test_dag.py

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from __future__ import annotations

import datetime
import io
import logging
import os
import pickle
import re
from contextlib import redirect_stdout
from datetime import timedelta
from pathlib import Path
from tempfile import NamedTemporaryFile
from unittest import mock
from unittest.mock import patch

import jinja2
import pendulum
import pytest
from dateutil.relativedelta import relativedelta
from freezegun import freeze_time
from sqlalchemy import inspect

import airflow
from airflow import models, settings
from airflow.configuration import conf
from airflow.datasets import Dataset
from airflow.decorators import task as task_decorator
from airflow.exceptions import AirflowException, DuplicateTaskIdFound, ParamValidationError
from airflow.models import DAG, DagModel, DagRun, DagTag, TaskFail, TaskInstance as TI
from airflow.models.baseoperator import BaseOperator
from airflow.models.dag import DagOwnerAttributes, dag as dag_decorator, get_dataset_triggered_next_run_info
from airflow.models.dataset import DatasetDagRunQueue, DatasetEvent, DatasetModel, TaskOutletDatasetReference
from airflow.models.param import DagParam, Param, ParamsDict
from airflow.models.serialized_dag import SerializedDagModel
from airflow.operators.bash import BashOperator
from airflow.operators.empty import EmptyOperator
from airflow.operators.python import PythonOperator
from airflow.operators.subdag import SubDagOperator
from airflow.security import permissions
from airflow.templates import NativeEnvironment, SandboxedEnvironment
from airflow.timetables.base import DagRunInfo, DataInterval, TimeRestriction, Timetable
from airflow.timetables.simple import DatasetTriggeredTimetable, NullTimetable, OnceTimetable
from airflow.utils import timezone
from airflow.utils.file import list_py_file_paths
from airflow.utils.session import create_session, provide_session
from airflow.utils.state import DagRunState, State, TaskInstanceState
from airflow.utils.task_group import TaskGroup, TaskGroupContext
from airflow.utils.timezone import datetime as datetime_tz
from airflow.utils.types import DagRunType
from airflow.utils.weight_rule import WeightRule
from tests.models import DEFAULT_DATE
from tests.test_utils.asserts import assert_queries_count
from tests.test_utils.config import conf_vars
from tests.test_utils.db import clear_db_dags, clear_db_datasets, clear_db_runs, clear_db_serialized_dags
from tests.test_utils.mapping import expand_mapped_task
from tests.test_utils.timetables import cron_timetable, delta_timetable

TEST_DATE = datetime_tz(2015, 1, 2, 0, 0)

repo_root = Path(airflow.__file__).parent.parent


@pytest.fixture
def clear_dags():
    clear_db_dags()
    clear_db_serialized_dags()
    yield
    clear_db_dags()
    clear_db_serialized_dags()


class TestDag:
    def setup_method(self) -> None:
        clear_db_runs()
        clear_db_dags()
        self.patcher_dag_code = mock.patch('airflow.models.dag.DagCode.bulk_sync_to_db')
        self.patcher_dag_code.start()

    def teardown_method(self) -> None:
        clear_db_runs()
        clear_db_dags()
        self.patcher_dag_code.stop()

    @staticmethod
    def _clean_up(dag_id: str):
        with create_session() as session:
            session.query(DagRun).filter(DagRun.dag_id == dag_id).delete(synchronize_session=False)
            session.query(TI).filter(TI.dag_id == dag_id).delete(synchronize_session=False)
            session.query(TaskFail).filter(TaskFail.dag_id == dag_id).delete(synchronize_session=False)

    @staticmethod
    def _occur_before(a, b, list_):
        """
        Assert that a occurs before b in the list.
        """
        a_index = -1
        b_index = -1
        for i, e in enumerate(list_):
            if e.task_id == a:
                a_index = i
            if e.task_id == b:
                b_index = i
        return 0 <= a_index < b_index

    def test_params_not_passed_is_empty_dict(self):
        """
        Test that when 'params' is _not_ passed to a new Dag, that the params
        attribute is set to an empty dictionary.
        """
        dag = models.DAG('test-dag')

        assert isinstance(dag.params, ParamsDict)
        assert 0 == len(dag.params)

    def test_params_passed_and_params_in_default_args_no_override(self):
        """
        Test that when 'params' exists as a key passed to the default_args dict
        in addition to params being passed explicitly as an argument to the
        dag, that the 'params' key of the default_args dict is merged with the
        dict of the params argument.
        """
        params1 = {'parameter1': 1}
        params2 = {'parameter2': 2}

        dag = models.DAG('test-dag', default_args={'params': params1}, params=params2)

        assert params1['parameter1'] == dag.params['parameter1']
        assert params2['parameter2'] == dag.params['parameter2']

    def test_not_none_schedule_with_non_default_params(self):
        """
        Test if there is a DAG with not None schedule_interval and have some params that
        don't have a default value raise a error while DAG parsing
        """
        params = {'param1': Param(type="string")}

        with pytest.raises(AirflowException):
            models.DAG('dummy-dag', params=params)

    def test_dag_invalid_default_view(self):
        """
        Test invalid `default_view` of DAG initialization
        """
        with pytest.raises(AirflowException, match='Invalid values of dag.default_view: only support'):
            models.DAG(dag_id='test-invalid-default_view', default_view='airflow')

    def test_dag_default_view_default_value(self):
        """
        Test `default_view` default value of DAG initialization
        """
        dag = models.DAG(dag_id='test-default_default_view')
        assert conf.get('webserver', 'dag_default_view').lower() == dag.default_view

    def test_dag_invalid_orientation(self):
        """
        Test invalid `orientation` of DAG initialization
        """
        with pytest.raises(AirflowException, match='Invalid values of dag.orientation: only support'):
            models.DAG(dag_id='test-invalid-orientation', orientation='airflow')

    def test_dag_orientation_default_value(self):
        """
        Test `orientation` default value of DAG initialization
        """
        dag = models.DAG(dag_id='test-default_orientation')
        assert conf.get('webserver', 'dag_orientation') == dag.orientation

    def test_dag_as_context_manager(self):
        """
        Test DAG as a context manager.
        When used as a context manager, Operators are automatically added to
        the DAG (unless they specify a different DAG)
        """
        dag = DAG('dag', start_date=DEFAULT_DATE, default_args={'owner': 'owner1'})
        dag2 = DAG('dag2', start_date=DEFAULT_DATE, default_args={'owner': 'owner2'})

        with dag:
            op1 = EmptyOperator(task_id='op1')
            op2 = EmptyOperator(task_id='op2', dag=dag2)

        assert op1.dag is dag
        assert op1.owner == 'owner1'
        assert op2.dag is dag2
        assert op2.owner == 'owner2'

        with dag2:
            op3 = EmptyOperator(task_id='op3')

        assert op3.dag is dag2
        assert op3.owner == 'owner2'

        with dag:
            with dag2:
                op4 = EmptyOperator(task_id='op4')
            op5 = EmptyOperator(task_id='op5')

        assert op4.dag is dag2
        assert op5.dag is dag
        assert op4.owner == 'owner2'
        assert op5.owner == 'owner1'

        with DAG('creating_dag_in_cm', start_date=DEFAULT_DATE) as dag:
            EmptyOperator(task_id='op6')

        assert dag.dag_id == 'creating_dag_in_cm'
        assert dag.tasks[0].task_id == 'op6'

        with dag:
            with dag:
                op7 = EmptyOperator(task_id='op7')
            op8 = EmptyOperator(task_id='op8')
        op9 = EmptyOperator(task_id='op8')
        op9.dag = dag2

        assert op7.dag == dag
        assert op8.dag == dag
        assert op9.dag == dag2

    def test_dag_topological_sort_include_subdag_tasks(self):
        child_dag = DAG(
            'parent_dag.child_dag',
            schedule='@daily',
            start_date=DEFAULT_DATE,
        )

        with child_dag:
            EmptyOperator(task_id='a_child')
            EmptyOperator(task_id='b_child')

        parent_dag = DAG(
            'parent_dag',
            schedule='@daily',
            start_date=DEFAULT_DATE,
        )

        # a_parent -> child_dag -> (a_child | b_child) -> b_parent
        with parent_dag:
            op1 = EmptyOperator(task_id='a_parent')
            op2 = SubDagOperator(task_id='child_dag', subdag=child_dag)
            op3 = EmptyOperator(task_id='b_parent')

            op1 >> op2 >> op3

        topological_list = parent_dag.topological_sort(include_subdag_tasks=True)

        assert self._occur_before('a_parent', 'child_dag', topological_list)
        assert self._occur_before('child_dag', 'a_child', topological_list)
        assert self._occur_before('child_dag', 'b_child', topological_list)
        assert self._occur_before('a_child', 'b_parent', topological_list)
        assert self._occur_before('b_child', 'b_parent', topological_list)

    def test_dag_topological_sort_dag_without_tasks(self):
        dag = DAG('dag', start_date=DEFAULT_DATE, default_args={'owner': 'owner1'})

        assert () == dag.topological_sort()

    def test_dag_naive_start_date_string(self):
        DAG('DAG', default_args={'start_date': '2019-06-01'})

    def test_dag_naive_start_end_dates_strings(self):
        DAG('DAG', default_args={'start_date': '2019-06-01', 'end_date': '2019-06-05'})

    def test_dag_start_date_propagates_to_end_date(self):
        """
        Tests that a start_date string with a timezone and an end_date string without a timezone
        are accepted and that the timezone from the start carries over the end

        This test is a little indirect, it works by setting start and end equal except for the
        timezone and then testing for equality after the DAG construction.  They'll be equal
        only if the same timezone was applied to both.

        An explicit check the `tzinfo` attributes for both are the same is an extra check.
        """
        dag = DAG(
            'DAG', default_args={'start_date': '2019-06-05T00:00:00+05:00', 'end_date': '2019-06-05T00:00:00'}
        )
        assert dag.default_args['start_date'] == dag.default_args['end_date']
        assert dag.default_args['start_date'].tzinfo == dag.default_args['end_date'].tzinfo

    def test_dag_naive_default_args_start_date(self):
        dag = DAG('DAG', default_args={'start_date': datetime.datetime(2018, 1, 1)})
        assert dag.timezone == settings.TIMEZONE
        dag = DAG('DAG', start_date=datetime.datetime(2018, 1, 1))
        assert dag.timezone == settings.TIMEZONE

    def test_dag_none_default_args_start_date(self):
        """
        Tests if a start_date of None in default_args
        works.
        """
        dag = DAG('DAG', default_args={'start_date': None})
        assert dag.timezone == settings.TIMEZONE

    def test_dag_task_priority_weight_total(self):
        width = 5
        depth = 5
        weight = 5
        pattern = re.compile('stage(\\d*).(\\d*)')
        # Fully connected parallel tasks. i.e. every task at each parallel
        # stage is dependent on every task in the previous stage.
        # Default weight should be calculated using downstream descendants
        with DAG('dag', start_date=DEFAULT_DATE, default_args={'owner': 'owner1'}) as dag:
            pipeline = [
                [EmptyOperator(task_id=f'stage{i}.{j}', priority_weight=weight) for j in range(0, width)]
                for i in range(0, depth)
            ]
            for i, stage in enumerate(pipeline):
                if i == 0:
                    continue
                for current_task in stage:
                    for prev_task in pipeline[i - 1]:
                        current_task.set_upstream(prev_task)

            for task in dag.task_dict.values():
                match = pattern.match(task.task_id)
                task_depth = int(match.group(1))
                # the sum of each stages after this task + itself
                correct_weight = ((depth - (task_depth + 1)) * width + 1) * weight

                calculated_weight = task.priority_weight_total
                assert calculated_weight == correct_weight

    def test_dag_task_priority_weight_total_using_upstream(self):
        # Same test as above except use 'upstream' for weight calculation
        weight = 3
        width = 5
        depth = 5
        pattern = re.compile('stage(\\d*).(\\d*)')
        with DAG('dag', start_date=DEFAULT_DATE, default_args={'owner': 'owner1'}) as dag:
            pipeline = [
                [
                    EmptyOperator(
                        task_id=f'stage{i}.{j}',
                        priority_weight=weight,
                        weight_rule=WeightRule.UPSTREAM,
                    )
                    for j in range(0, width)
                ]
                for i in range(0, depth)
            ]
            for i, stage in enumerate(pipeline):
                if i == 0:
                    continue
                for current_task in stage:
                    for prev_task in pipeline[i - 1]:
                        current_task.set_upstream(prev_task)

            for task in dag.task_dict.values():
                match = pattern.match(task.task_id)
                task_depth = int(match.group(1))
                # the sum of each stages after this task + itself
                correct_weight = (task_depth * width + 1) * weight

                calculated_weight = task.priority_weight_total
                assert calculated_weight == correct_weight

    def test_dag_task_priority_weight_total_using_absolute(self):
        # Same test as above except use 'absolute' for weight calculation
        weight = 10
        width = 5
        depth = 5
        with DAG('dag', start_date=DEFAULT_DATE, default_args={'owner': 'owner1'}) as dag:
            pipeline = [
                [
                    EmptyOperator(
                        task_id=f'stage{i}.{j}',
                        priority_weight=weight,
                        weight_rule=WeightRule.ABSOLUTE,
                    )
                    for j in range(0, width)
                ]
                for i in range(0, depth)
            ]
            for i, stage in enumerate(pipeline):
                if i == 0:
                    continue
                for current_task in stage:
                    for prev_task in pipeline[i - 1]:
                        current_task.set_upstream(prev_task)

            for task in dag.task_dict.values():
                # the sum of each stages after this task + itself
                correct_weight = weight
                calculated_weight = task.priority_weight_total
                assert calculated_weight == correct_weight

    def test_dag_task_invalid_weight_rule(self):
        # Test if we enter an invalid weight rule
        with DAG('dag', start_date=DEFAULT_DATE, default_args={'owner': 'owner1'}):
            with pytest.raises(AirflowException):
                EmptyOperator(task_id='should_fail', weight_rule='no rule')

    def test_get_num_task_instances(self):
        test_dag_id = 'test_get_num_task_instances_dag'
        test_task_id = 'task_1'

        test_dag = DAG(dag_id=test_dag_id, start_date=DEFAULT_DATE)
        test_task = EmptyOperator(task_id=test_task_id, dag=test_dag)

        dr1 = test_dag.create_dagrun(state=None, run_id="test1", execution_date=DEFAULT_DATE)
        dr2 = test_dag.create_dagrun(
            state=None, run_id="test2", execution_date=DEFAULT_DATE + datetime.timedelta(days=1)
        )
        dr3 = test_dag.create_dagrun(
            state=None, run_id="test3", execution_date=DEFAULT_DATE + datetime.timedelta(days=2)
        )
        dr4 = test_dag.create_dagrun(
            state=None, run_id="test4", execution_date=DEFAULT_DATE + datetime.timedelta(days=3)
        )

        ti1 = TI(task=test_task, run_id=dr1.run_id)
        ti1.state = None
        ti2 = TI(task=test_task, run_id=dr2.run_id)
        ti2.state = State.RUNNING
        ti3 = TI(task=test_task, run_id=dr3.run_id)
        ti3.state = State.QUEUED
        ti4 = TI(task=test_task, run_id=dr4.run_id)
        ti4.state = State.RUNNING
        session = settings.Session()
        session.merge(ti1)
        session.merge(ti2)
        session.merge(ti3)
        session.merge(ti4)
        session.commit()

        assert 0 == DAG.get_num_task_instances(test_dag_id, ['fakename'], session=session)
        assert 4 == DAG.get_num_task_instances(test_dag_id, [test_task_id], session=session)
        assert 4 == DAG.get_num_task_instances(test_dag_id, ['fakename', test_task_id], session=session)
        assert 1 == DAG.get_num_task_instances(test_dag_id, [test_task_id], states=[None], session=session)
        assert 2 == DAG.get_num_task_instances(
            test_dag_id, [test_task_id], states=[State.RUNNING], session=session
        )
        assert 3 == DAG.get_num_task_instances(
            test_dag_id, [test_task_id], states=[None, State.RUNNING], session=session
        )
        assert 4 == DAG.get_num_task_instances(
            test_dag_id, [test_task_id], states=[None, State.QUEUED, State.RUNNING], session=session
        )
        session.close()

    def test_user_defined_filters_macros(self):
        def jinja_udf(name):
            return f'Hello {name}'

        dag = models.DAG(
            'test-dag',
            start_date=DEFAULT_DATE,
            user_defined_filters={"hello": jinja_udf},
            user_defined_macros={"foo": "bar"},
        )
        jinja_env = dag.get_template_env()

        assert 'hello' in jinja_env.filters
        assert jinja_env.filters['hello'] == jinja_udf
        assert jinja_env.globals['foo'] == 'bar'

    def test_set_jinja_env_additional_option(self):
        dag = DAG("test-dag", jinja_environment_kwargs={'keep_trailing_newline': True, 'cache_size': 50})
        jinja_env = dag.get_template_env()
        assert jinja_env.keep_trailing_newline is True
        assert jinja_env.cache.capacity == 50

        assert jinja_env.undefined is jinja2.StrictUndefined

    def test_template_undefined(self):
        dag = DAG("test-dag", template_undefined=jinja2.Undefined)
        jinja_env = dag.get_template_env()
        assert jinja_env.undefined is jinja2.Undefined

    @pytest.mark.parametrize(
        "use_native_obj, force_sandboxed, expected_env",
        [
            (False, True, SandboxedEnvironment),
            (False, False, SandboxedEnvironment),
            (True, False, NativeEnvironment),
            (True, True, SandboxedEnvironment),
        ],
    )
    def test_template_env(self, use_native_obj, force_sandboxed, expected_env):
        dag = DAG("test-dag", render_template_as_native_obj=use_native_obj)
        jinja_env = dag.get_template_env(force_sandboxed=force_sandboxed)
        assert isinstance(jinja_env, expected_env)

    def test_resolve_template_files_value(self):

        with NamedTemporaryFile(suffix='.template') as f:
            f.write(b'{{ ds }}')
            f.flush()
            template_dir = os.path.dirname(f.name)
            template_file = os.path.basename(f.name)

            with DAG('test-dag', start_date=DEFAULT_DATE, template_searchpath=template_dir):
                task = EmptyOperator(task_id='op1')

            task.test_field = template_file
            task.template_fields = ('test_field',)
            task.template_ext = ('.template',)
            task.resolve_template_files()

        assert task.test_field == '{{ ds }}'

    def test_resolve_template_files_list(self):

        with NamedTemporaryFile(suffix='.template') as f:
            f.write(b'{{ ds }}')
            f.flush()
            template_dir = os.path.dirname(f.name)
            template_file = os.path.basename(f.name)

            with DAG('test-dag', start_date=DEFAULT_DATE, template_searchpath=template_dir):
                task = EmptyOperator(task_id='op1')

            task.test_field = [template_file, 'some_string']
            task.template_fields = ('test_field',)
            task.template_ext = ('.template',)
            task.resolve_template_files()

        assert task.test_field == ['{{ ds }}', 'some_string']

    def test_following_previous_schedule(self):
        """
        Make sure DST transitions are properly observed
        """
        local_tz = pendulum.timezone('Europe/Zurich')
        start = local_tz.convert(datetime.datetime(2018, 10, 28, 2, 55), dst_rule=pendulum.PRE_TRANSITION)
        assert start.isoformat() == "2018-10-28T02:55:00+02:00", "Pre-condition: start date is in DST"

        utc = timezone.convert_to_utc(start)
        assert utc.isoformat() == "2018-10-28T00:55:00+00:00", "Pre-condition: correct DST->UTC conversion"

        dag = DAG('tz_dag', start_date=start, schedule='*/5 * * * *')
        _next = dag.following_schedule(utc)
        next_local = local_tz.convert(_next)

        assert _next.isoformat() == "2018-10-28T01:00:00+00:00"
        assert next_local.isoformat() == "2018-10-28T02:00:00+01:00"

        prev = dag.previous_schedule(utc)
        prev_local = local_tz.convert(prev)

        assert prev_local.isoformat() == "2018-10-28T02:50:00+02:00"

        prev = dag.previous_schedule(_next)
        prev_local = local_tz.convert(prev)

        assert prev_local.isoformat() == "2018-10-28T02:55:00+02:00"
        assert prev == utc

    def test_following_previous_schedule_daily_dag_cest_to_cet(self):
        """
        Make sure DST transitions are properly observed
        """
        local_tz = pendulum.timezone('Europe/Zurich')
        start = local_tz.convert(datetime.datetime(2018, 10, 27, 3), dst_rule=pendulum.PRE_TRANSITION)

        utc = timezone.convert_to_utc(start)

        dag = DAG('tz_dag', start_date=start, schedule='0 3 * * *')

        prev = dag.previous_schedule(utc)
        prev_local = local_tz.convert(prev)

        assert prev_local.isoformat() == "2018-10-26T03:00:00+02:00"
        assert prev.isoformat() == "2018-10-26T01:00:00+00:00"

        _next = dag.following_schedule(utc)
        next_local = local_tz.convert(_next)

        assert next_local.isoformat() == "2018-10-28T03:00:00+01:00"
        assert _next.isoformat() == "2018-10-28T02:00:00+00:00"

        prev = dag.previous_schedule(_next)
        prev_local = local_tz.convert(prev)

        assert prev_local.isoformat() == "2018-10-27T03:00:00+02:00"
        assert prev.isoformat() == "2018-10-27T01:00:00+00:00"

    def test_following_previous_schedule_daily_dag_cet_to_cest(self):
        """
        Make sure DST transitions are properly observed
        """
        local_tz = pendulum.timezone('Europe/Zurich')
        start = local_tz.convert(datetime.datetime(2018, 3, 25, 2), dst_rule=pendulum.PRE_TRANSITION)

        utc = timezone.convert_to_utc(start)

        dag = DAG('tz_dag', start_date=start, schedule='0 3 * * *')

        prev = dag.previous_schedule(utc)
        prev_local = local_tz.convert(prev)

        assert prev_local.isoformat() == "2018-03-24T03:00:00+01:00"
        assert prev.isoformat() == "2018-03-24T02:00:00+00:00"

        _next = dag.following_schedule(utc)
        next_local = local_tz.convert(_next)

        assert next_local.isoformat() == "2018-03-25T03:00:00+02:00"
        assert _next.isoformat() == "2018-03-25T01:00:00+00:00"

        prev = dag.previous_schedule(_next)
        prev_local = local_tz.convert(prev)

        assert prev_local.isoformat() == "2018-03-24T03:00:00+01:00"
        assert prev.isoformat() == "2018-03-24T02:00:00+00:00"

    def test_following_schedule_relativedelta(self):
        """
        Tests following_schedule a dag with a relativedelta schedule
        """
        dag_id = "test_schedule_dag_relativedelta"
        delta = relativedelta(hours=+1)
        dag = DAG(dag_id=dag_id, schedule=delta)
        dag.add_task(BaseOperator(task_id="faketastic", owner='Also fake', start_date=TEST_DATE))

        _next = dag.following_schedule(TEST_DATE)
        assert _next.isoformat() == "2015-01-02T01:00:00+00:00"

        _next = dag.following_schedule(_next)
        assert _next.isoformat() == "2015-01-02T02:00:00+00:00"

    def test_following_schedule_relativedelta_with_deprecated_schedule_interval(self):
        """
        Tests following_schedule a dag with a relativedelta schedule_interval
        """
        dag_id = "test_schedule_dag_relativedelta"
        delta = relativedelta(hours=+1)
        dag = DAG(dag_id=dag_id, schedule_interval=delta)
        dag.add_task(BaseOperator(task_id="faketastic", owner='Also fake', start_date=TEST_DATE))

        _next = dag.following_schedule(TEST_DATE)
        assert _next.isoformat() == "2015-01-02T01:00:00+00:00"

        _next = dag.following_schedule(_next)
        assert _next.isoformat() == "2015-01-02T02:00:00+00:00"

    def test_following_schedule_relativedelta_with_depr_schedule_interval_decorated_dag(self):
        """
        Tests following_schedule a dag with a relativedelta schedule_interval
        using decorated dag
        """
        from airflow.decorators import dag

        dag_id = "test_schedule_dag_relativedelta"
        delta = relativedelta(hours=+1)

        @dag(dag_id=dag_id, schedule_interval=delta)
        def mydag():
            BaseOperator(task_id="faketastic", owner='Also fake', start_date=TEST_DATE)

        _dag = mydag()

        _next = _dag.following_schedule(TEST_DATE)
        assert _next.isoformat() == "2015-01-02T01:00:00+00:00"

        _next = _dag.following_schedule(_next)
        assert _next.isoformat() == "2015-01-02T02:00:00+00:00"

    def test_previous_schedule_datetime_timezone(self):
        # Check that we don't get an AttributeError 'name' for self.timezone

        start = datetime.datetime(2018, 3, 25, 2, tzinfo=datetime.timezone.utc)
        dag = DAG('tz_dag', start_date=start, schedule='@hourly')
        when = dag.previous_schedule(start)
        assert when.isoformat() == "2018-03-25T01:00:00+00:00"

    def test_following_schedule_datetime_timezone(self):
        # Check that we don't get an AttributeError 'name' for self.timezone

        start = datetime.datetime(2018, 3, 25, 2, tzinfo=datetime.timezone.utc)
        dag = DAG('tz_dag', start_date=start, schedule='@hourly')
        when = dag.following_schedule(start)
        assert when.isoformat() == "2018-03-25T03:00:00+00:00"

    def test_following_schedule_datetime_timezone_utc0530(self):
        # Check that we don't get an AttributeError 'name' for self.timezone
        class UTC0530(datetime.tzinfo):
            """tzinfo derived concrete class named "+0530" with offset of 19800"""

            # can be configured here
            _offset = datetime.timedelta(seconds=19800)
            _dst = datetime.timedelta(0)
            _name = "+0530"

            def utcoffset(self, dt):
                return self.__class__._offset

            def dst(self, dt):
                return self.__class__._dst

            def tzname(self, dt):
                return self.__class__._name

        start = datetime.datetime(2018, 3, 25, 10, tzinfo=UTC0530())
        dag = DAG('tz_dag', start_date=start, schedule='@hourly')
        when = dag.following_schedule(start)
        assert when.isoformat() == "2018-03-25T05:30:00+00:00"

    def test_dagtag_repr(self):
        clear_db_dags()
        dag = DAG('dag-test-dagtag', start_date=DEFAULT_DATE, tags=['tag-1', 'tag-2'])
        dag.sync_to_db()
        with create_session() as session:
            assert {'tag-1', 'tag-2'} == {
                repr(t) for t in session.query(DagTag).filter(DagTag.dag_id == 'dag-test-dagtag').all()
            }

    def test_bulk_write_to_db(self):
        clear_db_dags()
        dags = [DAG(f'dag-bulk-sync-{i}', start_date=DEFAULT_DATE, tags=["test-dag"]) for i in range(0, 4)]

        with assert_queries_count(5):
            DAG.bulk_write_to_db(dags)
        with create_session() as session:
            assert {'dag-bulk-sync-0', 'dag-bulk-sync-1', 'dag-bulk-sync-2', 'dag-bulk-sync-3'} == {
                row[0] for row in session.query(DagModel.dag_id).all()
            }
            assert {
                ('dag-bulk-sync-0', 'test-dag'),
                ('dag-bulk-sync-1', 'test-dag'),
                ('dag-bulk-sync-2', 'test-dag'),
                ('dag-bulk-sync-3', 'test-dag'),
            } == set(session.query(DagTag.dag_id, DagTag.name).all())

            for row in session.query(DagModel.last_parsed_time).all():
                assert row[0] is not None

        # Re-sync should do fewer queries
        with assert_queries_count(8):
            DAG.bulk_write_to_db(dags)
        with assert_queries_count(8):
            DAG.bulk_write_to_db(dags)
        # Adding tags
        for dag in dags:
            dag.tags.append("test-dag2")
        with assert_queries_count(9):
            DAG.bulk_write_to_db(dags)
        with create_session() as session:
            assert {'dag-bulk-sync-0', 'dag-bulk-sync-1', 'dag-bulk-sync-2', 'dag-bulk-sync-3'} == {
                row[0] for row in session.query(DagModel.dag_id).all()
            }
            assert {
                ('dag-bulk-sync-0', 'test-dag'),
                ('dag-bulk-sync-0', 'test-dag2'),
                ('dag-bulk-sync-1', 'test-dag'),
                ('dag-bulk-sync-1', 'test-dag2'),
                ('dag-bulk-sync-2', 'test-dag'),
                ('dag-bulk-sync-2', 'test-dag2'),
                ('dag-bulk-sync-3', 'test-dag'),
                ('dag-bulk-sync-3', 'test-dag2'),
            } == set(session.query(DagTag.dag_id, DagTag.name).all())
        # Removing tags
        for dag in dags:
            dag.tags.remove("test-dag")
        with assert_queries_count(9):
            DAG.bulk_write_to_db(dags)
        with create_session() as session:
            assert {'dag-bulk-sync-0', 'dag-bulk-sync-1', 'dag-bulk-sync-2', 'dag-bulk-sync-3'} == {
                row[0] for row in session.query(DagModel.dag_id).all()
            }
            assert {
                ('dag-bulk-sync-0', 'test-dag2'),
                ('dag-bulk-sync-1', 'test-dag2'),
                ('dag-bulk-sync-2', 'test-dag2'),
                ('dag-bulk-sync-3', 'test-dag2'),
            } == set(session.query(DagTag.dag_id, DagTag.name).all())

            for row in session.query(DagModel.last_parsed_time).all():
                assert row[0] is not None

        # Removing all tags
        for dag in dags:
            dag.tags = None
        with assert_queries_count(9):
            DAG.bulk_write_to_db(dags)
        with create_session() as session:
            assert {'dag-bulk-sync-0', 'dag-bulk-sync-1', 'dag-bulk-sync-2', 'dag-bulk-sync-3'} == {
                row[0] for row in session.query(DagModel.dag_id).all()
            }
            assert not set(session.query(DagTag.dag_id, DagTag.name).all())

            for row in session.query(DagModel.last_parsed_time).all():
                assert row[0] is not None

    @pytest.mark.parametrize("state", [DagRunState.RUNNING, DagRunState.QUEUED])
    def test_bulk_write_to_db_max_active_runs(self, state):
        """
        Test that DagModel.next_dagrun_create_after is set to NULL when the dag cannot be created due to max
        active runs being hit.
        """
        dag = DAG(dag_id='test_scheduler_verify_max_active_runs', start_date=DEFAULT_DATE)
        dag.max_active_runs = 1

        EmptyOperator(task_id='dummy', dag=dag, owner='airflow')

        session = settings.Session()
        dag.clear()
        DAG.bulk_write_to_db([dag], session=session)

        model = session.query(DagModel).get((dag.dag_id,))

        assert model.next_dagrun == DEFAULT_DATE
        assert model.next_dagrun_create_after == DEFAULT_DATE + timedelta(days=1)

        dr = dag.create_dagrun(
            state=state,
            execution_date=model.next_dagrun,
            run_type=DagRunType.SCHEDULED,
            session=session,
        )
        assert dr is not None
        DAG.bulk_write_to_db([dag])

        model = session.query(DagModel).get((dag.dag_id,))
        # We signal "at max active runs" by saying this run is never eligible to be created
        assert model.next_dagrun_create_after is None
        # test that bulk_write_to_db again doesn't update next_dagrun_create_after
        DAG.bulk_write_to_db([dag])
        model = session.query(DagModel).get((dag.dag_id,))
        assert model.next_dagrun_create_after is None

    def test_bulk_write_to_db_has_import_error(self):
        """
        Test that DagModel.has_import_error is set to false if no import errors.
        """
        dag = DAG(dag_id='test_has_import_error', start_date=DEFAULT_DATE)

        EmptyOperator(task_id='dummy', dag=dag, owner='airflow')

        session = settings.Session()
        dag.clear()
        DAG.bulk_write_to_db([dag], session=session)

        model = session.query(DagModel).get((dag.dag_id,))

        assert not model.has_import_errors

        # Simulate Dagfileprocessor setting the import error to true
        model.has_import_errors = True
        session.merge(model)
        session.flush()
        model = session.query(DagModel).get((dag.dag_id,))
        # assert
        assert model.has_import_errors
        # parse
        DAG.bulk_write_to_db([dag])

        model = session.query(DagModel).get((dag.dag_id,))
        # assert that has_import_error is now false
        assert not model.has_import_errors
        session.close()

    def test_bulk_write_to_db_datasets(self):
        """
        Ensure that datasets referenced in a dag are correctly loaded into the database.
        """
        dag_id1 = 'test_dataset_dag1'
        dag_id2 = 'test_dataset_dag2'
        task_id = 'test_dataset_task'
        uri1 = 's3://dataset1'
        d1 = Dataset(uri1, extra={"not": "used"})
        d2 = Dataset('s3://dataset2')
        d3 = Dataset('s3://dataset3')
        dag1 = DAG(dag_id=dag_id1, start_date=DEFAULT_DATE, schedule=[d1])
        EmptyOperator(task_id=task_id, dag=dag1, outlets=[d2, d3])
        dag2 = DAG(dag_id=dag_id2, start_date=DEFAULT_DATE)
        EmptyOperator(task_id=task_id, dag=dag2, outlets=[Dataset(uri1, extra={"should": "be used"})])
        session = settings.Session()
        dag1.clear()
        DAG.bulk_write_to_db([dag1, dag2], session=session)
        session.commit()
        stored_datasets = {x.uri: x for x in session.query(DatasetModel).all()}
        d1_orm = stored_datasets[d1.uri]
        d2_orm = stored_datasets[d2.uri]
        d3_orm = stored_datasets[d3.uri]
        assert stored_datasets[uri1].extra == {"should": "be used"}
        assert [x.dag_id for x in d1_orm.consuming_dags] == [dag_id1]
        assert [(x.task_id, x.dag_id) for x in d1_orm.producing_tasks] == [(task_id, dag_id2)]
        assert set(
            session.query(
                TaskOutletDatasetReference.task_id,
                TaskOutletDatasetReference.dag_id,
                TaskOutletDatasetReference.dataset_id,
            )
            .filter(TaskOutletDatasetReference.dag_id.in_((dag_id1, dag_id2)))
            .all()
        ) == {
            (task_id, dag_id1, d2_orm.id),
            (task_id, dag_id1, d3_orm.id),
            (task_id, dag_id2, d1_orm.id),
        }

        # now that we have verified that a new dag has its dataset references recorded properly,
        # we need to verify that *changes* are recorded properly.
        # so if any references are *removed*, they should also be deleted from the DB
        # so let's remove some references and see what happens
        dag1 = DAG(dag_id=dag_id1, start_date=DEFAULT_DATE, schedule=None)
        EmptyOperator(task_id=task_id, dag=dag1, outlets=[d2])
        dag2 = DAG(dag_id=dag_id2, start_date=DEFAULT_DATE)
        EmptyOperator(task_id=task_id, dag=dag2)
        DAG.bulk_write_to_db([dag1, dag2], session=session)
        session.commit()
        session.expunge_all()
        stored_datasets = {x.uri: x for x in session.query(DatasetModel).all()}
        d1_orm = stored_datasets[d1.uri]
        d2_orm = stored_datasets[d2.uri]
        assert [x.dag_id for x in d1_orm.consuming_dags] == []
        assert set(
            session.query(
                TaskOutletDatasetReference.task_id,
                TaskOutletDatasetReference.dag_id,
                TaskOutletDatasetReference.dataset_id,
            )
            .filter(TaskOutletDatasetReference.dag_id.in_((dag_id1, dag_id2)))
            .all()
        ) == {(task_id, dag_id1, d2_orm.id)}

    def test_sync_to_db(self):
        dag = DAG(
            'dag',
            start_date=DEFAULT_DATE,
        )
        with dag:
            EmptyOperator(task_id='task', owner='owner1')
            subdag = DAG(
                'dag.subtask',
                start_date=DEFAULT_DATE,
            )
            # parent_dag and is_subdag was set by DagBag. We don't use DagBag, so this value is not set.
            subdag.parent_dag = dag
            SubDagOperator(task_id='subtask', owner='owner2', subdag=subdag)
        session = settings.Session()
        dag.sync_to_db(session=session)

        orm_dag = session.query(DagModel).filter(DagModel.dag_id == 'dag').one()
        assert set(orm_dag.owners.split(', ')) == {'owner1', 'owner2'}
        assert orm_dag.is_active
        assert orm_dag.default_view is not None
        assert orm_dag.default_view == conf.get('webserver', 'dag_default_view').lower()
        assert orm_dag.safe_dag_id == 'dag'

        orm_subdag = session.query(DagModel).filter(DagModel.dag_id == 'dag.subtask').one()
        assert set(orm_subdag.owners.split(', ')) == {'owner1', 'owner2'}
        assert orm_subdag.is_active
        assert orm_subdag.safe_dag_id == 'dag__dot__subtask'
        assert orm_subdag.fileloc == orm_dag.fileloc
        session.close()

    def test_sync_to_db_default_view(self):
        dag = DAG(
            'dag',
            start_date=DEFAULT_DATE,
            default_view="graph",
        )
        with dag:
            EmptyOperator(task_id='task', owner='owner1')
            SubDagOperator(
                task_id='subtask',
                owner='owner2',
                subdag=DAG(
                    'dag.subtask',
                    start_date=DEFAULT_DATE,
                ),
            )
        session = settings.Session()
        dag.sync_to_db(session=session)

        orm_dag = session.query(DagModel).filter(DagModel.dag_id == 'dag').one()
        assert orm_dag.default_view is not None
        assert orm_dag.default_view == "graph"
        session.close()

    @provide_session
    def test_is_paused_subdag(self, session):
        subdag_id = 'dag.subdag'
        subdag = DAG(
            subdag_id,
            start_date=DEFAULT_DATE,
        )
        with subdag:
            EmptyOperator(
                task_id='dummy_task',
            )

        dag_id = 'dag'
        dag = DAG(
            dag_id,
            start_date=DEFAULT_DATE,
        )

        with dag:
            SubDagOperator(task_id='subdag', subdag=subdag)

        # parent_dag and is_subdag was set by DagBag. We don't use DagBag, so this value is not set.
        subdag.parent_dag = dag

        session.query(DagModel).filter(DagModel.dag_id.in_([subdag_id, dag_id])).delete(
            synchronize_session=False
        )

        dag.sync_to_db(session=session)

        unpaused_dags = (
            session.query(DagModel.dag_id, DagModel.is_paused)
            .filter(
                DagModel.dag_id.in_([subdag_id, dag_id]),
            )
            .all()
        )

        assert {
            (dag_id, False),
            (subdag_id, False),
        } == set(unpaused_dags)

        DagModel.get_dagmodel(dag.dag_id).set_is_paused(is_paused=True, including_subdags=False)

        paused_dags = (
            session.query(DagModel.dag_id, DagModel.is_paused)
            .filter(
                DagModel.dag_id.in_([subdag_id, dag_id]),
            )
            .all()
        )

        assert {
            (dag_id, True),
            (subdag_id, False),
        } == set(paused_dags)

        DagModel.get_dagmodel(dag.dag_id).set_is_paused(is_paused=True)

        paused_dags = (
            session.query(DagModel.dag_id, DagModel.is_paused)
            .filter(
                DagModel.dag_id.in_([subdag_id, dag_id]),
            )
            .all()
        )

        assert {
            (dag_id, True),
            (subdag_id, True),
        } == set(paused_dags)

    def test_existing_dag_is_paused_upon_creation(self):
        dag = DAG('dag_paused')
        dag.sync_to_db()
        assert not dag.get_is_paused()

        dag = DAG('dag_paused', is_paused_upon_creation=True)
        dag.sync_to_db()
        # Since the dag existed before, it should not follow the pause flag upon creation
        assert not dag.get_is_paused()

    def test_new_dag_is_paused_upon_creation(self):
        dag = DAG('new_nonexisting_dag', is_paused_upon_creation=True)
        session = settings.Session()
        dag.sync_to_db(session=session)

        orm_dag = session.query(DagModel).filter(DagModel.dag_id == 'new_nonexisting_dag').one()
        # Since the dag didn't exist before, it should follow the pause flag upon creation
        assert orm_dag.is_paused
        session.close()

    def test_existing_dag_default_view(self):

        with create_session() as session:
            session.add(DagModel(dag_id='dag_default_view_old', default_view=None))
            session.commit()
            orm_dag = session.query(DagModel).filter(DagModel.dag_id == 'dag_default_view_old').one()
        assert orm_dag.default_view is None
        assert orm_dag.get_default_view() == conf.get('webserver', 'dag_default_view').lower()

    def test_dag_is_deactivated_upon_dagfile_deletion(self):
        dag_id = 'old_existing_dag'
        dag_fileloc = "/usr/local/airflow/dags/non_existing_path.py"
        dag = DAG(
            dag_id,
            is_paused_upon_creation=True,
        )
        dag.fileloc = dag_fileloc
        session = settings.Session()
        with mock.patch('airflow.models.dag.DagCode.bulk_sync_to_db'):
            dag.sync_to_db(session=session)

        orm_dag = session.query(DagModel).filter(DagModel.dag_id == dag_id).one()

        assert orm_dag.is_active
        assert orm_dag.fileloc == dag_fileloc

        DagModel.deactivate_deleted_dags(list_py_file_paths(settings.DAGS_FOLDER))

        orm_dag = session.query(DagModel).filter(DagModel.dag_id == dag_id).one()
        assert not orm_dag.is_active

        session.execute(DagModel.__table__.delete().where(DagModel.dag_id == dag_id))
        session.close()

    def test_dag_naive_default_args_start_date_with_timezone(self):
        local_tz = pendulum.timezone('Europe/Zurich')
        default_args = {'start_date': datetime.datetime(2018, 1, 1, tzinfo=local_tz)}

        dag = DAG('DAG', default_args=default_args)
        assert dag.timezone.name == local_tz.name

        dag = DAG('DAG', default_args=default_args)
        assert dag.timezone.name == local_tz.name

    def test_roots(self):
        """Verify if dag.roots returns the root tasks of a DAG."""
        with DAG("test_dag", start_date=DEFAULT_DATE) as dag:
            op1 = EmptyOperator(task_id="t1")
            op2 = EmptyOperator(task_id="t2")
            op3 = EmptyOperator(task_id="t3")
            op4 = EmptyOperator(task_id="t4")
            op5 = EmptyOperator(task_id="t5")
            [op1, op2] >> op3 >> [op4, op5]

            assert set(dag.roots) == {op1, op2}

    def test_leaves(self):
        """Verify if dag.leaves returns the leaf tasks of a DAG."""
        with DAG("test_dag", start_date=DEFAULT_DATE) as dag:
            op1 = EmptyOperator(task_id="t1")
            op2 = EmptyOperator(task_id="t2")
            op3 = EmptyOperator(task_id="t3")
            op4 = EmptyOperator(task_id="t4")
            op5 = EmptyOperator(task_id="t5")
            [op1, op2] >> op3 >> [op4, op5]

            assert set(dag.leaves) == {op4, op5}

    def test_tree_view(self):
        """Verify correctness of dag.tree_view()."""
        with DAG("test_dag", start_date=DEFAULT_DATE) as dag:
            op1 = EmptyOperator(task_id="t1")
            op2 = EmptyOperator(task_id="t2")
            op3 = EmptyOperator(task_id="t3")
            op1 >> op2 >> op3

            with redirect_stdout(io.StringIO()) as stdout:
                dag.tree_view()
                stdout = stdout.getvalue()

            stdout_lines = stdout.split("\n")
            assert 't1' in stdout_lines[0]
            assert 't2' in stdout_lines[1]
            assert 't3' in stdout_lines[2]

    def test_duplicate_task_ids_not_allowed_with_dag_context_manager(self):
        """Verify tasks with Duplicate task_id raises error"""
        with pytest.raises(DuplicateTaskIdFound, match="Task id 't1' has already been added to the DAG"):
            with DAG("test_dag", start_date=DEFAULT_DATE) as dag:
                op1 = EmptyOperator(task_id="t1")
                op2 = BashOperator(task_id="t1", bash_command="sleep 1")
                op1 >> op2

        assert dag.task_dict == {op1.task_id: op1}

    def test_duplicate_task_ids_not_allowed_without_dag_context_manager(self):
        """Verify tasks with Duplicate task_id raises error"""
        with pytest.raises(DuplicateTaskIdFound, match="Task id 't1' has already been added to the DAG"):
            dag = DAG("test_dag", start_date=DEFAULT_DATE)
            op1 = EmptyOperator(task_id="t1", dag=dag)
            op2 = EmptyOperator(task_id="t1", dag=dag)
            op1 >> op2

        assert dag.task_dict == {op1.task_id: op1}

    def test_duplicate_task_ids_for_same_task_is_allowed(self):
        """Verify that same tasks with Duplicate task_id do not raise error"""
        with DAG("test_dag", start_date=DEFAULT_DATE) as dag:
            op1 = op2 = EmptyOperator(task_id="t1")
            op3 = EmptyOperator(task_id="t3")
            op1 >> op3
            op2 >> op3

        assert op1 == op2
        assert dag.task_dict == {op1.task_id: op1, op3.task_id: op3}
        assert dag.task_dict == {op2.task_id: op2, op3.task_id: op3}

    def test_sub_dag_updates_all_references_while_deepcopy(self):
        with DAG("test_dag", start_date=DEFAULT_DATE) as dag:
            op1 = EmptyOperator(task_id='t1')
            op2 = EmptyOperator(task_id='t2')
            op3 = EmptyOperator(task_id='t3')
            op1 >> op2
            op2 >> op3

        sub_dag = dag.partial_subset('t2', include_upstream=True, include_downstream=False)
        assert id(sub_dag.task_dict['t1'].downstream_list[0].dag) == id(sub_dag)

        # Copied DAG should not include unused task IDs in used_group_ids
        assert 't3' not in sub_dag._task_group.used_group_ids

    def test_schedule_dag_no_previous_runs(self):
        """
        Tests scheduling a dag with no previous runs
        """
        dag_id = "test_schedule_dag_no_previous_runs"
        dag = DAG(dag_id=dag_id)
        dag.add_task(BaseOperator(task_id="faketastic", owner='Also fake', start_date=TEST_DATE))

        dag_run = dag.create_dagrun(
            run_type=DagRunType.SCHEDULED,
            execution_date=TEST_DATE,
            state=State.RUNNING,
        )
        assert dag_run is not None
        assert dag.dag_id == dag_run.dag_id
        assert dag_run.run_id is not None
        assert '' != dag_run.run_id
        assert (
            TEST_DATE == dag_run.execution_date
        ), f'dag_run.execution_date did not match expectation: {dag_run.execution_date}'
        assert State.RUNNING == dag_run.state
        assert not dag_run.external_trigger
        dag.clear()
        self._clean_up(dag_id)

    @patch('airflow.models.dag.Stats')
    def test_dag_handle_callback_crash(self, mock_stats):
        """
        Tests avoid crashes from calling dag callbacks exceptions
        """
        dag_id = "test_dag_callback_crash"
        mock_callback_with_exception = mock.MagicMock()
        mock_callback_with_exception.side_effect = Exception
        dag = DAG(
            dag_id=dag_id,
            # callback with invalid signature should not cause crashes
            on_success_callback=lambda: 1,
            on_failure_callback=mock_callback_with_exception,
        )
        when = TEST_DATE
        dag.add_task(BaseOperator(task_id="faketastic", owner='Also fake', start_date=when))

        with create_session() as session:
            dag_run = dag.create_dagrun(State.RUNNING, when, run_type=DagRunType.MANUAL, session=session)

            # should not raise any exception
            dag.handle_callback(dag_run, success=False)
            dag.handle_callback(dag_run, success=True)

        mock_stats.incr.assert_called_with("dag.callback_exceptions")

        dag.clear()
        self._clean_up(dag_id)

    def test_next_dagrun_after_fake_scheduled_previous(self):
        """
        Test scheduling a dag where there is a prior DagRun
        which has the same run_id as the next run should have
        """
        delta = datetime.timedelta(hours=1)
        dag_id = "test_schedule_dag_fake_scheduled_previous"
        dag = DAG(dag_id=dag_id, schedule=delta, start_date=DEFAULT_DATE)
        dag.add_task(BaseOperator(task_id="faketastic", owner='Also fake', start_date=DEFAULT_DATE))

        dag.create_dagrun(
            run_type=DagRunType.SCHEDULED,
            execution_date=DEFAULT_DATE,
            state=State.SUCCESS,
            external_trigger=True,
        )
        dag.sync_to_db()
        with create_session() as session:
            model = session.query(DagModel).get((dag.dag_id,))

        # Even though there is a run for this date already, it is marked as manual/external, so we should
        # create a scheduled one anyway!
        assert model.next_dagrun == DEFAULT_DATE
        assert model.next_dagrun_create_after == DEFAULT_DATE + delta

        self._clean_up(dag_id)

    def test_schedule_dag_once(self):
        """
        Tests scheduling a dag scheduled for @once - should be scheduled the first time
        it is called, and not scheduled the second.
        """
        dag_id = "test_schedule_dag_once"
        dag = DAG(dag_id=dag_id, schedule="@once")
        assert isinstance(dag.timetable, OnceTimetable)
        dag.add_task(BaseOperator(task_id="faketastic", owner='Also fake', start_date=TEST_DATE))

        # Sync once to create the DagModel
        dag.sync_to_db()

        dag.create_dagrun(run_type=DagRunType.SCHEDULED, execution_date=TEST_DATE, state=State.SUCCESS)

        # Then sync again after creating the dag run -- this should update next_dagrun
        dag.sync_to_db()
        with create_session() as session:
            model = session.query(DagModel).get((dag.dag_id,))

        assert model.next_dagrun is None
        assert model.next_dagrun_create_after is None
        self._clean_up(dag_id)

    def test_fractional_seconds(self):
        """
        Tests if fractional seconds are stored in the database
        """
        dag_id = "test_fractional_seconds"
        dag = DAG(dag_id=dag_id, schedule="@once")
        dag.add_task(BaseOperator(task_id="faketastic", owner='Also fake', start_date=TEST_DATE))

        start_date = timezone.utcnow()

        run = dag.create_dagrun(
            run_id='test_' + start_date.isoformat(),
            execution_date=start_date,
            start_date=start_date,
            state=State.RUNNING,
            external_trigger=False,
        )

        run.refresh_from_db()

        assert start_date == run.execution_date, "dag run execution_date loses precision"
        assert start_date == run.start_date, "dag run start_date loses precision "
        self._clean_up(dag_id)

    def test_pickling(self):
        test_dag_id = 'test_pickling'
        args = {'owner': 'airflow', 'start_date': DEFAULT_DATE}
        dag = DAG(test_dag_id, default_args=args)
        dag_pickle = dag.pickle()
        assert dag_pickle.pickle.dag_id == dag.dag_id

    def test_rich_comparison_ops(self):
        test_dag_id = 'test_rich_comparison_ops'

        class DAGsubclass(DAG):
            pass

        args = {'owner': 'airflow', 'start_date': DEFAULT_DATE}
        dag = DAG(test_dag_id, default_args=args)

        dag_eq = DAG(test_dag_id, default_args=args)

        dag_diff_load_time = DAG(test_dag_id, default_args=args)
        dag_diff_name = DAG(test_dag_id + '_neq', default_args=args)

        dag_subclass = DAGsubclass(test_dag_id, default_args=args)
        dag_subclass_diff_name = DAGsubclass(test_dag_id + '2', default_args=args)

        for dag_ in [dag_eq, dag_diff_name, dag_subclass, dag_subclass_diff_name]:
            dag_.last_loaded = dag.last_loaded

        # test identity equality
        assert dag == dag

        # test dag (in)equality based on _comps
        assert dag_eq == dag
        assert dag_diff_name != dag
        assert dag_diff_load_time != dag

        # test dag inequality based on type even if _comps happen to match
        assert dag_subclass != dag

        # a dag should equal an unpickled version of itself
        dump = pickle.dumps(dag)
        assert pickle.loads(dump) == dag

        # dags are ordered based on dag_id no matter what the type is
        assert dag < dag_diff_name
        assert dag > dag_diff_load_time
        assert dag < dag_subclass_diff_name

        # greater than should have been created automatically by functools
        assert dag_diff_name > dag

        # hashes are non-random and match equality
        assert hash(dag) == hash(dag)
        assert hash(dag_eq) == hash(dag)
        assert hash(dag_diff_name) != hash(dag)
        assert hash(dag_subclass) != hash(dag)

    def test_get_paused_dag_ids(self):
        dag_id = "test_get_paused_dag_ids"
        dag = DAG(dag_id, is_paused_upon_creation=True)
        dag.sync_to_db()
        assert DagModel.get_dagmodel(dag_id) is not None

        paused_dag_ids = DagModel.get_paused_dag_ids([dag_id])
        assert paused_dag_ids == {dag_id}

        with create_session() as session:
            session.query(DagModel).filter(DagModel.dag_id == dag_id).delete(synchronize_session=False)

    @pytest.mark.parametrize(
        "schedule_interval_arg, expected_timetable, interval_description",
        [
            (None, NullTimetable(), "Never, external triggers only"),
            ("@daily", cron_timetable("0 0 * * *"), "At 00:00"),
            ("@weekly", cron_timetable("0 0 * * 0"), "At 00:00, only on Sunday"),
            ("@monthly", cron_timetable("0 0 1 * *"), "At 00:00, on day 1 of the month"),
            ("@quarterly", cron_timetable("0 0 1 */3 *"), "At 00:00, on day 1 of the month, every 3 months"),
            ("@yearly", cron_timetable("0 0 1 1 *"), "At 00:00, on day 1 of the month, only in January"),
            ("5 0 * 8 *", cron_timetable("5 0 * 8 *"), "At 00:05, only in August"),
            ("@once", OnceTimetable(), "Once, as soon as possible"),
            (datetime.timedelta(days=1), delta_timetable(datetime.timedelta(days=1)), ""),
            ("30 21 * * 5 1", cron_timetable("30 21 * * 5 1"), ""),
        ],
    )
    def test_timetable_and_description_from_schedule_interval_arg(
        self, schedule_interval_arg, expected_timetable, interval_description
    ):
        dag = DAG("test_schedule_interval_arg", schedule=schedule_interval_arg)
        assert dag.timetable == expected_timetable
        assert dag.schedule_interval == schedule_interval_arg
        assert dag.timetable.description == interval_description

    def test_timetable_and_description_from_dataset(self):
        dag = DAG("test_schedule_interval_arg", schedule=[Dataset(uri='hello')])
        assert dag.timetable == DatasetTriggeredTimetable()
        assert dag.schedule_interval == 'Dataset'
        assert dag.timetable.description == 'Triggered by datasets'

    def test_schedule_interval_still_works(self):
        dag = DAG("test_schedule_interval_arg", schedule_interval="*/5 * * * *")
        assert dag.timetable == cron_timetable("*/5 * * * *")
        assert dag.schedule_interval == "*/5 * * * *"
        assert dag.timetable.description == "Every 5 minutes"

    def test_timetable_still_works(self):
        dag = DAG("test_schedule_interval_arg", timetable=cron_timetable("*/6 * * * *"))
        assert dag.timetable == cron_timetable("*/6 * * * *")
        assert dag.schedule_interval == "*/6 * * * *"
        assert dag.timetable.description == "Every 6 minutes"

    @pytest.mark.parametrize(
        "timetable, expected_description",
        [
            (NullTimetable(), "Never, external triggers only"),
            (cron_timetable("0 0 * * *"), "At 00:00"),
            (cron_timetable("@daily"), "At 00:00"),
            (cron_timetable("0 0 * * 0"), "At 00:00, only on Sunday"),
            (cron_timetable("@weekly"), "At 00:00, only on Sunday"),
            (cron_timetable("0 0 1 * *"), "At 00:00, on day 1 of the month"),
            (cron_timetable("@monthly"), "At 00:00, on day 1 of the month"),
            (cron_timetable("0 0 1 */3 *"), "At 00:00, on day 1 of the month, every 3 months"),
            (cron_timetable("@quarterly"), "At 00:00, on day 1 of the month, every 3 months"),
            (cron_timetable("0 0 1 1 *"), "At 00:00, on day 1 of the month, only in January"),
            (cron_timetable("@yearly"), "At 00:00, on day 1 of the month, only in January"),
            (cron_timetable("5 0 * 8 *"), "At 00:05, only in August"),
            (OnceTimetable(), "Once, as soon as possible"),
            (delta_timetable(datetime.timedelta(days=1)), ""),
            (cron_timetable("30 21 * * 5 1"), ""),
        ],
    )
    def test_description_from_timetable(self, timetable, expected_description):
        dag = DAG("test_schedule_interval_description", timetable=timetable)
        assert dag.timetable == timetable
        assert dag.timetable.description == expected_description

    def test_create_dagrun_run_id_is_generated(self):
        dag = DAG(dag_id="run_id_is_generated")
        dr = dag.create_dagrun(run_type=DagRunType.MANUAL, execution_date=DEFAULT_DATE, state=State.NONE)
        assert dr.run_id == f"manual__{DEFAULT_DATE.isoformat()}"

    def test_create_dagrun_run_type_is_obtained_from_run_id(self):
        dag = DAG(dag_id="run_type_is_obtained_from_run_id")
        dr = dag.create_dagrun(run_id="scheduled__", state=State.NONE)
        assert dr.run_type == DagRunType.SCHEDULED

        dr = dag.create_dagrun(run_id="custom_is_set_to_manual", state=State.NONE)
        assert dr.run_type == DagRunType.MANUAL

    def test_create_dagrun_job_id_is_set(self):
        job_id = 42
        dag = DAG(dag_id="test_create_dagrun_job_id_is_set")
        dr = dag.create_dagrun(
            run_id="test_create_dagrun_job_id_is_set", state=State.NONE, creating_job_id=job_id
        )
        assert dr.creating_job_id == job_id

    def test_dag_add_task_sets_default_task_group(self):
        dag = DAG(dag_id="test_dag_add_task_sets_default_task_group", start_date=DEFAULT_DATE)
        task_without_task_group = EmptyOperator(task_id="task_without_group_id")
        default_task_group = TaskGroupContext.get_current_task_group(dag)
        dag.add_task(task_without_task_group)
        assert default_task_group.get_child_by_label("task_without_group_id") == task_without_task_group

        task_group = TaskGroup(group_id="task_group", dag=dag)
        task_with_task_group = EmptyOperator(task_id="task_with_task_group", task_group=task_group)
        dag.add_task(task_with_task_group)
        assert task_group.get_child_by_label("task_with_task_group") == task_with_task_group
        assert dag.get_task("task_group.task_with_task_group") == task_with_task_group

    @pytest.mark.parametrize("dag_run_state", [DagRunState.QUEUED, DagRunState.RUNNING])
    def test_clear_set_dagrun_state(self, dag_run_state):
        dag_id = 'test_clear_set_dagrun_state'
        self._clean_up(dag_id)
        task_id = 't1'
        dag = DAG(dag_id, start_date=DEFAULT_DATE, max_active_runs=1)
        t_1 = EmptyOperator(task_id=task_id, dag=dag)

        session = settings.Session()
        dagrun_1 = dag.create_dagrun(
            run_type=DagRunType.BACKFILL_JOB,
            state=State.FAILED,
            start_date=DEFAULT_DATE,
            execution_date=DEFAULT_DATE,
        )
        session.merge(dagrun_1)

        task_instance_1 = TI(t_1, execution_date=DEFAULT_DATE, state=State.RUNNING)
        session.merge(task_instance_1)
        session.commit()

        dag.clear(
            start_date=DEFAULT_DATE,
            end_date=DEFAULT_DATE + datetime.timedelta(days=1),
            dag_run_state=dag_run_state,
            include_subdags=False,
            include_parentdag=False,
            session=session,
        )

        dagruns = (
            session.query(
                DagRun,
            )
            .filter(
                DagRun.dag_id == dag_id,
            )
            .all()
        )

        assert len(dagruns) == 1
        dagrun = dagruns[0]  # type: DagRun
        assert dagrun.state == dag_run_state

    @pytest.mark.parametrize("dag_run_state", [DagRunState.QUEUED, DagRunState.RUNNING])
    def test_clear_set_dagrun_state_for_mapped_task(self, dag_run_state):
        dag_id = 'test_clear_set_dagrun_state'
        self._clean_up(dag_id)
        task_id = 't1'

        dag = DAG(dag_id, start_date=DEFAULT_DATE, max_active_runs=1)

        @dag.task
        def make_arg_lists():
            return [[1], [2], [{'a': 'b'}]]

        def consumer(value):
            print(value)

        mapped = PythonOperator.partial(task_id=task_id, dag=dag, python_callable=consumer).expand(
            op_args=make_arg_lists()
        )

        session = settings.Session()
        dagrun_1 = dag.create_dagrun(
            run_type=DagRunType.BACKFILL_JOB,
            state=State.FAILED,
            start_date=DEFAULT_DATE,
            execution_date=DEFAULT_DATE,
            session=session,
        )
        expand_mapped_task(mapped, dagrun_1.run_id, "make_arg_lists", length=2, session=session)

        upstream_ti = dagrun_1.get_task_instance("make_arg_lists", session=session)
        ti = dagrun_1.get_task_instance(task_id, map_index=0, session=session)
        ti2 = dagrun_1.get_task_instance(task_id, map_index=1, session=session)
        upstream_ti.state = State.SUCCESS
        ti.state = State.SUCCESS
        ti2.state = State.SUCCESS
        session.flush()

        dag.clear(
            task_ids=[(task_id, 0), ("make_arg_lists")],
            start_date=DEFAULT_DATE,
            end_date=DEFAULT_DATE + datetime.timedelta(days=1),
            dag_run_state=dag_run_state,
            include_subdags=False,
            include_parentdag=False,
            session=session,
        )
        session.refresh(upstream_ti)
        session.refresh(ti)
        session.refresh(ti2)
        assert upstream_ti.state is None  # cleared
        assert ti.state is None  # cleared
        assert ti2.state == State.SUCCESS  # not cleared
        dagruns = (
            session.query(
                DagRun,
            )
            .filter(
                DagRun.dag_id == dag_id,
            )
            .all()
        )

        assert len(dagruns) == 1
        dagrun = dagruns[0]  # type: DagRun
        assert dagrun.state == dag_run_state

    def _make_test_subdag(self, session):
        dag_id = 'test_subdag'
        self._clean_up(dag_id)
        task_id = 't1'
        dag = DAG(dag_id, start_date=DEFAULT_DATE, max_active_runs=1)
        t_1 = EmptyOperator(task_id=task_id, dag=dag)
        subdag = DAG(dag_id + '.test', start_date=DEFAULT_DATE, max_active_runs=1)
        SubDagOperator(task_id='test', subdag=subdag, dag=dag)
        t_2 = EmptyOperator(task_id='task', dag=subdag)
        subdag.parent_dag = dag

        dag.sync_to_db()

        session = settings.Session()
        dag.create_dagrun(
            run_type=DagRunType.MANUAL,
            state=State.FAILED,
            start_date=DEFAULT_DATE,
            execution_date=DEFAULT_DATE,
            session=session,
        )
        subdag.create_dagrun(
            run_type=DagRunType.MANUAL,
            state=State.FAILED,
            start_date=DEFAULT_DATE,
            execution_date=DEFAULT_DATE,
            session=session,
        )
        task_instance_1 = TI(t_1, execution_date=DEFAULT_DATE, state=State.RUNNING)
        task_instance_2 = TI(t_2, execution_date=DEFAULT_DATE, state=State.RUNNING)
        session.merge(task_instance_1)
        session.merge(task_instance_2)

        return dag, subdag

    @pytest.mark.parametrize("dag_run_state", [DagRunState.QUEUED, DagRunState.RUNNING])
    def test_clear_set_dagrun_state_for_subdag(self, dag_run_state):
        session = settings.Session()
        dag, subdag = self._make_test_subdag(session)
        session.flush()

        dag.clear(
            start_date=DEFAULT_DATE,
            end_date=DEFAULT_DATE + datetime.timedelta(days=1),
            dag_run_state=dag_run_state,
            include_subdags=True,
            include_parentdag=False,
            session=session,
        )

        dagrun = (
            session.query(
                DagRun,
            )
            .filter(DagRun.dag_id == subdag.dag_id)
            .one()
        )
        assert dagrun.state == dag_run_state
        session.rollback()

    @pytest.mark.parametrize("dag_run_state", [DagRunState.QUEUED, DagRunState.RUNNING])
    def test_clear_set_dagrun_state_for_parent_dag(self, dag_run_state):
        session = settings.Session()
        dag, subdag = self._make_test_subdag(session)
        session.flush()

        subdag.clear(
            start_date=DEFAULT_DATE,
            end_date=DEFAULT_DATE + datetime.timedelta(days=1),
            dag_run_state=dag_run_state,
            include_subdags=True,
            include_parentdag=True,
            session=session,
        )

        dagrun = (
            session.query(
                DagRun,
            )
            .filter(DagRun.dag_id == dag.dag_id)
            .one()
        )
        assert dagrun.state == dag_run_state

    @pytest.mark.parametrize(
        "ti_state_begin, ti_state_end",
        [
            *((state, None) for state in State.task_states if state != TaskInstanceState.RUNNING),
            (TaskInstanceState.RUNNING, TaskInstanceState.RESTARTING),
        ],
    )
    def test_clear_dag(
        self,
        ti_state_begin: TaskInstanceState | None,
        ti_state_end: TaskInstanceState | None,
    ):
        dag_id = 'test_clear_dag'
        self._clean_up(dag_id)
        task_id = 't1'
        dag = DAG(dag_id, start_date=DEFAULT_DATE, max_active_runs=1)
        t_1 = EmptyOperator(task_id=task_id, dag=dag)

        session = settings.Session()  # type: ignore
        dagrun_1 = dag.create_dagrun(
            run_type=DagRunType.BACKFILL_JOB,
            state=DagRunState.RUNNING,
            start_date=DEFAULT_DATE,
            execution_date=DEFAULT_DATE,
        )
        session.merge(dagrun_1)

        task_instance_1 = TI(t_1, execution_date=DEFAULT_DATE, state=ti_state_begin)
        task_instance_1.job_id = 123
        session.merge(task_instance_1)
        session.commit()

        dag.clear(
            start_date=DEFAULT_DATE,
            end_date=DEFAULT_DATE + datetime.timedelta(days=1),
            session=session,
        )

        task_instances = (
            session.query(
                TI,
            )
            .filter(
                TI.dag_id == dag_id,
            )
            .all()
        )

        assert len(task_instances) == 1
        task_instance = task_instances[0]  # type: TI
        assert task_instance.state == ti_state_end
        self._clean_up(dag_id)

    def test_next_dagrun_info_once(self):
        dag = DAG('test_scheduler_dagrun_once', start_date=timezone.datetime(2015, 1, 1), schedule="@once")

        next_info = dag.next_dagrun_info(None)
        assert next_info and next_info.logical_date == timezone.datetime(2015, 1, 1)

        next_info = dag.next_dagrun_info(next_info.data_interval)
        assert next_info is None

    def test_next_dagrun_info_start_end_dates(self):
        """
        Tests that an attempt to schedule a task after the Dag's end_date
        does not succeed.
        """
        delta = datetime.timedelta(hours=1)
        runs = 3
        start_date = DEFAULT_DATE
        end_date = start_date + (runs - 1) * delta
        dag_id = "test_schedule_dag_start_end_dates"
        dag = DAG(dag_id=dag_id, start_date=start_date, end_date=end_date, schedule=delta)
        dag.add_task(BaseOperator(task_id='faketastic', owner='Also fake'))

        # Create and schedule the dag runs
        dates = []
        interval = None
        for _ in range(runs):
            next_info = dag.next_dagrun_info(interval)
            if next_info is None:
                dates.append(None)
            else:
                interval = next_info.data_interval
                dates.append(interval.start)

        assert all(date is not None for date in dates)
        assert dates[-1] == end_date
        assert dag.next_dagrun_info(interval.start) is None

    def test_next_dagrun_info_catchup(self):
        """
        Test to check that a DAG with catchup = False only schedules beginning now, not back to the start date
        """

        def make_dag(dag_id, schedule, start_date, catchup):
            default_args = {
                'owner': 'airflow',
                'depends_on_past': False,
            }
            dag = DAG(
                dag_id,
                schedule=schedule,
                start_date=start_date,
                catchup=catchup,
                default_args=default_args,
            )

            op1 = EmptyOperator(task_id='t1', dag=dag)
            op2 = EmptyOperator(task_id='t2', dag=dag)
            op3 = EmptyOperator(task_id='t3', dag=dag)
            op1 >> op2 >> op3

            return dag

        now = timezone.utcnow()
        six_hours_ago_to_the_hour = (now - datetime.timedelta(hours=6)).replace(
            minute=0, second=0, microsecond=0
        )
        half_an_hour_ago = now - datetime.timedelta(minutes=30)
        two_hours_ago = now - datetime.timedelta(hours=2)

        dag1 = make_dag(
            dag_id='dag_without_catchup_ten_minute',
            schedule='*/10 * * * *',
            start_date=six_hours_ago_to_the_hour,
            catchup=False,
        )
        next_date, _ = dag1.next_dagrun_info(None)
        # The DR should be scheduled in the last half an hour, not 6 hours ago
        assert next_date > half_an_hour_ago
        assert next_date < timezone.utcnow()

        dag2 = make_dag(
            dag_id='dag_without_catchup_hourly',
            schedule='@hourly',
            start_date=six_hours_ago_to_the_hour,
            catchup=False,
        )

        next_date, _ = dag2.next_dagrun_info(None)
        # The DR should be scheduled in the last 2 hours, not 6 hours ago
        assert next_date > two_hours_ago
        # The DR should be scheduled BEFORE now
        assert next_date < timezone.utcnow()

        dag3 = make_dag(
            dag_id='dag_without_catchup_once',
            schedule='@once',
            start_date=six_hours_ago_to_the_hour,
            catchup=False,
        )

        next_date, _ = dag3.next_dagrun_info(None)
        # The DR should be scheduled in the last 2 hours, not 6 hours ago
        assert next_date == six_hours_ago_to_the_hour

    @freeze_time(timezone.datetime(2020, 1, 5))
    def test_next_dagrun_info_timedelta_schedule_and_catchup_false(self):
        """
        Test that the dag file processor does not create multiple dagruns
        if a dag is scheduled with 'timedelta' and catchup=False
        """
        dag = DAG(
            'test_scheduler_dagrun_once_with_timedelta_and_catchup_false',
            start_date=timezone.datetime(2015, 1, 1),
            schedule=timedelta(days=1),
            catchup=False,
        )

        next_info = dag.next_dagrun_info(None)
        assert next_info and next_info.logical_date == timezone.datetime(2020, 1, 4)

        # The date to create is in the future, this is handled by "DagModel.dags_needing_dagruns"
        next_info = dag.next_dagrun_info(next_info.data_interval)
        assert next_info and next_info.logical_date == timezone.datetime(2020, 1, 5)

    @freeze_time(timezone.datetime(2020, 5, 4))
    def test_next_dagrun_info_timedelta_schedule_and_catchup_true(self):
        """
        Test that the dag file processor creates multiple dagruns
        if a dag is scheduled with 'timedelta' and catchup=True
        """
        dag = DAG(
            'test_scheduler_dagrun_once_with_timedelta_and_catchup_true',
            start_date=timezone.datetime(2020, 5, 1),
            schedule=timedelta(days=1),
            catchup=True,
        )

        next_info = dag.next_dagrun_info(None)
        assert next_info and next_info.logical_date == timezone.datetime(2020, 5, 1)

        next_info = dag.next_dagrun_info(next_info.data_interval)
        assert next_info and next_info.logical_date == timezone.datetime(2020, 5, 2)

        next_info = dag.next_dagrun_info(next_info.data_interval)
        assert next_info and next_info.logical_date == timezone.datetime(2020, 5, 3)

        # The date to create is in the future, this is handled by "DagModel.dags_needing_dagruns"
        next_info = dag.next_dagrun_info(next_info.data_interval)
        assert next_info and next_info.logical_date == timezone.datetime(2020, 5, 4)

    def test_next_dagrun_info_timetable_exception(self, caplog):
        """Test the DAG does not crash the scheduler if the timetable raises an exception."""

        class FailingTimetable(Timetable):
            def next_dagrun_info(self, last_automated_data_interval, restriction):
                raise RuntimeError("this fails")

        dag = DAG(
            "test_next_dagrun_info_timetable_exception",
            start_date=timezone.datetime(2020, 5, 1),
            timetable=FailingTimetable(),
            catchup=True,
        )

        def _check_logs(records: list[logging.LogRecord], data_interval: DataInterval) -> None:
            assert len(records) == 1
            record = records[0]
            assert record.exc_info is not None, "Should contain exception"
            assert record.getMessage() == (
                f"Failed to fetch run info after data interval {data_interval} "
                f"for DAG 'test_next_dagrun_info_timetable_exception'"
            )

        with caplog.at_level(level=logging.ERROR):
            next_info = dag.next_dagrun_info(None)
        assert next_info is None, "failed next_dagrun_info should return None"
        _check_logs(caplog.records, data_interval=None)
        caplog.clear()
        data_interval = DataInterval(timezone.datetime(2020, 5, 1), timezone.datetime(2020, 5, 2))
        with caplog.at_level(level=logging.ERROR):
            next_info = dag.next_dagrun_info(data_interval)
        assert next_info is None, "failed next_dagrun_info should return None"
        _check_logs(caplog.records, data_interval)

    def test_next_dagrun_after_auto_align(self):
        """
        Test if the schedule_interval will be auto aligned with the start_date
        such that if the start_date coincides with the schedule the first
        execution_date will be start_date, otherwise it will be start_date +
        interval.
        """
        dag = DAG(
            dag_id='test_scheduler_auto_align_1',
            start_date=timezone.datetime(2016, 1, 1, 10, 10, 0),
            schedule="4 5 * * *",
        )
        EmptyOperator(task_id='dummy', dag=dag, owner='airflow')

        next_info = dag.next_dagrun_info(None)
        assert next_info and next_info.logical_date == timezone.datetime(2016, 1, 2, 5, 4)

        dag = DAG(
            dag_id='test_scheduler_auto_align_2',
            start_date=timezone.datetime(2016, 1, 1, 10, 10, 0),
            schedule="10 10 * * *",
        )
        EmptyOperator(task_id='dummy', dag=dag, owner='airflow')

        next_info = dag.next_dagrun_info(None)
        assert next_info and next_info.logical_date == timezone.datetime(2016, 1, 1, 10, 10)

    def test_next_dagrun_after_not_for_subdags(self):
        """
        Test the subdags are never marked to have dagruns created, as they are
        handled by the SubDagOperator, not the scheduler
        """

        def subdag(parent_dag_name, child_dag_name, args):
            """
            Create a subdag.
            """
            dag_subdag = DAG(
                dag_id=f'{parent_dag_name}.{child_dag_name}',
                schedule="@daily",
                default_args=args,
            )

            for i in range(2):
                EmptyOperator(task_id=f'{child_dag_name}-task-{i + 1}', dag=dag_subdag)

            return dag_subdag

        with DAG(
            dag_id='test_subdag_operator',
            start_date=datetime.datetime(2019, 1, 1),
            max_active_runs=1,
            schedule=timedelta(minutes=1),
        ) as dag:
            section_1 = SubDagOperator(
                task_id='section-1',
                subdag=subdag(dag.dag_id, 'section-1', {'start_date': dag.start_date}),
            )

        subdag = section_1.subdag
        # parent_dag and is_subdag was set by DagBag. We don't use DagBag, so this value is not set.
        subdag.parent_dag = dag

        next_parent_info = dag.next_dagrun_info(None)
        assert next_parent_info.logical_date == timezone.datetime(2019, 1, 1, 0, 0)

        next_subdag_info = subdag.next_dagrun_info(None)
        assert next_subdag_info is None, "SubDags should never have DagRuns created by the scheduler"

    def test_replace_outdated_access_control_actions(self):
        outdated_permissions = {
            'role1': {permissions.ACTION_CAN_READ, permissions.ACTION_CAN_EDIT},
            'role2': {permissions.DEPRECATED_ACTION_CAN_DAG_READ, permissions.DEPRECATED_ACTION_CAN_DAG_EDIT},
        }
        updated_permissions = {
            'role1': {permissions.ACTION_CAN_READ, permissions.ACTION_CAN_EDIT},
            'role2': {permissions.ACTION_CAN_READ, permissions.ACTION_CAN_EDIT},
        }

        with pytest.warns(DeprecationWarning):
            dag = DAG(dag_id='dag_with_outdated_perms', access_control=outdated_permissions)
        assert dag.access_control == updated_permissions

        with pytest.warns(DeprecationWarning):
            dag.access_control = outdated_permissions
        assert dag.access_control == updated_permissions

    def test_validate_params_on_trigger_dag(self):
        dag = models.DAG('dummy-dag', schedule=None, params={'param1': Param(type="string")})
        with pytest.raises(ParamValidationError, match="No value passed and Param has no default value"):
            dag.create_dagrun(
                run_id="test_dagrun_missing_param",
                state=State.RUNNING,
                execution_date=TEST_DATE,
            )

        dag = models.DAG('dummy-dag', schedule=None, params={'param1': Param(type="string")})
        with pytest.raises(
            ParamValidationError, match="Invalid input for param param1: None is not of type 'string'"
        ):
            dag.create_dagrun(
                run_id="test_dagrun_missing_param",
                state=State.RUNNING,
                execution_date=TEST_DATE,
                conf={"param1": None},
            )

        dag = models.DAG('dummy-dag', schedule=None, params={'param1': Param(type="string")})
        dag.create_dagrun(
            run_id="test_dagrun_missing_param",
            state=State.RUNNING,
            execution_date=TEST_DATE,
            conf={"param1": "hello"},
        )

    def test_return_date_range_with_num_method(self):
        start_date = TEST_DATE
        delta = timedelta(days=1)

        dag = models.DAG('dummy-dag', schedule=delta)
        dag_dates = dag.date_range(start_date=start_date, num=3)

        assert dag_dates == [
            start_date,
            start_date + delta,
            start_date + 2 * delta,
        ]

    def test_dag_owner_links(self):
        dag = DAG(
            'dag',
            start_date=DEFAULT_DATE,
            owner_links={"owner1": "https://mylink.com", "owner2": "mailto:someone@yoursite.com"},
        )

        assert dag.owner_links == {"owner1": "https://mylink.com", "owner2": "mailto:someone@yoursite.com"}
        session = settings.Session()
        dag.sync_to_db(session=session)

        expected_owners = {'dag': {'owner1': 'https://mylink.com', 'owner2': 'mailto:someone@yoursite.com'}}
        orm_dag_owners = DagOwnerAttributes.get_all(session)
        assert orm_dag_owners == expected_owners

        # Test dag owner links are removed completely
        dag = DAG(
            'dag',
            start_date=DEFAULT_DATE,
        )
        dag.sync_to_db(session=session)

        orm_dag_owners = session.query(DagOwnerAttributes).all()
        assert not orm_dag_owners

        # Check wrong formatted owner link
        with pytest.raises(AirflowException):
            DAG('dag', start_date=DEFAULT_DATE, owner_links={"owner1": "my-bad-link"})

    @pytest.mark.parametrize(
        "kwargs",
        [
            {"schedule_interval": "@daily", "schedule": "@weekly"},
            {"timetable": NullTimetable(), "schedule": "@weekly"},
            {"timetable": NullTimetable(), "schedule_interval": "@daily"},
        ],
        ids=[
            "schedule_interval+schedule",
            "timetable+schedule",
            "timetable+schedule_interval",
        ],
    )
    def test_schedule_dag_param(self, kwargs):
        with pytest.raises(ValueError, match='At most one'):
            with DAG(dag_id='hello', **kwargs):
                pass


class TestDagModel:
    def _clean(self):
        clear_db_dags()
        clear_db_datasets()
        clear_db_runs()

    def setup_method(self):
        self._clean()

    def teardown_method(self):
        self._clean()

    def test_dags_needing_dagruns_not_too_early(self):
        dag = DAG(dag_id='far_future_dag', start_date=timezone.datetime(2038, 1, 1))
        EmptyOperator(task_id='dummy', dag=dag, owner='airflow')

        session = settings.Session()
        orm_dag = DagModel(
            dag_id=dag.dag_id,
            max_active_tasks=1,
            has_task_concurrency_limits=False,
            next_dagrun=dag.start_date,
            next_dagrun_create_after=timezone.datetime(2038, 1, 2),
            is_active=True,
        )
        session.add(orm_dag)
        session.flush()

        query, _ = DagModel.dags_needing_dagruns(session)
        dag_models = query.all()
        assert dag_models == []

        session.rollback()
        session.close()

    def test_dags_needing_dagruns_datasets(self, dag_maker, session):
        dataset = Dataset(uri='hello')
        with dag_maker(
            session=session,
            dag_id='my_dag',
            max_active_runs=1,
            schedule=[dataset],
            start_date=pendulum.now().add(days=-2),
        ) as dag:
            EmptyOperator(task_id='dummy')

        # there's no queue record yet, so no runs needed at this time.
        query, _ = DagModel.dags_needing_dagruns(session)
        dag_models = query.all()
        assert dag_models == []

        # add queue records so we'll need a run
        dag_model = session.query(DagModel).filter(DagModel.dag_id == dag.dag_id).one()
        dataset_model: DatasetModel = dag_model.schedule_datasets[0]
        session.add(DatasetDagRunQueue(dataset_id=dataset_model.id, target_dag_id=dag_model.dag_id))
        session.flush()
        query, _ = DagModel.dags_needing_dagruns(session)
        dag_models = query.all()
        assert dag_models == [dag_model]

        # create run so we don't need a run anymore (due to max active runs)
        dag_maker.create_dagrun(
            run_type=DagRunType.DATASET_TRIGGERED,
            state=DagRunState.QUEUED,
            execution_date=pendulum.now('UTC'),
        )
        query, _ = DagModel.dags_needing_dagruns(session)
        dag_models = query.all()
        assert dag_models == []

        # increase max active runs and we should now need another run
        dag_maker.dag_model.max_active_runs = 2
        session.flush()
        query, _ = DagModel.dags_needing_dagruns(session)
        dag_models = query.all()
        assert dag_models == [dag_model]

    def test_max_active_runs_not_none(self):
        dag = DAG(dag_id='test_max_active_runs_not_none', start_date=timezone.datetime(2038, 1, 1))
        EmptyOperator(task_id='dummy', dag=dag, owner='airflow')

        session = settings.Session()
        orm_dag = DagModel(
            dag_id=dag.dag_id,
            has_task_concurrency_limits=False,
            next_dagrun=None,
            next_dagrun_create_after=None,
            is_active=True,
        )
        # assert max_active_runs updated
        assert orm_dag.max_active_runs == 16
        session.add(orm_dag)
        session.flush()
        assert orm_dag.max_active_runs is not None

        session.rollback()
        session.close()

    def test_dags_needing_dagruns_only_unpaused(self):
        """
        We should never create dagruns for unpaused DAGs
        """
        dag = DAG(dag_id='test_dags', start_date=DEFAULT_DATE)
        EmptyOperator(task_id='dummy', dag=dag, owner='airflow')

        session = settings.Session()
        orm_dag = DagModel(
            dag_id=dag.dag_id,
            has_task_concurrency_limits=False,
            next_dagrun=DEFAULT_DATE,
            next_dagrun_create_after=DEFAULT_DATE + timedelta(days=1),
            is_active=True,
        )
        session.add(orm_dag)
        session.flush()

        query, _ = DagModel.dags_needing_dagruns(session)
        needed = query.all()
        assert needed == [orm_dag]

        orm_dag.is_paused = True
        session.flush()

        query, _ = DagModel.dags_needing_dagruns(session)
        dag_models = query.all()
        assert dag_models == []

        session.rollback()
        session.close()

    def test_dags_needing_dagruns_doesnot_send_dagmodel_with_import_errors(self, session):
        """
        We check that has_import_error is false for dags
        being set to scheduler to create dagruns
        """
        dag = DAG(dag_id='test_dags', start_date=DEFAULT_DATE)
        EmptyOperator(task_id='dummy', dag=dag, owner='airflow')

        orm_dag = DagModel(
            dag_id=dag.dag_id,
            has_task_concurrency_limits=False,
            next_dagrun=DEFAULT_DATE,
            next_dagrun_create_after=DEFAULT_DATE + timedelta(days=1),
            is_active=True,
        )
        assert not orm_dag.has_import_errors
        session.add(orm_dag)
        session.flush()

        query, _ = DagModel.dags_needing_dagruns(session)
        needed = query.all()
        assert needed == [orm_dag]
        orm_dag.has_import_errors = True
        session.merge(orm_dag)
        session.flush()
        query, _ = DagModel.dags_needing_dagruns(session)
        needed = query.all()
        assert needed == []

    @pytest.mark.parametrize(
        ('fileloc', 'expected_relative'),
        [
            (os.path.join(settings.DAGS_FOLDER, 'a.py'), Path('a.py')),
            ('/tmp/foo.py', Path('/tmp/foo.py')),
        ],
    )
    def test_relative_fileloc(self, fileloc, expected_relative):
        dag = DAG(dag_id='test')
        dag.fileloc = fileloc

        assert dag.relative_fileloc == expected_relative

    @pytest.mark.parametrize(
        'reader_dags_folder', [settings.DAGS_FOLDER, str(repo_root / 'airflow/example_dags')]
    )
    @pytest.mark.parametrize(
        ('fileloc', 'expected_relative'),
        [
            (str(Path(settings.DAGS_FOLDER, 'a.py')), Path('a.py')),
            ('/tmp/foo.py', Path('/tmp/foo.py')),
        ],
    )
    def test_relative_fileloc_serialized(
        self, fileloc, expected_relative, session, clear_dags, reader_dags_folder
    ):
        """
        The serialized dag model includes the dags folder as configured on the thing serializing
        the dag.  On the thing deserializing the dag, when determining relative fileloc,
        we should use the dags folder of the processor.  So even if the dags folder of
        the deserializer is different (meaning that the full path is no longer relative to
        the dags folder) then we should still get the relative fileloc as it existed on the
        serializer process.  When the full path is not relative to the configured dags folder,
        then relative fileloc should just be the full path.
        """
        dag = DAG(dag_id='test')
        dag.fileloc = fileloc
        sdm = SerializedDagModel(dag)
        session.add(sdm)
        session.commit()
        session.expunge_all()
        sdm = SerializedDagModel.get(dag.dag_id, session)
        dag = sdm.dag
        with conf_vars({('core', 'dags_folder'): reader_dags_folder}):
            assert dag.relative_fileloc == expected_relative

    def test__processor_dags_folder(self, session):
        """Only populated after deserializtion"""
        dag = DAG(dag_id='test')
        dag.fileloc = '/abc/test.py'
        assert dag._processor_dags_folder is None
        sdm = SerializedDagModel(dag)
        assert sdm.dag._processor_dags_folder == settings.DAGS_FOLDER

    @pytest.mark.need_serialized_dag
    def test_dags_needing_dagruns_dataset_triggered_dag_info_queued_times(self, session, dag_maker):
        dataset1 = Dataset(uri="ds1")
        dataset2 = Dataset(uri="ds2")

        for dag_id, dataset in [("datasets-1", dataset1), ("datasets-2", dataset2)]:
            with dag_maker(dag_id=dag_id, start_date=timezone.utcnow(), session=session):
                EmptyOperator(task_id="task", outlets=[dataset])
            dr = dag_maker.create_dagrun()

            ds_id = session.query(DatasetModel.id).filter_by(uri=dataset.uri).scalar()

            session.add(
                DatasetEvent(
                    dataset_id=ds_id,
                    source_task_id="task",
                    source_dag_id=dr.dag_id,
                    source_run_id=dr.run_id,
                    source_map_index=-1,
                )
            )

        ds1_id = session.query(DatasetModel.id).filter_by(uri=dataset1.uri).scalar()
        ds2_id = session.query(DatasetModel.id).filter_by(uri=dataset2.uri).scalar()

        with dag_maker(dag_id="datasets-consumer-multiple", schedule=[dataset1, dataset2]) as dag:
            pass

        session.flush()
        session.add_all(
            [
                DatasetDagRunQueue(dataset_id=ds1_id, target_dag_id=dag.dag_id, created_at=DEFAULT_DATE),
                DatasetDagRunQueue(
                    dataset_id=ds2_id, target_dag_id=dag.dag_id, created_at=DEFAULT_DATE + timedelta(hours=1)
                ),
            ]
        )
        session.flush()

        query, dataset_triggered_dag_info = DagModel.dags_needing_dagruns(session)
        assert 1 == len(dataset_triggered_dag_info)
        assert dag.dag_id in dataset_triggered_dag_info
        first_queued_time, last_queued_time = dataset_triggered_dag_info[dag.dag_id]
        assert first_queued_time == DEFAULT_DATE
        assert last_queued_time == DEFAULT_DATE + timedelta(hours=1)


class TestQueries:
    def setup_method(self) -> None:
        clear_db_runs()

    def teardown_method(self) -> None:
        clear_db_runs()

    @pytest.mark.parametrize("tasks_count", [3, 12])
    def test_count_number_queries(self, tasks_count):
        dag = DAG('test_dagrun_query_count', start_date=DEFAULT_DATE)
        for i in range(tasks_count):
            EmptyOperator(task_id=f'dummy_task_{i}', owner='test', dag=dag)
        with assert_queries_count(2):
            dag.create_dagrun(
                run_id="test_dagrun_query_count",
                state=State.RUNNING,
                execution_date=TEST_DATE,
            )


class TestDagDecorator:
    DEFAULT_ARGS = {
        "owner": "test",
        "depends_on_past": True,
        "start_date": timezone.utcnow(),
        "retries": 1,
        "retry_delay": timedelta(minutes=1),
    }
    DEFAULT_DATE = timezone.datetime(2016, 1, 1)
    VALUE = 42

    def setup_method(self):
        self.operator = None

    def teardown_method(self):
        clear_db_runs()

    def test_fileloc(self):
        @dag_decorator(default_args=self.DEFAULT_ARGS)
        def noop_pipeline():
            ...

        dag = noop_pipeline()
        assert isinstance(dag, DAG)
        assert dag.dag_id, 'noop_pipeline'
        assert dag.fileloc == __file__

    def test_set_dag_id(self):
        """Test that checks you can set dag_id from decorator."""

        @dag_decorator('test', default_args=self.DEFAULT_ARGS)
        def noop_pipeline():
            @task_decorator
            def return_num(num):
                return num

            return_num(4)

        dag = noop_pipeline()
        assert isinstance(dag, DAG)
        assert dag.dag_id, 'test'

    def test_default_dag_id(self):
        """Test that @dag uses function name as default dag id."""

        @dag_decorator(default_args=self.DEFAULT_ARGS)
        def noop_pipeline():
            @task_decorator
            def return_num(num):
                return num

            return_num(4)

        dag = noop_pipeline()
        assert isinstance(dag, DAG)
        assert dag.dag_id, 'noop_pipeline'

    def test_documentation_added(self):
        """Test that @dag uses function docs as doc_md for DAG object"""

        @dag_decorator(default_args=self.DEFAULT_ARGS)
        def noop_pipeline():
            """
            Regular DAG documentation
            """

            @task_decorator
            def return_num(num):
                return num

            return_num(4)

        dag = noop_pipeline()
        assert isinstance(dag, DAG)
        assert dag.dag_id, 'test'
        assert dag.doc_md.strip(), "Regular DAG documentation"

    def test_documentation_template_rendered(self):
        """Test that @dag uses function docs as doc_md for DAG object"""

        @dag_decorator(default_args=self.DEFAULT_ARGS)
        def noop_pipeline():
            """
            {% if True %}
               Regular DAG documentation
            {% endif %}
            """

            @task_decorator
            def return_num(num):
                return num

            return_num(4)

        dag = noop_pipeline()
        assert isinstance(dag, DAG)
        assert dag.dag_id, 'test'
        assert dag.doc_md.strip(), "Regular DAG documentation"

    def test_resolve_documentation_template_file_rendered(self):
        """Test that @dag uses function docs as doc_md for DAG object"""

        with NamedTemporaryFile(suffix='.md') as f:
            f.write(
                b"""
            {% if True %}
               External Markdown DAG documentation
            {% endif %}
            """
            )
            f.flush()
            template_file = os.path.basename(f.name)

            with DAG('test-dag', start_date=DEFAULT_DATE, doc_md=template_file) as dag:
                task = EmptyOperator(task_id='op1')

                task

                assert isinstance(dag, DAG)
                assert dag.dag_id, 'test'
                assert dag.doc_md.strip(), "External Markdown DAG documentation"

    def test_fails_if_arg_not_set(self):
        """Test that @dag decorated function fails if positional argument is not set"""

        @dag_decorator(default_args=self.DEFAULT_ARGS)
        def noop_pipeline(value):
            @task_decorator
            def return_num(num):
                return num

            return_num(value)

        # Test that if arg is not passed it raises a type error as expected.
        with pytest.raises(TypeError):
            noop_pipeline()

    def test_dag_param_resolves(self):
        """Test that dag param is correctly resolved by operator"""

        @dag_decorator(default_args=self.DEFAULT_ARGS)
        def xcom_pass_to_op(value=self.VALUE):
            @task_decorator
            def return_num(num):
                return num

            xcom_arg = return_num(value)
            self.operator = xcom_arg.operator

        dag = xcom_pass_to_op()

        dr = dag.create_dagrun(
            run_id=DagRunType.MANUAL.value,
            start_date=timezone.utcnow(),
            execution_date=self.DEFAULT_DATE,
            data_interval=(self.DEFAULT_DATE, self.DEFAULT_DATE),
            state=State.RUNNING,
        )

        self.operator.run(start_date=self.DEFAULT_DATE, end_date=self.DEFAULT_DATE)
        ti = dr.get_task_instances()[0]
        assert ti.xcom_pull() == self.VALUE

    def test_dag_param_dagrun_parameterized(self):
        """Test that dag param is correctly overwritten when set in dag run"""

        @dag_decorator(default_args=self.DEFAULT_ARGS)
        def xcom_pass_to_op(value=self.VALUE):
            @task_decorator
            def return_num(num):
                return num

            assert isinstance(value, DagParam)

            xcom_arg = return_num(value)
            self.operator = xcom_arg.operator

        dag = xcom_pass_to_op()
        new_value = 52
        dr = dag.create_dagrun(
            run_id=DagRunType.MANUAL.value,
            start_date=timezone.utcnow(),
            execution_date=self.DEFAULT_DATE,
            data_interval=(self.DEFAULT_DATE, self.DEFAULT_DATE),
            state=State.RUNNING,
            conf={'value': new_value},
        )

        self.operator.run(start_date=self.DEFAULT_DATE, end_date=self.DEFAULT_DATE)
        ti = dr.get_task_instances()[0]
        assert ti.xcom_pull(), new_value

    @pytest.mark.parametrize("value", [VALUE, 0])
    def test_set_params_for_dag(self, value):
        """Test that dag param is correctly set when using dag decorator"""

        @dag_decorator(default_args=self.DEFAULT_ARGS)
        def xcom_pass_to_op(value=value):
            @task_decorator
            def return_num(num):
                return num

            xcom_arg = return_num(value)
            self.operator = xcom_arg.operator

        dag = xcom_pass_to_op()
        assert dag.params['value'] == value


@pytest.mark.parametrize("timetable", [NullTimetable(), OnceTimetable()])
def test_dag_timetable_match_schedule_interval(timetable):
    dag = DAG("my-dag", timetable=timetable)
    assert dag._check_schedule_interval_matches_timetable()


@pytest.mark.parametrize("schedule_interval", [None, "@once", "@daily", timedelta(days=1)])
def test_dag_schedule_interval_match_timetable(schedule_interval):
    dag = DAG("my-dag", schedule=schedule_interval)
    assert dag._check_schedule_interval_matches_timetable()


@pytest.mark.parametrize("schedule_interval", [None, "@daily", timedelta(days=1)])
def test_dag_schedule_interval_change_after_init(schedule_interval):
    dag = DAG("my-dag", timetable=OnceTimetable())
    dag.schedule_interval = schedule_interval
    assert not dag._check_schedule_interval_matches_timetable()


@pytest.mark.parametrize("timetable", [NullTimetable(), OnceTimetable()])
def test_dag_timetable_change_after_init(timetable):
    dag = DAG("my-dag")  # Default is timedelta(days=1).
    dag.timetable = timetable
    assert not dag._check_schedule_interval_matches_timetable()


@pytest.mark.parametrize("run_id, execution_date", [(None, datetime_tz(2020, 1, 1)), ('test-run-id', None)])
def test_set_task_instance_state(run_id, execution_date, session, dag_maker):
    """Test that set_task_instance_state updates the TaskInstance state and clear downstream failed"""

    start_date = datetime_tz(2020, 1, 1)
    with dag_maker("test_set_task_instance_state", start_date=start_date, session=session) as dag:
        task_1 = EmptyOperator(task_id="task_1")
        task_2 = EmptyOperator(task_id="task_2")
        task_3 = EmptyOperator(task_id="task_3")
        task_4 = EmptyOperator(task_id="task_4")
        task_5 = EmptyOperator(task_id="task_5")

        task_1 >> [task_2, task_3, task_4, task_5]

    dagrun = dag_maker.create_dagrun(
        run_id=run_id,
        execution_date=execution_date,
        state=State.FAILED,
        run_type=DagRunType.SCHEDULED,
    )

    def get_ti_from_db(task):
        return (
            session.query(TI)
            .filter(
                TI.dag_id == dag.dag_id,
                TI.task_id == task.task_id,
                TI.run_id == dagrun.run_id,
            )
            .one()
        )

    get_ti_from_db(task_1).state = State.FAILED
    get_ti_from_db(task_2).state = State.SUCCESS
    get_ti_from_db(task_3).state = State.UPSTREAM_FAILED
    get_ti_from_db(task_4).state = State.FAILED
    get_ti_from_db(task_5).state = State.SKIPPED

    session.flush()

    altered = dag.set_task_instance_state(
        task_id=task_1.task_id,
        run_id=run_id,
        execution_date=execution_date,
        state=State.SUCCESS,
        session=session,
    )

    # After _mark_task_instance_state, task_1 is marked as SUCCESS
    ti1 = get_ti_from_db(task_1)
    assert ti1.state == State.SUCCESS
    # TIs should have DagRun pre-loaded
    assert isinstance(inspect(ti1).attrs.dag_run.loaded_value, DagRun)
    # task_2 remains as SUCCESS
    assert get_ti_from_db(task_2).state == State.SUCCESS
    # task_3 and task_4 are cleared because they were in FAILED/UPSTREAM_FAILED state
    assert get_ti_from_db(task_3).state == State.NONE
    assert get_ti_from_db(task_4).state == State.NONE
    # task_5 remains as SKIPPED
    assert get_ti_from_db(task_5).state == State.SKIPPED
    dagrun.refresh_from_db(session=session)
    # dagrun should be set to QUEUED
    assert dagrun.get_state() == State.QUEUED

    assert {t.key for t in altered} == {('test_set_task_instance_state', 'task_1', dagrun.run_id, 1, -1)}


def test_set_task_instance_state_mapped(dag_maker, session):
    """Test that when setting an individual mapped TI that the other TIs are not affected"""
    task_id = 't1'

    with dag_maker(session=session) as dag:

        @dag.task
        def make_arg_lists():
            return [[1], [2], [{'a': 'b'}]]

        def consumer(value):
            print(value)

        mapped = PythonOperator.partial(task_id=task_id, dag=dag, python_callable=consumer).expand(
            op_args=make_arg_lists()
        )

        mapped >> BaseOperator(task_id='downstream')

    dr1 = dag_maker.create_dagrun(
        run_type=DagRunType.SCHEDULED,
        state=DagRunState.FAILED,
    )
    expand_mapped_task(mapped, dr1.run_id, "make_arg_lists", length=2, session=session)

    # set_state(future=True) only applies to scheduled runs
    dr2 = dag_maker.create_dagrun(
        run_type=DagRunType.SCHEDULED,
        state=DagRunState.FAILED,
        execution_date=DEFAULT_DATE + datetime.timedelta(days=1),
    )
    expand_mapped_task(mapped, dr2.run_id, "make_arg_lists", length=2, session=session)

    session.query(TI).filter_by(dag_id=dag.dag_id).update({'state': TaskInstanceState.FAILED})

    ti_query = (
        session.query(TI.task_id, TI.map_index, TI.run_id, TI.state)
        .filter(TI.dag_id == dag.dag_id, TI.task_id.in_([task_id, 'downstream']))
        .order_by(TI.run_id, TI.task_id, TI.map_index)
    )

    # Check pre-conditions
    assert ti_query.all() == [
        ('downstream', -1, dr1.run_id, TaskInstanceState.FAILED),
        (task_id, 0, dr1.run_id, TaskInstanceState.FAILED),
        (task_id, 1, dr1.run_id, TaskInstanceState.FAILED),
        ('downstream', -1, dr2.run_id, TaskInstanceState.FAILED),
        (task_id, 0, dr2.run_id, TaskInstanceState.FAILED),
        (task_id, 1, dr2.run_id, TaskInstanceState.FAILED),
    ]

    dag.set_task_instance_state(
        task_id=task_id,
        map_indexes=[1],
        future=True,
        run_id=dr1.run_id,
        state=TaskInstanceState.SUCCESS,
        session=session,
    )
    assert dr1 in session, "Check session is passed down all the way"

    assert ti_query.all() == [
        ('downstream', -1, dr1.run_id, None),
        (task_id, 0, dr1.run_id, TaskInstanceState.FAILED),
        (task_id, 1, dr1.run_id, TaskInstanceState.SUCCESS),
        ('downstream', -1, dr2.run_id, None),
        (task_id, 0, dr2.run_id, TaskInstanceState.FAILED),
        (task_id, 1, dr2.run_id, TaskInstanceState.SUCCESS),
    ]


@pytest.mark.parametrize(
    "start_date, expected_infos",
    [
        (
            DEFAULT_DATE,
            [DagRunInfo.interval(DEFAULT_DATE, DEFAULT_DATE + datetime.timedelta(hours=1))],
        ),
        (
            DEFAULT_DATE - datetime.timedelta(hours=3),
            [
                DagRunInfo.interval(
                    DEFAULT_DATE - datetime.timedelta(hours=3),
                    DEFAULT_DATE - datetime.timedelta(hours=2),
                ),
                DagRunInfo.interval(
                    DEFAULT_DATE - datetime.timedelta(hours=2),
                    DEFAULT_DATE - datetime.timedelta(hours=1),
                ),
                DagRunInfo.interval(
                    DEFAULT_DATE - datetime.timedelta(hours=1),
                    DEFAULT_DATE,
                ),
                DagRunInfo.interval(
                    DEFAULT_DATE,
                    DEFAULT_DATE + datetime.timedelta(hours=1),
                ),
            ],
        ),
    ],
    ids=["in-dag-restriction", "out-of-dag-restriction"],
)
def test_iter_dagrun_infos_between(start_date, expected_infos):
    dag = DAG(dag_id='test_get_dates', start_date=DEFAULT_DATE, schedule="@hourly")
    EmptyOperator(task_id='dummy', dag=dag)

    iterator = dag.iter_dagrun_infos_between(
        earliest=pendulum.instance(start_date),
        latest=pendulum.instance(DEFAULT_DATE),
        align=True,
    )
    assert expected_infos == list(iterator)


def test_iter_dagrun_infos_between_error(caplog):
    start = pendulum.instance(DEFAULT_DATE - datetime.timedelta(hours=1))
    end = pendulum.instance(DEFAULT_DATE)

    class FailingAfterOneTimetable(Timetable):
        def next_dagrun_info(self, last_automated_data_interval, restriction):
            if last_automated_data_interval is None:
                return DagRunInfo.interval(start, end)
            raise RuntimeError("this fails")

    dag = DAG(
        dag_id='test_iter_dagrun_infos_between_error',
        start_date=DEFAULT_DATE,
        timetable=FailingAfterOneTimetable(),
    )

    iterator = dag.iter_dagrun_infos_between(earliest=start, latest=end, align=True)
    with caplog.at_level(logging.ERROR):
        infos = list(iterator)

    # The second timetable.next_dagrun_info() call raises an exception, so only the first result is returned.
    assert infos == [DagRunInfo.interval(start, end)]

    assert caplog.record_tuples == [
        (
            "airflow.models.dag.DAG",
            logging.ERROR,
            f"Failed to fetch run info after data interval {DataInterval(start, end)} for DAG {dag.dag_id!r}",
        ),
    ]
    assert caplog.records[0].exc_info is not None, "should contain exception context"


@pytest.mark.parametrize(
    "logical_date, data_interval_start, data_interval_end, expected_data_interval",
    [
        pytest.param(None, None, None, None, id="no-next-run"),
        pytest.param(
            DEFAULT_DATE,
            DEFAULT_DATE,
            DEFAULT_DATE + timedelta(days=2),
            DataInterval(DEFAULT_DATE, DEFAULT_DATE + timedelta(days=2)),
            id="modern",
        ),
        pytest.param(
            DEFAULT_DATE,
            None,
            None,
            DataInterval(DEFAULT_DATE, DEFAULT_DATE + timedelta(days=1)),
            id="legacy",
        ),
    ],
)
def test_get_next_data_interval(
    logical_date,
    data_interval_start,
    data_interval_end,
    expected_data_interval,
):
    dag = DAG(dag_id="test_get_next_data_interval", schedule="@daily")
    dag_model = DagModel(
        dag_id="test_get_next_data_interval",
        next_dagrun=logical_date,
        next_dagrun_data_interval_start=data_interval_start,
        next_dagrun_data_interval_end=data_interval_end,
    )

    assert dag.get_next_data_interval(dag_model) == expected_data_interval


@pytest.mark.parametrize(
    ('dag_date', 'tasks_date', 'restrict'),
    [
        [
            (DEFAULT_DATE, None),
            [
                (DEFAULT_DATE + timedelta(days=1), DEFAULT_DATE + timedelta(days=2)),
                (DEFAULT_DATE + timedelta(days=3), DEFAULT_DATE + timedelta(days=4)),
            ],
            TimeRestriction(DEFAULT_DATE, DEFAULT_DATE + timedelta(days=4), True),
        ],
        [
            (DEFAULT_DATE, None),
            [(DEFAULT_DATE, DEFAULT_DATE + timedelta(days=1)), (DEFAULT_DATE, None)],
            TimeRestriction(DEFAULT_DATE, None, True),
        ],
    ],
)
def test__time_restriction(dag_maker, dag_date, tasks_date, restrict):
    with dag_maker("test__time_restriction", start_date=dag_date[0], end_date=dag_date[1]) as dag:
        EmptyOperator(task_id="do1", start_date=tasks_date[0][0], end_date=tasks_date[0][1])
        EmptyOperator(task_id="do2", start_date=tasks_date[1][0], end_date=tasks_date[1][1])

    assert dag._time_restriction == restrict


@pytest.mark.parametrize(
    'tags, should_pass',
    [
        pytest.param([], True, id="empty tags"),
        pytest.param(['a normal tag'], True, id="one tag"),
        pytest.param(['a normal tag', 'another normal tag'], True, id="two tags"),
        pytest.param(['a' * 100], True, id="a tag that's of just length 100"),
        pytest.param(['a normal tag', 'a' * 101], False, id="two tags and one of them is of length > 100"),
    ],
)
def test__tags_length(tags: list[str], should_pass: bool):
    if should_pass:
        models.DAG('test-dag', tags=tags)
    else:
        with pytest.raises(AirflowException):
            models.DAG('test-dag', tags=tags)


@pytest.mark.need_serialized_dag
def test_get_dataset_triggered_next_run_info(dag_maker):
    dataset1 = Dataset(uri="ds1")
    dataset2 = Dataset(uri="ds2")
    dataset3 = Dataset(uri="ds3")
    with dag_maker(dag_id="datasets-1", schedule=[dataset2]):
        pass
    dag1 = dag_maker.dag

    with dag_maker(dag_id="datasets-2", schedule=[dataset1, dataset2]):
        pass
    dag2 = dag_maker.dag

    with dag_maker(dag_id="datasets-3", schedule=[dataset1, dataset2, dataset3]):
        pass
    dag3 = dag_maker.dag

    session = dag_maker.session
    ds1_id = session.query(DatasetModel.id).filter_by(uri=dataset1.uri).scalar()
    session.bulk_save_objects(
        [
            DatasetDagRunQueue(dataset_id=ds1_id, target_dag_id=dag2.dag_id),
            DatasetDagRunQueue(dataset_id=ds1_id, target_dag_id=dag3.dag_id),
        ]
    )
    session.flush()

    info = get_dataset_triggered_next_run_info([dag1.dag_id], session=session)
    assert info[dag1.dag_id] == {
        "ready": 0,
        "total": 1,
    }

    # This time, check both dag2 and dag3 at the same time (tests filtering)
    info = get_dataset_triggered_next_run_info([dag2.dag_id, dag3.dag_id], session=session)
    assert info[dag2.dag_id] == {
        "ready": 1,
        "total": 2,
    }
    assert info[dag3.dag_id] == {
        "ready": 1,
        "total": 3,
    }


def test_dag_uses_timetable_for_run_id(session):
    class CustomRunIdTimetable(Timetable):
        def generate_run_id(self, *, run_type, logical_date, data_interval, **extra) -> str:
            return 'abc'

    dag = DAG(dag_id='test', start_date=DEFAULT_DATE, schedule=CustomRunIdTimetable())

    dag_run = dag.create_dagrun(
        run_type=DagRunType.MANUAL,
        state=DagRunState.QUEUED,
        execution_date=DEFAULT_DATE,
        data_interval=(DEFAULT_DATE, DEFAULT_DATE),
    )

    assert dag_run.run_id == 'abc'