstream.state = prev_ti_state
session.flush()
decision = dag_run_2.task_instance_scheduling_decisions(session=session)
schedulable_tis = [ti.task_id for ti in decision.schedulable_tis]
assert (upstream.task_id in schedulable_tis) == is_ti_schedulable
@pytest.mark.parametrize("state", [DagRunState.QUEUED, DagRunState.RUNNING])
def test_next_dagruns_to_examine_only_unpaused(self, session, state, testing_dag_bundle):
"""
Check that "next_dagruns_to_examine" ignores runs from paused/inactive DAGs
and gets running/queued dagruns
"""
dag = DAG(dag_id="test_dags", schedule=datetime.timedelta(days=1), start_date=DEFAULT_DATE)
EmptyOperator(task_id="dummy", dag=dag, owner="airflow")
orm_dag = DagModel(
dag_id=dag.dag_id,
bundle_name="testing",
has_task_concurrency_limits=False,
next_dagrun=DEFAULT_DATE,
next_dagrun_create_after=DEFAULT_DATE + datetime.timedelta(days=1),
is_stale=False,
)
session.add(orm_dag)
session.flush()
scheduler_dag = sync_dag_to_db(dag, session=session)
dr = scheduler_dag.create_dagrun(
run_id=scheduler_dag.timetable.generate_run_id(
run_type=DagRunType.SCHEDULED,
run_after=DEFAULT_DATE,
data_interval=infer_automated_data_interval(scheduler_dag.timetable, DEFAULT_DATE),
),
run_type=DagRunType.SCHEDULED,
state=state,
logical_date=DEFAULT_DATE,
data_interval=infer_automated_data_interval(scheduler_dag.timetable, DEFAULT_DATE),
run_after=DEFAULT_DATE,
start_date=DEFAULT_DATE if state == DagRunState.RUNNING else None,
session=session,
triggered_by=DagRunTriggeredByType.TEST,
)
if state == DagRunState.RUNNING:
func = DagRun.get_running_dag_runs_to_examine
else:
func = DagRun.get_queued_dag_runs_to_set_running
runs = func(session).all()
assert runs == [dr]
orm_dag.is_paused = True
session.merge(orm_dag)
session.commit()
runs = func(session).all()
assert runs == []
@mock.patch.object(Stats, "timing")
def test_no_scheduling_delay_for_nonscheduled_runs(self, stats_mock, session, testing_dag_bundle):
"""
Tests that dag scheduling delay stat is not called if the dagrun is not a scheduled run.
This case is manual run. Simple test for coherence check.
"""
dag = DAG(dag_id="test_dagrun_stats", schedule=datetime.timedelta(days=1), start_date=DEFAULT_DATE)
dag_task = EmptyOperator(task_id="dummy", dag=dag)
# Create DagModel directly with bundle_name
dag_model = DagModel(
dag_id=dag.dag_id,
bundle_name="testing",
)
session.merge(dag_model)
session.flush()
scheduler_dag = sync_dag_to_db(dag, session=session)
initial_task_states = {dag_task.task_id: TaskInstanceState.SUCCESS}
dag_run = self.create_dag_run(scheduler_dag, task_states=initial_task_states, session=session)
dag_run.update_state(session=session)
assert call(f"dagrun.{dag.dag_id}.first_task_scheduling_delay") not in stats_mock.mock_calls
@pytest.mark.parametrize(
"schedule, expected",
[
("*/5 * * * *", True),
(None, False),
("@once", False),
],
)
def test_emit_scheduling_delay(self, session, schedule, expected, testing_dag_bundle):
"""
Tests that dag scheduling delay stat is set properly once running scheduled dag.
dag_run.update_state() invokes the _emit_true_scheduling_delay_stats_for_finished_state method.
"""
dag = DAG(dag_id="test_emit_dag_stats", start_date=DEFAULT_DATE, schedule=schedule)
dag_task = EmptyOperator(task_id="dummy", dag=dag, owner="airflow")
expected_stat_tags = {"dag_id": f"{dag.dag_id}", "run_type": DagRunType.SCHEDULED}
scheduler_dag = sync_dag_to_db(dag, session=session)
try:
info = scheduler_dag.next_dagrun_info(None)
orm_dag_kwargs = {
"dag_id": dag.dag_id,
"bundle_name": "testing",
"has_task_concurrency_limits": False,
"is_stale": False,
}
if info is not None:
orm_dag_kwargs.update(
{
"next_dagrun": info.logical_date,
"next_dagrun_data_interval": info.data_interval,
"next_dagrun_create_after": info.run_after,
},
)
orm_dag = DagModel(**orm_dag_kwargs)
session.merge(orm_dag)
session.flush()
dag_run = scheduler_dag.create_dagrun(
run_id=scheduler_dag.timetable.generate_run_id(
run_type=DagRunType.SCHEDULED,
run_after=dag.start_date,
data_interval=infer_automated_data_interval(scheduler_dag.timetable, dag.start_date),
),
run_type=DagRunType.SCHEDULED,
state=DagRunState.SUCCESS,
logical_date=dag.start_date,
data_interval=infer_automated_data_interval(scheduler_dag.timetable, dag.start_date),
run_after=dag.start_date,
start_date=dag.start_date,
triggered_by=DagRunTriggeredByType.TEST,
session=session,
)
ti = dag_run.get_task_instance(dag_task.task_id, session)
ti.set_state(TaskInstanceState.SUCCESS, session)
session.flush()
with mock.patch.object(Stats, "timing") as stats_mock:
dag_run.update_state(session)
metric_name = f"dagrun.{dag.dag_id}.first_task_scheduling_delay"
if expected:
true_delay = ti.start_date - dag_run.data_interval_end
sched_delay_stat_call = call(metric_name, true_delay, tags=expected_stat_tags)
sched_delay_stat_call_with_tags = call(
"dagrun.first_task_scheduling_delay", true_delay, tags=expected_stat_tags
)
assert sched_delay_stat_call in stats_mock.mock_calls
assert sched_delay_stat_call_with_tags in stats_mock.mock_calls
else:
# Assert that we never passed the metric
sched_delay_stat_call = call(
metric_name,
mock.ANY,
)
assert sched_delay_stat_call not in stats_mock.mock_calls
finally:
# Don't write anything to the DB
session.rollback()
session.close()
def test_states_sets(self, dag_maker, session):
"""
Tests that adding State.failed_states and State.success_states work as expected.
"""
with dag_maker(
dag_id="test_dagrun_states", schedule=datetime.timedelta(days=1), start_date=DEFAULT_DATE
) as dag:
dag_task_success = EmptyOperator(task_id="dummy")
dag_task_failed = EmptyOperator(task_id="dummy2")
initial_task_states = {
dag_task_success.task_id: TaskInstanceState.SUCCESS,
dag_task_failed.task_id: TaskInstanceState.FAILED,
}
dag_run = self.create_dag_run(dag=dag, task_states=initial_task_states, session=session)
ti_success = dag_run.get_task_instance(dag_task_success.task_id)
ti_failed = dag_run.get_task_instance(dag_task_failed.task_id)
assert ti_success.state in State.success_states
assert ti_failed.state in State.failed_states
def test_update_state_one_unfinished(self, dag_maker, session):
"""
Previously this lived in test_scheduler_job.py
It only really tested the behavior of DagRun.update_state.
As far as I can tell, it checks that if you null out the state on a TI of a finished dag,
and then you call ``update_state``, then the DR will be set to running.
"""
with dag_maker(session=session) as dag:
PythonOperator(task_id="t1", python_callable=lambda: print)
PythonOperator(task_id="t2", python_callable=lambda: print)
dr = dag_maker.create_dagrun(state=DagRunState.FAILED)
for ti in dr.get_task_instances(session=session):
ti.state = TaskInstanceState.FAILED
session.commit()
session.expunge_all()
dr = session.get(DagRun, dr.id)
assert dr.state == DagRunState.FAILED
ti = dr.get_task_instance("t1", session=session)
ti.state = State.NONE
session.commit()
dr = session.get(DagRun, dr.id)
assert dr.state == DagRunState.FAILED
dr.dag = dag
dr.update_state(session=session)
session.commit()
dr = session.get(DagRun, dr.id)
assert dr.state == State.RUNNING
def test_dag_run_dag_versions_method(self, dag_maker, session):
with dag_maker(
"test_dag_run_dag_versions", schedule=datetime.timedelta(days=1), start_date=DEFAULT_DATE
):
EmptyOperator(task_id="empty")
dag_run = dag_maker.create_dagrun()
dm = session.query(DagModel).options(joinedload(DagModel.dag_versions)).one()
assert dag_run.dag_versions[0].id == dm.dag_versions[0].id
def test_dag_run_version_number(self, dag_maker, session):
with dag_maker(
"test_dag_run_version_number", schedule=datetime.timedelta(days=1), start_date=DEFAULT_DATE
):
EmptyOperator(task_id="empty") >> EmptyOperator(task_id="empty2")
dag_run = dag_maker.create_dagrun()
tis = dag_run.task_instances
tis[0].set_state(TaskInstanceState.SUCCESS)
dag_v = DagVersion.write_dag(dag_id=dag_run.dag_id, bundle_name="testing", version_number=2)
tis[1].dag_version = dag_v
session.merge(tis[1])
session.flush()
dag_run = session.query(DagRun).filter(DagRun.run_id == dag_run.run_id).one()
# Check that dag_run.version_number returns the version number of
# the latest task instance dag_version
assert dag_run.version_number == dag_v.version_number
def test_dag_run_dag_versions_with_null_created_dag_version(self, dag_maker, session):
"""Test that dag_versions returns empty list when created_dag_version is None and bundle_version is populated."""
with dag_maker(
"test_dag_run_null_created_dag_version",
schedule=datetime.timedelta(days=1),
start_date=DEFAULT_DATE,
):
EmptyOperator(task_id="empty")
dag_run = dag_maker.create_dagrun()
dag_run.bundle_version = "some_bundle_version"
dag_run.created_dag_version_id = None
dag_run.created_dag_version = None
session.merge(dag_run)
session.flush()
# This should return empty list, not [None]
assert dag_run.dag_versions == []
assert isinstance(dag_run.dag_versions, list)
assert len(dag_run.dag_versions) == 0
def test_dagrun_success_deadline(self, dag_maker, session):
def on_success_callable(context):
assert context["dag_run"].dag_id == "test_dagrun_success_callback"
future_date = datetime.datetime.now() + datetime.timedelta(days=365)
with dag_maker(
dag_id="test_dagrun_success_callback",
schedule=datetime.timedelta(days=1),
on_success_callback=on_success_callable,
deadline=DeadlineAlert(
reference=DeadlineReference.FIXED_DATETIME(future_date),
interval=datetime.timedelta(hours=1),
callback=AsyncCallback(empty_callback_for_deadline),
),
) as dag:
dag_task1 = EmptyOperator(task_id="test_state_succeeded1")
dag_task2 = EmptyOperator(task_id="test_state_succeeded2")
dag_task1.set_downstream(dag_task2)
initial_task_states = {
"test_state_succeeded1": TaskInstanceState.SUCCESS,
"test_state_succeeded2": TaskInstanceState.SUCCESS,
}
# Scheduler uses Serialized DAG -- so use that instead of the Actual DAG.
dag_run = self.create_dag_run(dag=dag, task_states=initial_task_states, session=session)
dag_run = session.merge(dag_run)
dag_run.dag = dag
with mock.patch.object(dag_run, "handle_dag_callback") as handle_dag_callback:
_, callback = dag_run.update_state()
assert handle_dag_callback.mock_calls == [mock.call(dag=dag, success=True, reason="success")]
assert dag_run.state == DagRunState.SUCCESS
# Callbacks are not added until handle_callback = False is passed to dag_run.update_state()
assert callback is None
@pytest.mark.parametrize(
("run_type", "expected_tis"),
[
pytest.param(DagRunType.MANUAL, 1, id="manual"),
pytest.param(DagRunType.BACKFILL_JOB, 3, id="backfill"),
],
)
@mock.patch.object(Stats, "incr")
def test_verify_integrity_task_start_and_end_date(Stats_incr, dag_maker, session, run_type, expected_tis):
"""Test that tasks with specific dates are only created for backfill runs"""
with dag_maker("test", schedule=datetime.timedelta(days=1), start_date=DEFAULT_DATE) as dag:
EmptyOperator(task_id="without")
EmptyOperator(task_id="with_start_date", start_date=DEFAULT_DATE + datetime.timedelta(1))
EmptyOperator(task_id="with_end_date", end_date=DEFAULT_DATE - datetime.timedelta(1))
dag_run = DagRun(
dag_id=dag.dag_id,
run_type=run_type,
logical_date=DEFAULT_DATE,
run_id=DagRun.generate_run_id(run_type=run_type, logical_date=DEFAULT_DATE, run_after=DEFAULT_DATE),
)
dag_run.dag = dag
session.add(dag_run)
session.flush()
dag_version_id = DagVersion.get_latest_version(dag.dag_id, session=session).id
dag_run.verify_integrity(dag_version_id=dag_version_id, session=session)
tis = dag_run.task_instances
assert len(tis) == expected_tis
Stats_incr.assert_any_call(
"task_instance_created_EmptyOperator", expected_tis, tags={"dag_id": "test", "run_type": run_type}
)
Stats_incr.assert_any_call(
"task_instance_created",
expected_tis,
tags={"dag_id": "test", "run_type": run_type, "task_type": "EmptyOperator"},
)
@pytest.mark.parametrize("is_noop", [True, False])
def test_expand_mapped_task_instance_at_create(is_noop, dag_maker, session):
with mock.patch("airflow.settings.task_instance_mutation_hook") as mock_mut:
mock_mut.is_noop = is_noop
literal = [1, 2, 3, 4]
with dag_maker(session=session, dag_id="test_dag"):
mapped = MockOperator.partial(task_id="task_2").expand(arg2=literal)
dr = dag_maker.create_dagrun()
indices = (
session.query(TI.map_index)
.filter_by(task_id=mapped.task_id, dag_id=mapped.dag_id, run_id=dr.run_id)
.order_by(TI.map_index)
.all()
)
assert indices == [(0,), (1,), (2,), (3,)]
@pytest.mark.parametrize("is_noop", [True, False])
def test_expand_mapped_task_instance_task_decorator(is_noop, dag_maker, session):
with mock.patch("airflow.settings.task_instance_mutation_hook") as mock_mut:
mock_mut.is_noop = is_noop
@task
def mynameis(arg):
print(arg)
literal = [1, 2, 3, 4]
with dag_maker(session=session, dag_id="test_dag"):
mynameis.expand(arg=literal)
dr = dag_maker.create_dagrun()
indices = (
session.query(TI.map_index)
.filter_by(task_id="mynameis", dag_id=dr.dag_id, run_id=dr.run_id)
.order_by(TI.map_index)
.all()
)
assert indices == [(0,), (1,), (2,), (3,)]
def test_mapped_literal_verify_integrity(dag_maker, session):
"""Test that when the length of a mapped literal changes we remove extra TIs"""
@task
def task_2(arg2): ...
with dag_maker(session=session):
task_2.expand(arg2=[1, 2, 3, 4])
dr = dag_maker.create_dagrun()
query = (
select(TI.map_index, TI.state)
.filter_by(task_id="task_2", dag_id=dr.dag_id, run_id=dr.run_id)
.order_by(TI.map_index)
)
indices = session.execute(query).all()
assert indices == [(0, None), (1, None), (2, None), (3, None)]
# Now "change" the DAG and we should see verify_integrity REMOVE some TIs
with dag_maker(session=session):
task_2.expand(arg2=[1, 2])
# Update it to use the new serialized DAG
dr.dag = dag_maker.dag
dag_version_id = DagVersion.get_latest_version(dag_id=dr.dag_id, session=session).id
dr.verify_integrity(dag_version_id=dag_version_id, session=session)
indices = session.execute(query).all()
assert indices == [(0, None), (1, None), (2, TaskInstanceState.REMOVED), (3, TaskInstanceState.REMOVED)]
def test_mapped_literal_to_xcom_arg_verify_integrity(dag_maker, session):
"""Test that when we change from literal to a XComArg the TIs are removed"""
@task
def task_2(arg2): ...
with dag_maker(session=session):
task_2.expand(arg2=[1, 2, 3, 4])
dr = dag_maker.create_dagrun()
with dag_maker(session=session):
t1 = BaseOperator(task_id="task_1")
task_2.expand(arg2=t1.output)
dr.dag = dag_maker.dag
dag_version_id = DagVersion.get_latest_version(dag_id=dr.dag_id, session=session).id
dr.verify_integrity(dag_version_id=dag_version_id, session=session)
indices = (
session.query(TI.map_index, TI.state)
.filter_by(task_id="task_2", dag_id=dr.dag_id, run_id=dr.run_id)
.order_by(TI.map_index)
.all()
)
assert indices == [
(0, TaskInstanceState.REMOVED),
(1, TaskInstanceState.REMOVED),
(2, TaskInstanceState.REMOVED),
(3, TaskInstanceState.REMOVED),
]
def test_mapped_literal_length_increase_adds_additional_ti(dag_maker, session):
"""Test that when the length of mapped literal increases, additional ti is added"""
@task
def task_2(arg2): ...
with dag_maker(session=session, serialized=True):
task_2.expand(arg2=[1, 2, 3, 4])
dr = dag_maker.create_dagrun()
query = (
select(TI.map_index, TI.state)
.filter_by(task_id="task_2", dag_id=dr.dag_id, run_id=dr.run_id)
.order_by(TI.map_index)
)
indices = session.execute(query).all()
assert sorted(indices) == [
(0, State.NONE),
(1, State.NONE),
(2, State.NONE),
(3, State.NONE),
]
# Now "increase" the length of literal
with dag_maker(session=session, serialized=True) as dag:
task_2.expand(arg2=[1, 2, 3, 4, 5])
dr.dag = dag
# Every mapped task is revised at task_instance_scheduling_decision
dr.task_instance_scheduling_decisions()
indices = session.execute(query).all()
assert sorted(indices) == [
(0, State.NONE),
(1, State.NONE),
(2, State.NONE),
(3, State.NONE),
(4, State.NONE),
]
def test_mapped_literal_length_reduction_adds_removed_state(dag_maker, session):
"""Test that when the length of mapped literal reduces, removed state is added"""
@task
def task_2(arg2): ...
with dag_maker(session=session):
task_2.expand(arg2=[1, 2, 3, 4])
dr = dag_maker.create_dagrun()
query = (
select(TI.map_index, TI.state)
.filter_by(task_id="task_2", dag_id=dr.dag_id, run_id=dr.run_id)
.order_by(TI.map_index)
)
indices = session.execute(query).all()
assert sorted(indices) == [
(0, State.NONE),
(1, State.NONE),
(2, State.NONE),
(3, State.NONE),
]
with dag_maker(session=session):
task_2.expand(arg2=[1, 2])
dr.dag = dag_maker.dag
# Since we change the literal on the dag file itself, the dag_hash will
# change which will have the scheduler verify the dr integrity
dag_version_id = DagVersion.get_latest_version(dag_id=dr.dag_id, session=session).id
dr.verify_integrity(dag_version_id=dag_version_id, session=session)
indices = session.execute(query).all()
assert sorted(indices) == [
(0, State.NONE),
(1, State.NONE),
(2, State.REMOVED),
(3, State.REMOVED),
]
def test_mapped_length_increase_at_runtime_adds_additional_tis(dag_maker, session):
"""Test that when the length of mapped literal increases at runtime, additional ti is added"""
# Variable.set(key="arg1", value=[1, 2, 3])
@task
def task_1():
# Behave as if we did this
# return Variable.get("arg1", deserialize_json=True)
...
with dag_maker(session=session) as dag:
@task
def task_2(arg2): ...
task_2.expand(arg2=task_1())
dr: DagRun = dag_maker.create_dagrun()
ti = dr.get_task_instance(task_id="task_1", session=session)
assert ti
ti.state = TaskInstanceState.SUCCESS
# Behave as if TI ran after: Variable.set(key="arg1", value=[1, 2, 3])
session.add(TaskMap.from_task_instance_xcom(ti, [1, 2, 3]))
session.flush()
decision = dr.task_instance_scheduling_decisions(session=session)
indices = [(ti.task_id, ti.map_index) for ti in decision.schedulable_tis]
assert indices == [("task_2", 0), ("task_2", 1), ("task_2", 2)]
# Now "clear" and "increase" the length of literal
dag.clear()
# "Run" the first task again to get the new lengths
ti = dr.get_task_instance(task_id="task_1", session=session)
assert ti
# Behave as if we did and re-ran the task: Variable.set(key="arg1", value=[1, 2, 3, 4])
session.merge(TaskMap.from_task_instance_xcom(ti, [1, 2, 3, 4]))
ti.state = TaskInstanceState.SUCCESS
session.flush()
# this would be called by the localtask job
decision = dr.task_instance_scheduling_decisions(session=session)
indices = [(ti.task_id, ti.state, ti.map_index) for ti in decision.schedulable_tis]
assert sorted(indices) == [
("task_2", None, 0),
("task_2", None, 1),
("task_2", None, 2),
("task_2", None, 3),
]
def test_mapped_literal_length_reduction_at_runtime_adds_removed_state(dag_maker, session):
"""
Test that when the length of mapped literal reduces at runtime, the missing task instances
are marked as removed
"""
@task
def task_1():
# return Variable.get("arg1", deserialize_json=True)
...
with dag_maker(session=session) as dag:
@task
def task_2(arg2): ...
task_2.expand(arg2=task_1())
dr: DagRun = dag_maker.create_dagrun()
ti = dr.get_task_instance(task_id="task_1", session=session)
assert ti
ti.state = TaskInstanceState.SUCCESS
# Behave as if TI ran after: Variable.set(key="arg1", value=[1, 2, 3])
session.add(TaskMap.from_task_instance_xcom(ti, [1, 2, 3]))
session.flush()
dr.task_instance_scheduling_decisions(session=session)
query = (
select(TI.map_index, TI.state)
.filter_by(task_id="task_2", dag_id=dr.dag_id, run_id=dr.run_id)
.order_by(TI.map_index)
)
indices = session.execute(query).all()
assert indices == [(0, None), (1, None), (2, None)]
# Now "clear" and "reduce" the length of literal
dag.clear()
# "Run" the first task again to get the new lengths
ti = dr.get_task_instance(task_id="task_1", session=session)
assert ti
# Behave as if we did and re-ran the task: Variable.set(key="arg1", value=[1, 2])
session.merge(TaskMap.from_task_instance_xcom(ti, [1, 2]))
ti.state = TaskInstanceState.SUCCESS
session.flush()
dag_version_id = DagVersion.get_latest_version(dag.dag_id, session=session).id
dr.verify_integrity(dag_version_id=dag_version_id, session=session)
indices = session.execute(query).all()
assert sorted(indices) == [
(0, State.NONE),
(1, State.NONE),
(2, TaskInstanceState.REMOVED),
]
def test_mapped_literal_faulty_state_in_db(dag_maker, session):
"""
This test tries to recreate a faulty state in the database and checks if we can recover from it.
The state that happens is that there exists mapped task instances and the unmapped task instance.
So we have instances with map_index [-1, 0, 1]. The -1 task instances should be removed in this case.
"""
with dag_maker(session=session) as dag:
@task
def task_1():
return [1, 2]
@task
def task_2(arg2): ...
task_2.expand(arg2=task_1())
dr = dag_maker.create_dagrun()
ti = dr.get_task_instance(task_id="task_1")
ti.run()
decision = dr.task_instance_scheduling_decisions()
assert len(decision.schedulable_tis) == 2
# We insert a faulty record
session.add(TaskInstance(task=dag.get_task("task_2"), run_id=dr.run_id, dag_version_id=ti.dag_version_id))
session.flush()
decision = dr.task_instance_scheduling_decisions()
assert len(decision.schedulable_tis) == 2
def test_calls_to_verify_integrity_with_mapped_task_zero_length_at_runtime(dag_maker, session, caplog):
"""
Test zero length reduction in mapped task at runtime with calls to dagrun.verify_integrity
"""
import logging
with dag_maker(session=session) as dag:
@task
def task_1():
# return Variable.get("arg1", deserialize_json=True)
...
@task
def task_2(arg2): ...
task_2.expand(arg2=task_1())
dr: DagRun = dag_maker.create_dagrun()
ti = dr.get_task_instance(task_id="task_1", session=session)
assert ti
# "Run" task_1
ti.state = TaskInstanceState.SUCCESS
# Behave as if TI ran after: Variable.set(key="arg1", value=[1, 2, 3])
session.add(TaskMap.from_task_instance_xcom(ti, [1, 2, 3]))
session.flush()
decision = dr.task_instance_scheduling_decisions(session=session)
ti_2 = decision.schedulable_tis[0]
assert ti_2
query = (
select(TI.map_index, TI.state)
.filter_by(task_id="task_2", dag_id=dr.dag_id, run_id=dr.run_id)
.order_by(TI.map_index)
)
indices = session.execute(query).all()
assert sorted(indices) == [(0, State.NONE), (1, State.NONE), (2, State.NONE)]
# Now "clear" and "reduce" the length to empty list
dag.clear()
# We don't execute task anymore, but this is what we are
# simulating happened:
# Variable.set(key="arg1", value=[])
session.merge(TaskMap.from_task_instance_xcom(ti, []))
session.flush()
# Run the first task again to get the new lengths
with caplog.at_level(logging.DEBUG):
# Run verify_integrity as a whole and assert the tasks were removed
dag_version = DagVersion.get_latest_version(dag.dag_id)
dr.verify_integrity(dag_version_id=dag_version.id, session=session)
indices = session.execute(query).all()
assert indices == [
(0, TaskInstanceState.REMOVED),
(1, TaskInstanceState.REMOVED),
(2, TaskInstanceState.REMOVED),
]
def test_mapped_mixed_literal_not_expanded_at_create(dag_maker, session):
literal = [1, 2, 3, 4]
with dag_maker(session=session):
task = BaseOperator(task_id="task_1")
mapped = MockOperator.partial(task_id="task_2").expand(arg1=literal, arg2=task.output)
dr = dag_maker.create_dagrun()
query = (
session.query(TI.map_index, TI.state)
.filter_by(task_id=mapped.task_id, dag_id=mapped.dag_id, run_id=dr.run_id)
.order_by(TI.map_index)
)
assert query.all() == [(-1, None)]
# Verify_integrity shouldn't change the result now that the TIs exist
dag_version_id = DagVersion.get_latest_version(dag_id=dr.dag_id, session=session).id
dr.verify_integrity(dag_version_id=dag_version_id, session=session)
assert query.all() == [(-1, None)]
def test_mapped_task_group_expands_at_create(dag_maker, session):
literal = [[1, 2], [3, 4]]
with dag_maker(session=session):
@task_group
def tg(x):
# Normal operator in mapped task group, expands to 2 tis.
MockOperator(task_id="t1")
# Mapped operator expands *again* against mapped task group arguments to 4 tis.
with pytest.raises(NotImplementedError) as ctx:
MockOperator.partial(task_id="t2").expand(arg1=literal)
assert str(ctx.value) == "operator expansion in an expanded task group is not yet supported"
# Normal operator referencing mapped task group arguments does not further expand, only 2 tis.
MockOperator(task_id="t3", arg1=x)
# It can expand *again* (since each item in x is a list) but this is not done at parse time.
with pytest.raises(NotImplementedError) as ctx:
MockOperator.partial(task_id="t4").expand(arg1=x)
assert str(ctx.value) == "operator expansion in an expanded task group is not yet supported"
tg.expand(x=literal)
dr = dag_maker.create_dagrun()
query = (
session.query(TI.task_id, TI.map_index, TI.state)
.filter_by(dag_id=dr.dag_id, run_id=dr.run_id)
.order_by(TI.task_id, TI.map_index)
)
assert query.all() == [
("tg.t1", 0, None),
("tg.t1", 1, None),
# ("tg.t2", 0, None),
# ("tg.t2", 1, None),
# ("tg.t2", 2, None),
# ("tg.t2", 3, None),
("tg.t3", 0, None),
("tg.t3", 1, None),
# ("tg.t4", -1, None),
]
def test_mapped_task_group_empty_operator(dag_maker, session):
"""
Test that dynamic task inside a dynamic task group only marks
the corresponding downstream EmptyOperator as success.
"""
literal = [1, 2, 3]
with dag_maker(session=session) as dag:
@task_group
def tg(x):
@task
def t1(x):
return x
t2 = EmptyOperator(task_id="t2")
@task
def t3(x):
return x
t1(x) >> t2 >> t3(x)
tg.expand(x=literal)
dr = dag_maker.create_dagrun()
t2_task = dag.get_task("tg.t2")
t2_0 = dr.get_task_instance(task_id="tg.t2", map_index=0)
t2_0.refresh_from_task(t2_task)
assert t2_0.state is None
t2_1 = dr.get_task_instance(task_id="tg.t2", map_index=1)
t2_1.refresh_from_task(t2_task)
assert t2_1.state is None
dr.schedule_tis([t2_0])
t2_0 = dr.get_task_instance(task_id="tg.t2", map_index=0)
assert t2_0.state == TaskInstanceState.SUCCESS
t2_1 = dr.get_task_instance(task_id="tg.t2", map_index=1)
assert t2_1.state is None
def test_ti_scheduling_mapped_zero_length(dag_maker, session):
with dag_maker(session=session):
task = BaseOperator(task_id="task_1")
mapped = MockOperator.partial(task_id="task_2").expand(arg2=task.output)
dr: DagRun = dag_maker.create_dagrun()
ti1, ti2 = sorted(dr.task_instances, key=lambda ti: ti.task_id)
ti1.state = TaskInstanceState.SUCCESS
session.add(
TaskMap(dag_id=dr.dag_id, task_id=ti1.task_id, run_id=dr.run_id, map_index=-1, length=0, keys=None)
)
session.flush()
decision = dr.task_instance_scheduling_decisions(session=session)
# ti1 finished execution. ti2 goes directly to finished state because it's
# expanded against a zero-length XCom.
assert decision.finished_tis == [ti1, ti2]
indices = (
session.query(TI.map_index, TI.state)
.filter_by(task_id=mapped.task_id, dag_id=mapped.dag_id, run_id=dr.run_id)
.order_by(TI.map_index)
.all()
)
assert indices == [(-1, TaskInstanceState.SKIPPED)]
@pytest.mark.parametrize("trigger_rule", [TriggerRule.ALL_DONE, TriggerRule.ALL_SUCCESS])
def test_mapped_task_upstream_failed(dag_maker, session, trigger_rule):
from airflow.providers.standard.operators.python import PythonOperator
with dag_maker(session=session) as dag:
@dag.task
def make_list():
return [f'echo "{a!r}"' for a in [1, 2, {"a": "b"}]]
def consumer(*args):
print(repr(args))
PythonOperator.partial(
task_id="consumer",
trigger_rule=trigger_rule,
python_callable=consumer,
).expand(op_args=make_list())
dr = dag_maker.create_dagrun()
_, make_list_ti = sorted(dr.task_instances, key=lambda ti: ti.task_id)
make_list_ti.state = TaskInstanceState.FAILED
session.flush()
tis, _ = dr.update_state(execute_callbacks=False, session=session)
assert tis == []
tis = sorted(dr.task_instances, key=lambda ti: ti.task_id)
assert sorted((ti.task_id, ti.map_index, ti.state) for ti in tis) == [
("consumer", -1, TaskInstanceState.UPSTREAM_FAILED),
("make_list", -1, TaskInstanceState.FAILED),
]
# Bug/possible source of optimization: The DR isn't marked as failed until
# in the loop that marks the last task as UPSTREAM_FAILED
tis, _ = dr.update_state(execute_callbacks=False, session=session)
assert tis == []
assert dr.state == DagRunState.FAILED
def test_mapped_task_all_finish_before_downstream(dag_maker, session):
with dag_maker(session=session) as dag:
@dag.task
def make_list():
return [1, 2]
@dag.task
def double(value):
return value * 2
@dag.task
def consumer(value):
...
# result = list(value)
consumer(value=double.expand(value=make_list()))
dr: DagRun = dag_maker.create_dagrun()
def _task_ids(tis):
return [ti.task_id for ti in tis]
# The first task is always make_list.
decision = dr.task_instance_scheduling_decisions(session=session)
assert _task_ids(decision.schedulable_tis) == ["make_list"]
# After make_list is run, double is expanded.
ti = decision.schedulable_tis[0]
ti.state = TaskInstanceState.SUCCESS
session.add(TaskMap.from_task_instance_xcom(ti, [1, 2]))
session.flush()
decision = dr.task_instance_scheduling_decisions(session=session)
assert _task_ids(decision.schedulable_tis) == ["double", "double"]
# Running just one of the mapped tis does not make downstream schedulable.
ti = decision.schedulable_tis[0]
ti.state = TaskInstanceState.SUCCESS
session.flush()
decision = dr.task_instance_scheduling_decisions(session=session)
assert _task_ids(decision.schedulable_tis) == ["double"]
# Downstream is scheduleable after all mapped tis are run.
ti = decision.schedulable_tis[0]
ti.state = TaskInstanceState.SUCCESS
session.flush()
decision = dr.task_instance_scheduling_decisions(session=session)
assert _task_ids(decision.schedulable_tis) == ["consumer"]
def test_schedule_tis_map_index(dag_maker, session):
with dag_maker(session=session, dag_id="test"):
task = BaseOperator(task_id="task_1")
dr = DagRun(dag_id="test", run_id="test", run_type=DagRunType.MANUAL)
dag_version = DagVersion.get_latest_version(dag_id=dr.dag_id)
ti0 = TI(
task=task,
run_id=dr.run_id,
map_index=0,
state=TaskInstanceState.SUCCESS,
dag_version_id=dag_version.id,
)
ti1 = TI(task=task, run_id=dr.run_id, map_index=1, state=None, dag_version_id=dag_version.id)
ti2 = TI(
task=task,
run_id=dr.run_id,
map_index=2,
state=TaskInstanceState.SUCCESS,
dag_version_id=dag_version.id,
)
session.add_all((dr, ti0, ti1, ti2))
session.flush()
assert dr.schedule_tis((ti1,), session=session) == 1
session.refresh(ti0)
session.refresh(ti1)
session.refresh(ti2)
assert ti0.state == TaskInstanceState.SUCCESS
assert ti1.state == TaskInstanceState.SCHEDULED
assert ti2.state == TaskInstanceState.SUCCESS
@pytest.mark.xfail(reason="We can't keep this behaviour with remote workers where scheduler can't reach xcom")
@pytest.mark.need_serialized_dag
def test_schedule_tis_start_trigger(dag_maker, session):
"""
Test that an operator with start_trigger_args set can be directly deferred during scheduling.
"""
class TestOperator(BaseOperator):
start_trigger_args = StartTriggerArgs(
trigger_cls="airflow.triggers.testing.SuccessTrigger",
trigger_kwargs=None,
next_method="execute_complete",
timeout=None,
)
start_from_trigger = True
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.start_trigger_args.trigger_kwargs = {}
def execute_complete(self):
pass
with dag_maker(session=session):
TestOperator(task_id="test_task")
dr: DagRun = dag_maker.create_dagrun()
ti = dr.get_task_instance("test_task")
assert ti.state is None
ti.task = dr.dag.get_task("test_task")
dr.schedule_tis((ti,), session=session)
assert ti.state == TaskInstanceState.DEFERRED
def test_schedule_tis_empty_operator_try_number(dag_maker, session: Session):
"""
When empty operator is not actually run, then we need to increment the try_number,
since ordinarily it's incremented when scheduled, but empty operator is generally not scheduled.
"""
with dag_maker(session=session):
BashOperator(task_id="real_task", bash_command="echo 1")
EmptyOperator(task_id="empty_task")
dr: DagRun = dag_maker.create_dagrun(session=session)
session.commit()
tis = dr.task_instances
dr.schedule_tis(tis, session=session)
session.commit()
session.expunge_all()
tis = dr.get_task_instances(session=session)
real_ti = next(x for x in tis if x.task_id == "real_task")
empty_ti = next(x for x in tis if x.task_id == "empty_task")
assert real_ti.try_number == 1
assert empty_ti.try_number == 1
@pytest.mark.xfail(reason="We can't keep this behaviour with remote workers where scheduler can't reach xcom")
def test_schedule_tis_start_trigger_through_expand(dag_maker, session):
"""
Test that an operator with start_trigger_args set can be directly deferred during scheduling.
"""
class TestOperator(BaseOperator):
start_trigger_args = StartTriggerArgs(
trigger_cls="airflow.triggers.testing.SuccessTrigger",
trigger_kwargs={},
next_method="execute_complete",
timeout=None,
)
start_from_trigger = False
def __init__(self, *args, start_from_trigger: bool = False, **kwargs):
super().__init__(*args, **kwargs)
self.start_from_trigger = start_from_trigger
def execute_complete(self):
pass
with dag_maker(session=session):
TestOperator.partial(task_id="test_task").expand(start_from_trigger=[True, False])
dr: DagRun = dag_maker.create_dagrun()
dr.schedule_tis(dr.task_instances, session=session)
tis = [(ti.state, ti.map_index) for ti in dr.task_instances]
assert tis[0] == (TaskInstanceState.DEFERRED, 0)
assert tis[1] == (None, 1)
def test_mapped_expand_kwargs(dag_maker):
with dag_maker():
@task
def task_0():
return {"arg1": "a", "arg2": "b"}
@task
def task_1(args_0):
return [args_0, {"arg1": "y"}, {"arg2": "z"}]
args_0 = task_0()
args_list = task_1(args_0=args_0)
MockOperator.partial(task_id="task_2").expand_kwargs(args_list)
MockOperator.partial(task_id="task_3").expand_kwargs(
[{"arg1": "a", "arg2": "b"}, {"arg1": "y"}, {"arg2": "z"}],
)
MockOperator.partial(task_id="task_4").expand_kwargs([args_0, {"arg1": "y"}, {"arg2": "z"}])
dr: DagRun = dag_maker.create_dagrun()
tis = {(ti.task_id, ti.map_index): ti for ti in dr.task_instances}
# task_2 is not expanded yet since it relies on one single XCom input.
# task_3 and task_4 received a pure literal and can expanded right away.
# task_4 relies on an XCom input in the list, but can also be expanded.
assert sorted(map_index for (task_id, map_index) in tis if task_id == "task_2") == [-1]
assert sorted(map_index for (task_id, map_index) in tis if task_id == "task_3") == [0, 1, 2]
assert sorted(map_index for (task_id, map_index) in tis if task_id == "task_4") == [0, 1, 2]
tis[("task_0", -1)].run()
tis[("task_1", -1)].run()
# With the upstreams available, everything should get expanded now.
decision = dr.task_instance_scheduling_decisions()
assert {(ti.task_id, ti.map_index): ti.state for ti in decision.schedulable_tis} == {
("task_2", 0): None,
("task_2", 1): None,
("task_2", 2): None,
("task_3", 0): None,
("task_3", 1): None,
("task_3", 2): None,
("task_4", 0): None,
("task_4", 1): None,
("task_4", 2): None,
}
def test_mapped_skip_upstream_not_deadlock(dag_maker):
with dag_maker() as dag:
@dag.task
def add_one(x: int):
return x + 1
@dag.task
def say_hi():
print("Hi")
added_values = add_one.expand(x=[])
added_more_values = add_one.expand(x=[])
say_hi() >> added_values
added_values >> added_more_values
dr = dag_maker.create_dagrun()
session = dag_maker.session
tis = {ti.task_id: ti for ti in dr.task_instances}
tis["say_hi"].state = TaskInstanceState.SUCCESS
session.flush()
dr.update_state(session=session) # expands the mapped tasks
dr.update_state(session=session) # marks the task as skipped
dr.update_state(session=session) # marks dagrun as success
assert dr.state == DagRunState.SUCCESS
assert tis["add_one__1"].state == TaskInstanceState.SKIPPED
def test_schedulable_task_exist_when_rerun_removed_upstream_mapped_task(session, dag_maker):
from airflow.sdk import task
@task
def do_something(i):
return 1
@task
def do_something_else(i):
return 1
with dag_maker():
nums = do_something.expand(i=[i + 1 for i in range(5)])
do_something_else.expand(i=nums)
dr = dag_maker.create_dagrun()
tis = dr.get_task_instances()
for ti in tis:
if ti.task_id == "do_something_else":
ti.map_index = 0
task = ti.task
for map_index in range(1, 5):
ti = TI(task, run_id=dr.run_id, map_index=map_index, dag_version_id=ti.dag_version_id)
session.add(ti)
ti.dag_run = dr
else:
# run tasks "do_something" to get XCOMs for correct downstream length
ti.run()
session.flush()
tis = dr.get_task_instances()
for ti in tis:
if ti.task_id == "do_something":
if ti.map_index > 2:
ti.state = TaskInstanceState.REMOVED
else:
ti.state = TaskInstanceState.SUCCESS
session.merge(ti)
session.commit()
# The Upstream is done with 2 removed tis and 3 success tis
(tis, _) = dr.update_state()
assert len(tis) == 3
assert dr.state != DagRunState.FAILED
@pytest.mark.parametrize(
"partial_params, mapped_params, expected",
[
pytest.param(None, [{"a": 1}], 1, id="simple"),
pytest.param({"b": 2}, [{"a": 1}], 1, id="merge"),
pytest.param({"b": 2}, [{"a": 1, "b": 3}], 1, id="override"),
],
)
def test_mapped_expand_against_params(dag_maker, partial_params, mapped_params, expected):
with dag_maker():
BaseOperator.partial(task_id="t", params=partial_params).expand(params=mapped_params)
dr: DagRun = dag_maker.create_dagrun()
decision = dr.task_instance_scheduling_decisions()
assert len(decision.schedulable_tis) == expected
def test_mapped_task_group_expands(dag_maker, session):
with dag_maker(session=session):
@task_group
def tg(x, y):
return MockOperator(task_id="task_2", arg1=x, arg2=y)
task_1 = BaseOperator(task_id="task_1")
tg.expand(x=task_1.output, y=[1, 2, 3])
dr: DagRun = dag_maker.create_dagrun()
# Not expanding task_2 yet since it depends on result from task_1.
decision = dr.task_instance_scheduling_decisions(session=session)
assert {(ti.task_id, ti.map_index, ti.state) for ti in decision.tis} == {
("task_1", -1, None),
("tg.task_2", -1, None),
}
# Simulate task_1 execution to produce TaskMap.
(ti_1,) = decision.schedulable_tis
assert ti_1.task_id == "task_1"
ti_1.state = TaskInstanceState.SUCCESS
session.add(TaskMap.from_task_instance_xcom(ti_1, ["a", "b"]))
session.flush()
# Now task_2 in mapped tagk group is expanded.
decision = dr.task_instance_scheduling_decisions(session=session)
assert {(ti.task_id, ti.map_index, ti.state) for ti in decision.schedulable_tis} == {
("tg.task_2", 0, None),
("tg.task_2", 1, None),
("tg.task_2", 2, None),
("tg.task_2", 3, None),
("tg.task_2", 4, None),
("tg.task_2", 5, None),
}
@pytest.mark.parametrize("rerun_length", [0, 1, 2, 3])
def test_mapped_task_rerun_with_different_length_of_args(session, dag_maker, rerun_length):
@task
def generate_mapping_args():
context = get_current_context()
if context["ti"].try_number == 0:
args = [i for i in range(2)]
else:
args = [i for i in range(rerun_length)]
return args
@task
def mapped_print_value(arg):
return arg
with dag_maker(session=session):
args = generate_mapping_args()
mapped_print_value.expand(arg=args)
# First Run
dr = dag_maker.create_dagrun()
dag_maker.run_ti("generate_mapping_args", dr)
decision = dr.task_instance_scheduling_decisions(session=session)
for ti in decision.schedulable_tis:
dag_maker.run_ti(ti.task_id, dr, map_index=ti.map_index)
clear_task_instances(dr.get_task_instances(), session=session)
# Second Run
ti = dr.get_task_instance(task_id="generate_mapping_args", session=session)
ti.try_number += 1
session.merge(ti)
dag_maker.run_ti("generate_mapping_args", dr)
# Check if the new mapped task instances are correctly scheduled
decision = dr.task_instance_scheduling_decisions(session=session)
assert len(decision.schedulable_tis) == rerun_length
assert all([ti.task_id == "mapped_print_value" for ti in decision.schedulable_tis])
# Check if mapped task rerun successfully
for ti in decision.schedulable_tis:
dag_maker.run_ti(ti.task_id, dr, map_index=ti.map_index)
query = select(TI).where(
TI.dag_id == dr.dag_id,
TI.run_id == dr.run_id,
TI.task_id == "mapped_print_value",
TI.state == TaskInstanceState.SUCCESS,
)
success_tis = session.execute(query).all()
assert len(success_tis) == rerun_length
def test_operator_mapped_task_group_receives_value(dag_maker, session):
with dag_maker(session=session):
@task
def t(value): ...
@task_group
def tg(va):
# Each expanded group has one t1 and t2 each.
t1 = t.override(task_id="t1")(va)
t2 = t.override(task_id="t2")(t1)
with pytest.raises(NotImplementedError) as ctx:
t.override(task_id="t4").expand(value=va)
assert str(ctx.value) == "operator expansion in an expanded task group is not yet supported"
return t2
# The group is mapped by 3.
t2 = tg.expand(va=[["a", "b"], [4], ["z"]])
# Aggregates results from task group.
t.override(task_id="t3")(t2)
dr: DagRun = dag_maker.create_dagrun()
results = set()
decision = dr.task_instance_scheduling_decisions(session=session)
for ti in decision.schedulable_tis:
results.add((ti.task_id, ti.map_index))
ti.state = TaskInstanceState.SUCCESS
session.flush()
assert results == {("tg.t1", 0), ("tg.t1", 1), ("tg.t1", 2)}
results.clear()
decision = dr.task_instance_scheduling_decisions(session=session)
for ti in decision.schedulable_tis:
results.add((ti.task_id, ti.map_index))
ti.state = TaskInstanceState.SUCCESS
session.flush()
assert results == {("tg.t2", 0), ("tg.t2", 1), ("tg.t2", 2)}
results.clear()
decision = dr.task_instance_scheduling_decisions(session=session)
for ti in decision.schedulable_tis:
results.add((ti.task_id, ti.map_index))
ti.state = TaskInstanceState.SUCCESS
session.flush()
assert results == {("t3", -1)}
def test_mapping_against_empty_list(dag_maker, session):
with dag_maker(session=session):
@task
def add_one(x: int):
return x + 1
@task
def say_hi():
print("Hi")
@task
def say_bye():
print("Bye")
added_values = add_one.expand(x=[])
added_more_values = add_one.expand(x=[])
added_more_more_values = add_one.expand(x=[])
say_hi() >> say_bye() >> added_values
added_values >> added_more_values >> added_more_more_values
dr: DagRun = dag_maker.create_dagrun()
tis = {ti.task_id: ti for ti in dr.get_task_instances(session=session)}
say_hi_ti = tis["say_hi"]
say_bye_ti = tis["say_bye"]
say_hi_ti.state = TaskInstanceState.SUCCESS
say_bye_ti.state = TaskInstanceState.SUCCESS
session.merge(say_hi_ti)
session.merge(say_bye_ti)
session.flush()
dr.update_state(session=session)
dr.update_state(session=session) # marks first empty mapped task as skipped
dr.update_state(session=session) # marks second empty mapped task as skipped
dr.update_state(session=session) # marks the third empty mapped task as skipped and dagrun as success
tis = {ti.task_id: ti.state for ti in dr.get_task_instances(session=session)}
assert tis["say_hi"] == TaskInstanceState.SUCCESS
assert tis["say_bye"] == TaskInstanceState.SUCCESS
assert tis["add_one"] == TaskInstanceState.SKIPPED
assert tis["add_one__1"] == TaskInstanceState.SKIPPED
assert tis["add_one__2"] == TaskInstanceState.SKIPPED
assert dr.state == State.SUCCESS
def test_mapped_task_depends_on_past(dag_maker, session):
with dag_maker(session=session):
@task(depends_on_past=True)
def print_value(value):
print(value)
print_value.expand_kwargs([{"value": i} for i in range(2)])
dr1: DagRun = dag_maker.create_dagrun(run_type=DagRunType.SCHEDULED)
dr2: DagRun = dag_maker.create_dagrun_after(dr1, run_type=DagRunType.SCHEDULED)
# print_value in dr2 is not ready yet since the task depends on past.
decision = dr2.task_instance_scheduling_decisions(session=session)
assert len(decision.schedulable_tis) == 0
# Run print_value in dr1.
decision = dr1.task_instance_scheduling_decisions(session=session)
assert len(decision.schedulable_tis) == 2
for ti in decision.schedulable_tis:
ti.state = TaskInstanceState.SUCCESS
session.flush()
# Now print_value in dr2 can run
decision = dr2.task_instance_scheduling_decisions(session=session)
assert len(decision.schedulable_tis) == 2
for ti in decision.schedulable_tis:
ti.state = TaskInstanceState.SUCCESS
session.flush()
# Both runs are finished now.
decision = dr1.task_instance_scheduling_decisions(session=session)
assert len(decision.unfinished_tis) == 0
decision = dr2.task_instance_scheduling_decisions(session=session)
assert len(decision.unfinished_tis) == 0
def test_xcom_map_skip_raised(dag_maker, session):
result = None
with dag_maker(session=session) as dag:
# Note: this doesn't actually run this dag, the callbacks are for reference only.
@dag.task()
def push():
return ["a", "b", "c"]
@dag.task()
def forward(value):
return value
@dag.task(trigger_rule=TriggerRule.ALL_DONE)
def collect(value):
nonlocal result
result = list(value)
def skip_c(v):
...
# if v == "c":
# raise AirflowSkipException
# return {"value": v}
collect(value=forward.expand_kwargs(push().map(skip_c)))
dr: DagRun = dag_maker.create_dagrun(session=session)
def _task_ids(tis):
return [(ti.task_id, ti.map_index) for ti in tis]
# Check that when forward w/ map_index=2 ends up skipping, that the collect task can still be
# scheduled!
# Run "push".
decision = dr.task_instance_scheduling_decisions(session=session)
assert _task_ids(decision.schedulable_tis) == [("push", -1)]
ti = decision.schedulable_tis[0]
ti.state = TaskInstanceState.SUCCESS
session.add(TaskMap.from_task_instance_xcom(ti, push.function()))
session.flush()
decision = dr.task_instance_scheduling_decisions(session=session)
assert _task_ids(decision.schedulable_tis) == [
("forward", 0),
("forward", 1),
("forward", 2),
]
# Run "forward". "c"/index 2 is skipped. Runtime behaviour checked in test_xcom_map_raise_to_skip in
# TaskSDK
for ti, state in zip(
decision.schedulable_tis,
[TaskInstanceState.SUCCESS, TaskInstanceState.SUCCESS, TaskInstanceState.SKIPPED],
):
ti.state = state
session.flush()
# Now "collect" should only get "a" and "b".
decision = dr.task_instance_scheduling_decisions(session=session)
assert _task_ids(decision.schedulable_tis) == [("collect", -1)]
def test_clearing_task_and_moving_from_non_mapped_to_mapped(dag_maker, session):
"""
Test that clearing a task and moving from non-mapped to mapped clears existing
references in XCom, TaskInstanceNote, TaskReschedule and
RenderedTaskInstanceFields. To be able to test this, RenderedTaskInstanceFields
was not used in the test since it would require that the task is expanded first.
"""
from airflow.models.xcom import XComModel
@task
def printx(x):
print(x)
with dag_maker() as dag:
printx.expand(x=[1])
dr1: DagRun = dag_maker.create_dagrun(run_type=DagRunType.SCHEDULED)
ti = dr1.get_task_instances()[0]
filter_kwargs = dict(dag_id=ti.dag_id, task_id=ti.task_id, run_id=ti.run_id, map_index=ti.map_index)
ti = session.query(TaskInstance).filter_by(**filter_kwargs).one()
tr = TaskReschedule(
ti_id=ti.id,
start_date=timezone.datetime(2017, 1, 1),
end_date=timezone.datetime(2017, 1, 2),
reschedule_date=timezone.datetime(2017, 1, 1),
)
# mimicking a case where task moved from non-mapped to mapped
# in that case, it would have map_index of -1 even though mapped
ti.map_index = -1
ti.note = "sample note"
session.merge(ti)
session.flush()
# Purposely omitted RenderedTaskInstanceFields because the ti need
# to be expanded but here we are mimicking and made it map_index -1
session.add(tr)
XComModel.set(key="test", value="value", task_id=ti.task_id, dag_id=dag.dag_id, run_id=ti.run_id)
session.commit()
for table in [TaskInstanceNote, TaskReschedule, XComModel]:
assert session.query(table).count() == 1
dr1.task_instance_scheduling_decisions(session)
for table in [TaskInstanceNote, TaskReschedule, XComModel]:
assert session.query(table).count() == 0
def test_dagrun_with_note(dag_maker, session):
with dag_maker():
@task
def the_task():
print("Hi")
the_task()
dr: DagRun = dag_maker.create_dagrun()
dr.note = "dag run with note"
session.add(dr)
session.commit()
dr_note = session.query(DagRunNote).filter(DagRunNote.dag_run_id == dr.id).one()
assert dr_note.content == "dag run with note"
session.delete(dr)
session.commit()
assert session.query(DagRun).filter(DagRun.id == dr.id).one_or_none() is None
assert session.query(DagRunNote).filter(DagRunNote.dag_run_id == dr.id).one_or_none() is None
@pytest.mark.parametrize(
"dag_run_state, on_failure_fail_dagrun", [[DagRunState.SUCCESS, False], [DagRunState.FAILED, True]]
)
def test_teardown_failure_behaviour_on_dagrun(dag_maker, session, dag_run_state, on_failure_fail_dagrun):
with dag_maker():
@teardown(on_failure_fail_dagrun=on_failure_fail_dagrun)
def teardowntask():
print(1)
@task
def mytask():
print(1)
mytask() >> teardowntask()
dr = dag_maker.create_dagrun()
ti1 = dr.get_task_instance(task_id="mytask")
td1 = dr.get_task_instance(task_id="teardowntask")
ti1.state = State.SUCCESS
td1.state = State.FAILED
session.merge(ti1)
session.merge(td1)
session.flush()
dr.update_state()
session.flush()
dr = session.query(DagRun).one()
assert dr.state == dag_run_state
@pytest.mark.parametrize(
"dag_run_state, on_failure_fail_dagrun", [[DagRunState.SUCCESS, False], [DagRunState.FAILED, True]]
)
def test_teardown_failure_on_non_leaf_behaviour_on_dagrun(
dag_maker, session, dag_run_state, on_failure_fail_dagrun
):
with dag_maker():
@teardown(on_failure_fail_dagrun=on_failure_fail_dagrun)
def teardowntask():
print(1)
@teardown
def teardowntask2():
print(1)
@task
def mytask():
print(1)
mytask() >> teardowntask() >> teardowntask2()
dr = dag_maker.create_dagrun()
ti1 = dr.get_task_instance(task_id="mytask")
td1 = dr.get_task_instance(task_id="teardowntask")
td2 = dr.get_task_instance(task_id="teardowntask2")
ti1.state = State.SUCCESS
td1.state = State.FAILED
td2.state = State.FAILED
session.merge(ti1)
session.merge(td1)
session.merge(td2)
session.flush()
dr.update_state()
session.flush()
dr = session.query(DagRun).one()
assert dr.state == dag_run_state
def test_work_task_failure_when_setup_teardown_are_successful(dag_maker, session):
with dag_maker():
@setup
def setuptask():
print(2)
@teardown
def teardown_task():
print(1)
@task
def mytask():
print(1)
with setuptask() >> teardown_task():
mytask()
dr = dag_maker.create_dagrun()
s1 = dr.get_task_instance(task_id="setuptask")
td1 = dr.get_task_instance(task_id="teardown_task")
t1 = dr.get_task_instance(task_id="mytask")
s1.state = TaskInstanceState.SUCCESS
td1.state = TaskInstanceState.SUCCESS
t1.state = TaskInstanceState.FAILED
session.merge(s1)
session.merge(td1)
session.merge(t1)
session.flush()
dr.update_state()
session.flush()
dr = session.query(DagRun).one()
assert dr.state == DagRunState.FAILED
def test_failure_of_leaf_task_not_connected_to_teardown_task(dag_maker, session):
with dag_maker():
@setup
def setuptask():
print(2)
@teardown
def teardown_task():
print(1)
@task
def mytask():
print(1)
setuptask()
teardown_task()
mytask()
dr = dag_maker.create_dagrun()
s1 = dr.get_task_instance(task_id="setuptask")
td1 = dr.get_task_instance(task_id="teardown_task")
t1 = dr.get_task_instance(task_id="mytask")
s1.state = TaskInstanceState.SUCCESS
td1.state = TaskInstanceState.SUCCESS
t1.state = TaskInstanceState.FAILED
session.merge(s1)
session.merge(td1)
session.merge(t1)
session.flush()
dr.update_state()
session.flush()
dr = session.query(DagRun).one()
assert dr.state == DagRunState.FAILED
@pytest.mark.parametrize(
"input, expected",
[
(["s1 >> w1 >> t1"], {"w1"}), # t1 ignored
(["s1 >> w1 >> t1", "s1 >> t1"], {"w1"}), # t1 ignored; properly wired to setup
(["s1 >> w1"], {"w1"}), # no teardown
(["s1 >> w1 >> t1_"], {"t1_"}), # t1_ is natural leaf and OFFD=True;
(["s1 >> w1 >> t1_", "s1 >> t1_"], {"t1_"}), # t1_ is natural leaf and OFFD=True; wired to setup
(["s1 >> w1 >> t1_ >> w2", "s1 >> t1_"], {"w2"}), # t1_ is not a natural leaf so excluded anyway
(["t1 >> t2"], {"t2"}), # all teardowns -- default to "leaves"
(["w1 >> t1_ >> t2"], {"t1_"}), # teardown to teardown
],
)
def test_tis_considered_for_state(dag_maker, session, input, expected):
"""
We use a convenience notation to wire up test scenarios:
t<num> -- teardown task
t<num>_ -- teardown task with on_failure_fail_dagrun = True
s<num> -- setup task
w<num> -- work task (a.k.a. normal task)
In the test input, each line is a statement. We'll automatically create the tasks and wire them up
as indicated in the test input.
"""
@teardown
def teardown_task():
print(1)
@task
def work_task():
print(1)
@setup
def setup_task():
print(1)
def make_task(task_id, dag):
"""
Task factory helper.
Will give a setup, teardown, work, or teardown-with-dagrun-failure task depending on input.
"""
if task_id.startswith("s"):
factory = setup_task
elif task_id.startswith("w"):
factory = work_task
elif task_id.endswith("_"):
factory = teardown_task.override(on_failure_fail_dagrun=True)
else:
factory = teardown_task
return dag.task_dict.get(task_id) or factory.override(task_id=task_id)()
with dag_maker() as dag:
for line in input:
tasks = [make_task(x, dag_maker.dag) for x in line.split(" >> ")]
reduce(lambda x, y: x >> y, tasks)
dr = dag_maker.create_dagrun()
tis = dr.task_instance_scheduling_decisions(session).tis
tis_for_state = {x.task_id for x in dr._tis_for_dagrun_state(dag=dag, tis=tis)}
assert tis_for_state == expected
@pytest.mark.parametrize(
"pattern, run_id, result",
[
["^[A-Z]", "ABC", True],
["^[A-Z]", "abc", False],
["^[0-9]", "123", True],
# The below params tests that user configuration does not affect internally generated
# run_ids
["", "scheduled__2023-01-01T00:00:00+00:00", True],
["", "manual__2023-01-01T00:00:00+00:00", True],
["", "asset_triggered__2023-01-01T00:00:00+00:00", True],
["", "scheduled_2023-01-01T00", False],
["", "manual_2023-01-01T00", False],
["", "asset_triggered_2023-01-01T00", False],
["^[0-9]", "scheduled__2023-01-01T00:00:00+00:00", True],
["^[0-9]", "manual__2023-01-01T00:00:00+00:00", True],
["^[a-z]", "asset_triggered__2023-01-01T00:00:00+00:00", True],
],
)
def test_dag_run_id_config(session, dag_maker, pattern, run_id, result):
with conf_vars({("scheduler", "allowed_run_id_pattern"): pattern}):
with dag_maker():
pass
run_type = DagRunType.from_run_id(run_id)
if result:
dag_maker.create_dagrun(run_id=run_id, run_type=run_type)
else:
with pytest.raises(ValueError, match=r"The run_id provided '.+' does not match regex pattern"):
dag_maker.create_dagrun(run_id=run_id, run_type=run_type)
def _get_states(dr):
"""
For a given dag run, get a dict of states.
Example::
{
"my_setup": "success",
"my_teardown": {0: "success", 1: "success", 2: "success"},
"my_work": "failed",
}
"""
ti_dict = defaultdict(dict)
for ti in dr.get_task_instances():
if ti.map_index == -1:
ti_dict[ti.task_id] = ti.state
else:
ti_dict[ti.task_id][ti.map_index] = ti.state
return dict(ti_dict)
@pytest.mark.db_test
@pytest.mark.need_serialized_dag(False)
def test_teardown_and_fail_fast(dag_maker):
"""
when fail_fast enabled, teardowns should run according to their setups.
in this case, the second teardown skips because its setup skips.
"""
from airflow.sdk import task as task_decorator
from airflow.sdk.definitions.taskgroup import TaskGroup
with dag_maker(fail_fast=True) as dag:
for num in (1, 2):
with TaskGroup(f"tg_{num}"):
@task_decorator
def my_setup():
print("setting up multiple things")
return [1, 2, 3]
@task_decorator
def my_work(val):
print(f"doing work with multiple things: {val}")
raise ValueError("this fails")
return val
@task_decorator
def my_teardown():
print("teardown")
s = my_setup()
t = my_teardown().as_teardown(setups=s)
with t:
my_work(s)
tg1, tg2 = dag.task_group.children.values()
tg1 >> tg2
dr = dag.test()
states = _get_states(dr)
assert states == {
"tg_1.my_setup": "success",
"tg_1.my_teardown": "success",
"tg_1.my_work": "failed",
"tg_2.my_setup": "skipped",
"tg_2.my_teardown": "skipped",
"tg_2.my_work": "skipped",
}
class TestDagRunGetLastTi:
def test_get_last_ti_with_multiple_tis(self, dag_maker, session):
"""Test get_last_ti returns the last TI (first created) when multiple TIs exist"""
with dag_maker("test_dag", session=session) as dag:
BashOperator(task_id="task1", bash_command="echo 1")
BashOperator(task_id="task2", bash_command="echo 2")
BashOperator(task_id="task3", bash_command="echo 3")
dr = dag_maker.create_dagrun()
tis = dr.get_task_instances(session=session)
assert len(tis) == 3
# Mark some TIs with different states
tis[0].state = TaskInstanceState.SUCCESS
tis[1].state = TaskInstanceState.FAILED
tis[2].state = TaskInstanceState.RUNNING
session.commit()
last_ti = dr.get_last_ti(dag, session=session)
# Should return the last TI in the list (index -1)
assert last_ti is not None
assert last_ti == tis[-1]
assert last_ti.task_id == "task3"
def test_get_last_ti_filters_none_state_in_partial_dag(self, dag_maker, session):
"""Test get_last_ti filters out NONE state TIs when dag is partial"""
with dag_maker("test_dag", session=session) as dag:
BashOperator(task_id="task1", bash_command="echo 1")
BashOperator(task_id="task2", bash_command="echo 2")
dr = dag_maker.create_dagrun()
dag.partial = True
# Create task instances with different states
tis = dr.get_task_instances(session=session)
tis[0].state = State.NONE # Should be filtered out in partial DAG
tis[1].state = TaskInstanceState.RUNNING
session.commit()
last_ti = dr.get_last_ti(dag, session=session)
assert last_ti is not None
assert last_ti.state != State.NONE
assert last_ti.task_id == "task2"
def test_get_last_ti_filters_removed_tasks(self, dag_maker, session):
"""Test get_last_ti filters out REMOVED task instances"""
with dag_maker("test_dag", session=session) as dag:
BashOperator(task_id="task1", bash_command="echo 1")
BashOperator(task_id="task2", bash_command="echo 2")
BashOperator(task_id="task3", bash_command="echo 3")
dr = dag_maker.create_dagrun()
tis = dr.get_task_instances(session=session)
# Mark some TIs as removed
tis[0].state = TaskInstanceState.REMOVED
tis[1].state = TaskInstanceState.REMOVED
tis[2].state = TaskInstanceState.SUCCESS
session.commit()
last_ti = dr.get_last_ti(dag, session=session)
# Should return the TI that is not REMOVED
assert last_ti is not None
assert last_ti.state != TaskInstanceState.REMOVED
assert last_ti.task_id == "task3"
def test_get_last_ti_with_single_ti(self, dag_maker, session):
"""Test get_last_ti works with single task instance"""
with dag_maker("test_dag", session=session) as dag:
BashOperator(task_id="single_task", bash_command="echo 1")
dr = dag_maker.create_dagrun()
tis = dr.get_task_instances(session=session)
assert len(tis) == 1
last_ti = dr.get_last_ti(dag, session=session)
assert last_ti is not None
assert last_ti == tis[0]
assert last_ti.task_id == "single_task"
class TestDagRunHandleDagCallback:
"""Test the handle_dag_callback method (only uses in dag.test)."""
def test_handle_dag_callback_success(self, dag_maker, session):
"""Test handle_dag_callback executes success callback with RuntimeTaskInstance context"""
called = False
context_received = None
def on_success(context):
nonlocal called, context_received
called = True
context_received = context
with dag_maker("test_dag", session=session, on_success_callback=on_success) as dag:
BashOperator(task_id="test_task", bash_command="echo 1")
dr = dag_maker.create_dagrun()
dag.on_success_callback = on_success
dag.has_on_success_callback = True
dr.handle_dag_callback(dag, success=True, reason="test_success")
assert called is True
assert context_received is not None
# Should have RuntimeTaskInstance context with template variables
assert "dag_run" in context_received
assert "logical_date" in context_received
assert "reason" in context_received
assert context_received["reason"] == "test_success"
assert "ts" in context_received
assert "params" in context_received
def test_handle_dag_callback_failure(self, dag_maker, session):
"""Test handle_dag_callback executes failure callback with RuntimeTaskInstance context"""
called = False
context_received = None
def on_failure(context):
nonlocal called, context_received
called = True
context_received = context
with dag_maker("test_dag", session=session, on_failure_callback=on_failure) as dag:
BashOperator(task_id="test_task", bash_command="echo 1")
dr = dag_maker.create_dagrun()
dag.on_failure_callback = on_failure
dag.has_on_failure_callback = True
dr.handle_dag_callback(dag, success=False, reason="test_failure")
assert called is True
assert context_received is not None
# Should have RuntimeTaskInstance context with template variables
assert "dag_run" in context_received
assert "logical_date" in context_received
assert "reason" in context_received
assert context_received["reason"] == "test_failure"
assert "ts" in context_received
assert "params" in context_received
def test_handle_dag_callback_multiple_callbacks(self, dag_maker, session):
"""Test handle_dag_callback executes multiple callbacks"""
call_count = 0
def on_failure_1(context):
nonlocal call_count
call_count += 1
def on_failure_2(context):
nonlocal call_count
call_count += 1
with dag_maker("test_dag", session=session, on_failure_callback=[on_failure_1, on_failure_2]) as dag:
BashOperator(task_id="test_task", bash_command="echo 1")
dr = dag_maker.create_dagrun()
dag.on_failure_callback = [on_failure_1, on_failure_2]
dag.has_on_failure_callback = True
dr.handle_dag_callback(dag, success=False, reason="test_failure")
assert call_count == 2
def test_handle_dag_callback_context_has_correct_ti_info(self, dag_maker, session):
"""Test handle_dag_callback context contains correct task instance information"""
context_received = None
def on_failure(context):
nonlocal context_received
context_received = context
with dag_maker("test_dag", session=session, on_failure_callback=on_failure) as dag:
BashOperator(task_id="test_task", bash_command="echo 1", retries=2)
dr = dag_maker.create_dagrun()
dag.on_failure_callback = on_failure
dag.has_on_failure_callback = True
dr.handle_dag_callback(dag, success=False, reason="test_failure")
assert context_received is not None
# Check that context contains correct task info
assert context_received["ti"].task_id == "test_task"
assert context_received["ti"].dag_id == "test_dag"
assert context_received["ti"].run_id == dr.run_id