ephraimbuddy commented on a change in pull request #16401:
URL: https://github.com/apache/airflow/pull/16401#discussion_r660073357
##########
File path: airflow/models/taskinstance.py
##########
@@ -237,7 +237,8 @@ def clear_task_instances(
)
for dr in drs:
dr.state = dag_run_state
- dr.start_date = timezone.utcnow()
+ dr.start_date = None
+ dr.last_scheduling_decision = None
Review comment:
I have set this and added test. Thanks!
##########
File path: tests/models/test_cleartasks.py
##########
@@ -68,6 +70,41 @@ def test_clear_task_instances(self):
assert ti1.try_number == 2
assert ti1.max_tries == 3
+ @parameterized.expand([(State.QUEUED, None), (State.RUNNING,
DEFAULT_DATE)])
+ def test_clear_task_instances_dr_state(self, state, last_scheduling):
+ """Test that DR state is set to None after clear.
+ And that DR.last_scheduling_decision is handled OK.
+ start
Review comment:
```suggestion
start_date is also set to None
```
##########
File path: airflow/jobs/scheduler_job.py
##########
@@ -980,89 +952,86 @@ def _create_dag_runs(self, dag_models:
Iterable[DagModel], session: Session) ->
# are not updated.
# We opted to check DagRun existence instead
# of catching an Integrity error and rolling back the session i.e
- # we need to run self._update_dag_next_dagruns if the Dag Run
already exists or if we
+ # we need to set dag.next_dagrun_info if the Dag Run already
exists or if we
# create a new one. This is so that in the next Scheduling loop we
try to create new runs
# instead of falling in a loop of Integrity Error.
- if (dag.dag_id, dag_model.next_dagrun) not in active_dagruns:
- run = dag.create_dagrun(
+ if (dag.dag_id, dag_model.next_dagrun) not in existing_dagruns:
+ dag.create_dagrun(
run_type=DagRunType.SCHEDULED,
execution_date=dag_model.next_dagrun,
- start_date=timezone.utcnow(),
- state=State.RUNNING,
+ state=State.QUEUED,
external_trigger=False,
session=session,
dag_hash=dag_hash,
creating_job_id=self.id,
)
-
- expected_start_date =
dag.following_schedule(run.execution_date)
- if expected_start_date:
- schedule_delay = run.start_date - expected_start_date
- Stats.timing(
- f'dagrun.schedule_delay.{dag.dag_id}',
- schedule_delay,
- )
-
- self._update_dag_next_dagruns(dag_models, session)
+ dag_model.next_dagrun, dag_model.next_dagrun_create_after =
dag.next_dagrun_info(
+ dag_model.next_dagrun
+ )
# TODO[HA]: Should we do a session.flush() so we don't have to keep
lots of state/object in
# memory for larger dags? or expunge_all()
- def _update_dag_next_dagruns(self, dag_models: Iterable[DagModel],
session: Session) -> None:
+ def _start_queued_dagruns(
+ self,
+ session: Session,
+ ) -> int:
"""
- Bulk update the next_dagrun and next_dagrun_create_after for all the
dags.
+ Find DagRuns in queued state and decide moving them to running state
- We batch the select queries to get info about all the dags at once
+ :param dag_run: The DagRun to schedule
"""
- # Check max_active_runs, to see if we are _now_ at the limit for any of
- # these dag? (we've just created a DagRun for them after all)
- active_runs_of_dags = dict(
+ dag_runs = self._get_next_dagruns_to_examine(State.QUEUED, session)
+
+ active_runs_of_dags = defaultdict(
+ lambda: 0,
session.query(DagRun.dag_id, func.count('*'))
- .filter(
- DagRun.dag_id.in_([o.dag_id for o in dag_models]),
+ .filter( # We use `list` here because SQLA doesn't accept a set
+ DagRun.dag_id.in_(list({dr.dag_id for dr in dag_runs})),
Review comment:
The reason for the set is so we don't have duplicate values. @ashb
suggested we use a set, What do you think?
##########
File path:
airflow/migrations/versions/a84a5abfca95_update_dagrun_state_and_datetime.py
##########
@@ -0,0 +1,70 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""update-dagrun-state-and-datetime
+
+Revision ID: a84a5abfca95
+Revises: 30867afad44a
+Create Date: 2021-06-13 11:08:18.705168
+
+"""
+import sqlalchemy as sa
+from alembic import op
+
+from airflow.utils.dates import timezone
+from airflow.utils.state import State
+
+# revision identifiers, used by Alembic.
+revision = 'a84a5abfca95'
+down_revision = '30867afad44a'
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+ """Apply Set default start_time for dagrun to None and set default state
to QUEUED"""
+ with op.batch_alter_table('dag_run') as batch_op:
+ batch_op.alter_column(
+ 'start_date',
+ type_=sa.TIMESTAMP(timezone=True),
+ existing_server_default=timezone.utcnow(),
+ server_default=None,
+ )
+ batch_op.alter_column(
+ 'state',
+ type_=sa.String(length=50),
+ existing_server_default=State.RUNNING,
+ server_default=State.QUEUED,
Review comment:
It works without migration but I'm a bit confused. I will remove it then
##########
File path: airflow/jobs/scheduler_job.py
##########
@@ -980,89 +952,86 @@ def _create_dag_runs(self, dag_models:
Iterable[DagModel], session: Session) ->
# are not updated.
# We opted to check DagRun existence instead
# of catching an Integrity error and rolling back the session i.e
- # we need to run self._update_dag_next_dagruns if the Dag Run
already exists or if we
+ # we need to set dag.next_dagrun_info if the Dag Run already
exists or if we
# create a new one. This is so that in the next Scheduling loop we
try to create new runs
# instead of falling in a loop of Integrity Error.
- if (dag.dag_id, dag_model.next_dagrun) not in active_dagruns:
- run = dag.create_dagrun(
+ if (dag.dag_id, dag_model.next_dagrun) not in existing_dagruns:
+ dag.create_dagrun(
run_type=DagRunType.SCHEDULED,
execution_date=dag_model.next_dagrun,
- start_date=timezone.utcnow(),
- state=State.RUNNING,
+ state=State.QUEUED,
external_trigger=False,
session=session,
dag_hash=dag_hash,
creating_job_id=self.id,
)
-
- expected_start_date =
dag.following_schedule(run.execution_date)
- if expected_start_date:
- schedule_delay = run.start_date - expected_start_date
- Stats.timing(
- f'dagrun.schedule_delay.{dag.dag_id}',
- schedule_delay,
- )
-
- self._update_dag_next_dagruns(dag_models, session)
+ dag_model.next_dagrun, dag_model.next_dagrun_create_after =
dag.next_dagrun_info(
+ dag_model.next_dagrun
+ )
# TODO[HA]: Should we do a session.flush() so we don't have to keep
lots of state/object in
# memory for larger dags? or expunge_all()
- def _update_dag_next_dagruns(self, dag_models: Iterable[DagModel],
session: Session) -> None:
+ def _start_queued_dagruns(
+ self,
+ session: Session,
+ ) -> int:
"""
- Bulk update the next_dagrun and next_dagrun_create_after for all the
dags.
+ Find DagRuns in queued state and decide moving them to running state
- We batch the select queries to get info about all the dags at once
+ :param dag_run: The DagRun to schedule
"""
- # Check max_active_runs, to see if we are _now_ at the limit for any of
- # these dag? (we've just created a DagRun for them after all)
- active_runs_of_dags = dict(
+ dag_runs = self._get_next_dagruns_to_examine(State.QUEUED, session)
+
+ active_runs_of_dags = defaultdict(
+ lambda: 0,
session.query(DagRun.dag_id, func.count('*'))
- .filter(
- DagRun.dag_id.in_([o.dag_id for o in dag_models]),
+ .filter( # We use `list` here because SQLA doesn't accept a set
+ DagRun.dag_id.in_(list({dr.dag_id for dr in dag_runs})),
Review comment:
I will update the comment
##########
File path: airflow/jobs/scheduler_job.py
##########
@@ -980,89 +952,86 @@ def _create_dag_runs(self, dag_models:
Iterable[DagModel], session: Session) ->
# are not updated.
# We opted to check DagRun existence instead
# of catching an Integrity error and rolling back the session i.e
- # we need to run self._update_dag_next_dagruns if the Dag Run
already exists or if we
+ # we need to set dag.next_dagrun_info if the Dag Run already
exists or if we
# create a new one. This is so that in the next Scheduling loop we
try to create new runs
# instead of falling in a loop of Integrity Error.
- if (dag.dag_id, dag_model.next_dagrun) not in active_dagruns:
- run = dag.create_dagrun(
+ if (dag.dag_id, dag_model.next_dagrun) not in existing_dagruns:
+ dag.create_dagrun(
run_type=DagRunType.SCHEDULED,
execution_date=dag_model.next_dagrun,
- start_date=timezone.utcnow(),
- state=State.RUNNING,
+ state=State.QUEUED,
Review comment:
Yeah. That's cool, separate PR is cool
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
