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test_scheduler_job.py
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test_scheduler_job.py
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#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations
import contextlib
import datetime
import logging
import os
from collections import deque
from datetime import timedelta
from importlib import reload
from typing import Generator
from unittest import mock
from unittest.mock import MagicMock, PropertyMock, patch
import psutil
import pytest
import time_machine
from sqlalchemy import func, select, update
import airflow.example_dags
from airflow import settings
from airflow.callbacks.callback_requests import DagCallbackRequest, SlaCallbackRequest, TaskCallbackRequest
from airflow.callbacks.database_callback_sink import DatabaseCallbackSink
from airflow.callbacks.pipe_callback_sink import PipeCallbackSink
from airflow.dag_processing.manager import DagFileProcessorAgent
from airflow.datasets import Dataset
from airflow.datasets.manager import DatasetManager
from airflow.exceptions import AirflowException
from airflow.executors.base_executor import BaseExecutor
from airflow.executors.executor_constants import MOCK_EXECUTOR
from airflow.executors.executor_loader import ExecutorLoader
from airflow.jobs.backfill_job_runner import BackfillJobRunner
from airflow.jobs.job import Job, run_job
from airflow.jobs.local_task_job_runner import LocalTaskJobRunner
from airflow.jobs.scheduler_job_runner import SchedulerJobRunner
from airflow.models.dag import DAG, DagModel
from airflow.models.dagbag import DagBag
from airflow.models.dagrun import DagRun
from airflow.models.dataset import DatasetDagRunQueue, DatasetEvent, DatasetModel
from airflow.models.db_callback_request import DbCallbackRequest
from airflow.models.pool import Pool
from airflow.models.serialized_dag import SerializedDagModel
from airflow.models.taskinstance import SimpleTaskInstance, TaskInstance, TaskInstanceKey
from airflow.operators.bash import BashOperator
from airflow.operators.empty import EmptyOperator
from airflow.serialization.serialized_objects import SerializedDAG
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, JobState, State, TaskInstanceState
from airflow.utils.types import DagRunType
from tests.listeners import dag_listener
from tests.listeners.test_listeners import get_listener_manager
from tests.models import TEST_DAGS_FOLDER
from tests.test_utils.asserts import assert_queries_count
from tests.test_utils.config import conf_vars, env_vars
from tests.test_utils.db import (
clear_db_dags,
clear_db_datasets,
clear_db_import_errors,
clear_db_jobs,
clear_db_pools,
clear_db_runs,
clear_db_serialized_dags,
clear_db_sla_miss,
set_default_pool_slots,
)
from tests.test_utils.mock_executor import MockExecutor
from tests.test_utils.mock_operators import CustomOperator
from tests.utils.test_timezone import UTC
pytestmark = pytest.mark.db_test
ROOT_FOLDER = os.path.realpath(
os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir, os.pardir)
)
PERF_DAGS_FOLDER = os.path.join(ROOT_FOLDER, "tests", "test_utils", "perf", "dags")
ELASTIC_DAG_FILE = os.path.join(PERF_DAGS_FOLDER, "elastic_dag.py")
TEST_DAG_FOLDER = os.environ["AIRFLOW__CORE__DAGS_FOLDER"]
DEFAULT_DATE = timezone.datetime(2016, 1, 1)
DEFAULT_LOGICAL_DATE = timezone.coerce_datetime(DEFAULT_DATE)
TRY_NUMBER = 1
@pytest.fixture(scope="class")
def disable_load_example():
with conf_vars({("core", "load_examples"): "false"}):
with env_vars({"AIRFLOW__CORE__LOAD_EXAMPLES": "false"}):
yield
@pytest.fixture(scope="module")
def dagbag():
# Ensure the DAGs we are looking at from the DB are up-to-date
non_serialized_dagbag = DagBag(read_dags_from_db=False, include_examples=False)
non_serialized_dagbag.sync_to_db()
return DagBag(read_dags_from_db=True)
@pytest.fixture
def load_examples():
with conf_vars({("core", "load_examples"): "True"}):
yield
# Patch the MockExecutor into the dict of known executors in the Loader
@patch.dict(
ExecutorLoader.executors, {MOCK_EXECUTOR: f"{MockExecutor.__module__}.{MockExecutor.__qualname__}"}
)
@pytest.mark.usefixtures("disable_load_example")
@pytest.mark.need_serialized_dag
class TestSchedulerJob:
@staticmethod
def clean_db():
clear_db_runs()
clear_db_pools()
clear_db_dags()
clear_db_sla_miss()
clear_db_import_errors()
clear_db_jobs()
clear_db_datasets()
# DO NOT try to run clear_db_serialized_dags() here - this will break the tests
# The tests expect DAGs to be fully loaded here via setUpClass method below
@pytest.fixture(autouse=True)
def per_test(self) -> Generator:
self.clean_db()
self.job_runner = None
yield
if self.job_runner and self.job_runner.processor_agent:
self.job_runner.processor_agent.end()
self.job_runner = None
self.clean_db()
@pytest.fixture(autouse=True)
def set_instance_attrs(self, dagbag) -> Generator:
self.dagbag: DagBag = dagbag
# Speed up some tests by not running the tasks, just look at what we
# enqueue!
self.null_exec: MockExecutor | None = MockExecutor()
# Since we don't want to store the code for the DAG defined in this file
with patch("airflow.dag_processing.manager.SerializedDagModel.remove_deleted_dags"), patch(
"airflow.models.dag.DagCode.bulk_sync_to_db"
):
yield
self.null_exec = None
del self.dagbag
@pytest.fixture
def mock_executors(self):
default_executor = mock.MagicMock(slots_available=8, slots_occupied=0)
default_executor.name = MagicMock(alias="default_exec", module_path="default.exec.module.path")
second_executor = mock.MagicMock(slots_available=8, slots_occupied=0)
second_executor.name = MagicMock(alias="secondary_exec", module_path="secondary.exec.module.path")
with mock.patch("airflow.jobs.job.Job.executors", new_callable=PropertyMock) as executors_mock:
with mock.patch("airflow.jobs.job.Job.executor", new_callable=PropertyMock) as executor_mock:
executor_mock.return_value = default_executor
executors_mock.return_value = [default_executor, second_executor]
yield [default_executor, second_executor]
@pytest.mark.parametrize(
"configs",
[
{("scheduler", "standalone_dag_processor"): "False"},
{("scheduler", "standalone_dag_processor"): "True"},
],
)
def test_is_alive(self, configs):
with conf_vars(configs):
scheduler_job = Job(heartrate=10, state=State.RUNNING)
self.job_runner = SchedulerJobRunner(scheduler_job)
assert scheduler_job.is_alive()
scheduler_job.latest_heartbeat = timezone.utcnow() - datetime.timedelta(seconds=20)
assert scheduler_job.is_alive()
scheduler_job.latest_heartbeat = timezone.utcnow() - datetime.timedelta(seconds=31)
assert not scheduler_job.is_alive()
# test because .seconds was used before instead of total_seconds
# internal repr of datetime is (days, seconds)
scheduler_job.latest_heartbeat = timezone.utcnow() - datetime.timedelta(days=1)
assert not scheduler_job.is_alive()
scheduler_job.state = State.SUCCESS
scheduler_job.latest_heartbeat = timezone.utcnow() - datetime.timedelta(seconds=10)
assert (
not scheduler_job.is_alive()
), "Completed jobs even with recent heartbeat should not be alive"
@pytest.mark.parametrize(
"heartrate",
[10, 5],
)
def test_heartrate(self, heartrate):
with conf_vars({("scheduler", "scheduler_heartbeat_sec"): str(heartrate)}):
scheduler_job = Job(executor=self.null_exec)
_ = SchedulerJobRunner(job=scheduler_job)
assert scheduler_job.heartrate == heartrate
def run_single_scheduler_loop_with_no_dags(self, dags_folder):
"""
Utility function that runs a single scheduler loop without actually
changing/scheduling any dags. This is useful to simulate the other side effects of
running a scheduler loop, e.g. to see what parse errors there are in the
dags_folder.
:param dags_folder: the directory to traverse
"""
scheduler_job = Job(
executor=self.null_exec,
num_times_parse_dags=1,
subdir=os.path.join(dags_folder),
)
self.job_runner = SchedulerJobRunner(scheduler_job)
scheduler_job.heartrate = 0
run_job(scheduler_job, execute_callable=self.job_runner._execute)
def test_no_orphan_process_will_be_left(self, tmp_path):
current_process = psutil.Process()
old_children = current_process.children(recursive=True)
scheduler_job = Job(
executor=MockExecutor(do_update=False),
)
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.fspath(tmp_path), num_runs=1)
run_job(scheduler_job, execute_callable=self.job_runner._execute)
# Remove potential noise created by previous tests.
current_children = set(current_process.children(recursive=True)) - set(old_children)
assert not current_children
@mock.patch("airflow.jobs.scheduler_job_runner.TaskCallbackRequest")
@mock.patch("airflow.jobs.scheduler_job_runner.Stats.incr")
def test_process_executor_events(self, mock_stats_incr, mock_task_callback, dag_maker):
dag_id = "test_process_executor_events"
task_id_1 = "dummy_task"
session = settings.Session()
with dag_maker(dag_id=dag_id, fileloc="/test_path1/"):
task1 = EmptyOperator(task_id=task_id_1)
ti1 = dag_maker.create_dagrun().get_task_instance(task1.task_id)
mock_stats_incr.reset_mock()
executor = MockExecutor(do_update=False)
task_callback = mock.MagicMock()
mock_task_callback.return_value = task_callback
scheduler_job = Job(executor=executor)
self.job_runner = SchedulerJobRunner(scheduler_job)
self.job_runner.processor_agent = mock.MagicMock()
ti1.state = State.QUEUED
session.merge(ti1)
session.commit()
executor.event_buffer[ti1.key] = State.FAILED, None
self.job_runner._process_executor_events(session=session)
ti1.refresh_from_db(session=session)
assert ti1.state == State.FAILED
scheduler_job.executor.callback_sink.send.assert_not_called()
self.job_runner.processor_agent.reset_mock()
# ti in success state
ti1.state = State.SUCCESS
session.merge(ti1)
session.commit()
executor.event_buffer[ti1.key] = State.SUCCESS, None
self.job_runner._process_executor_events(session=session)
ti1.refresh_from_db(session=session)
assert ti1.state == State.SUCCESS
scheduler_job.executor.callback_sink.send.assert_not_called()
mock_stats_incr.assert_has_calls(
[
mock.call(
"scheduler.tasks.killed_externally",
tags={"dag_id": dag_id, "task_id": ti1.task_id},
),
mock.call("operator_failures_EmptyOperator", tags={"dag_id": dag_id, "task_id": ti1.task_id}),
mock.call("ti_failures", tags={"dag_id": dag_id, "task_id": ti1.task_id}),
],
any_order=True,
)
@mock.patch("airflow.jobs.scheduler_job_runner.TaskCallbackRequest")
@mock.patch("airflow.jobs.scheduler_job_runner.Stats.incr")
def test_process_executor_events_with_no_callback(self, mock_stats_incr, mock_task_callback, dag_maker):
dag_id = "test_process_executor_events_with_no_callback"
task_id = "test_task"
run_id = "test_run"
mock_stats_incr.reset_mock()
executor = MockExecutor(do_update=False)
task_callback = mock.MagicMock()
mock_task_callback.return_value = task_callback
scheduler_job = Job(executor=executor)
self.job_runner = SchedulerJobRunner(scheduler_job)
self.job_runner.processor_agent = mock.MagicMock()
session = settings.Session()
with dag_maker(dag_id=dag_id, fileloc="/test_path1/"):
task1 = EmptyOperator(task_id=task_id, retries=1)
ti1 = dag_maker.create_dagrun(
run_id=run_id, execution_date=DEFAULT_DATE + timedelta(hours=1)
).get_task_instance(task1.task_id)
mock_stats_incr.reset_mock()
executor = MockExecutor(do_update=False)
task_callback = mock.MagicMock()
mock_task_callback.return_value = task_callback
scheduler_job = Job(executor=executor)
self.job_runner = SchedulerJobRunner(scheduler_job)
self.job_runner.processor_agent = mock.MagicMock()
ti1.state = State.QUEUED
session.merge(ti1)
session.commit()
executor.event_buffer[ti1.key] = State.FAILED, None
self.job_runner._process_executor_events(session=session)
ti1.refresh_from_db(session=session)
assert ti1.state == State.UP_FOR_RETRY
scheduler_job.executor.callback_sink.send.assert_not_called()
# ti in success state
ti1.state = State.SUCCESS
session.merge(ti1)
session.commit()
executor.event_buffer[ti1.key] = State.SUCCESS, None
self.job_runner._process_executor_events(session=session)
ti1.refresh_from_db(session=session)
assert ti1.state == State.SUCCESS
scheduler_job.executor.callback_sink.send.assert_not_called()
mock_stats_incr.assert_has_calls(
[
mock.call(
"scheduler.tasks.killed_externally",
tags={"dag_id": dag_id, "task_id": task_id},
),
mock.call("operator_failures_EmptyOperator", tags={"dag_id": dag_id, "task_id": task_id}),
mock.call("ti_failures", tags={"dag_id": dag_id, "task_id": task_id}),
],
any_order=True,
)
@mock.patch("airflow.jobs.scheduler_job_runner.TaskCallbackRequest")
@mock.patch("airflow.jobs.scheduler_job_runner.Stats.incr")
def test_process_executor_events_with_callback(self, mock_stats_incr, mock_task_callback, dag_maker):
dag_id = "test_process_executor_events_with_callback"
task_id_1 = "dummy_task"
with dag_maker(dag_id=dag_id, fileloc="/test_path1/") as dag:
task1 = EmptyOperator(task_id=task_id_1, on_failure_callback=lambda x: print("hi"))
ti1 = dag_maker.create_dagrun().get_task_instance(task1.task_id)
mock_stats_incr.reset_mock()
executor = MockExecutor(do_update=False)
task_callback = mock.MagicMock()
mock_task_callback.return_value = task_callback
scheduler_job = Job(executor=executor)
self.job_runner = SchedulerJobRunner(scheduler_job)
self.job_runner.processor_agent = mock.MagicMock()
session = settings.Session()
ti1.state = State.QUEUED
session.merge(ti1)
session.commit()
executor.event_buffer[ti1.key] = State.FAILED, None
self.job_runner._process_executor_events(session=session)
ti1.refresh_from_db()
# The state will remain in queued here and
# will be set to failed in dag parsing process
assert ti1.state == State.QUEUED
mock_task_callback.assert_called_once_with(
full_filepath=dag.fileloc,
simple_task_instance=mock.ANY,
processor_subdir=None,
msg="The executor reported that the task instance "
"<TaskInstance: test_process_executor_events_with_callback.dummy_task test [queued]> "
"finished with state failed, but the task instance's state attribute is queued. "
"Learn more: https://airflow.apache.org/docs/apache-airflow/stable/troubleshooting.html#task-state-changed-externally",
)
scheduler_job.executor.callback_sink.send.assert_called_once_with(task_callback)
scheduler_job.executor.callback_sink.reset_mock()
mock_stats_incr.assert_called_once_with(
"scheduler.tasks.killed_externally",
tags={
"dag_id": "test_process_executor_events_with_callback",
"task_id": "dummy_task",
},
)
@mock.patch("airflow.jobs.scheduler_job_runner.TaskCallbackRequest")
@mock.patch("airflow.jobs.scheduler_job_runner.Stats.incr")
def test_process_executor_event_missing_dag(self, mock_stats_incr, mock_task_callback, dag_maker, caplog):
dag_id = "test_process_executor_events_with_callback"
task_id_1 = "dummy_task"
with dag_maker(dag_id=dag_id, fileloc="/test_path1/"):
task1 = EmptyOperator(task_id=task_id_1, on_failure_callback=lambda x: print("hi"))
ti1 = dag_maker.create_dagrun().get_task_instance(task1.task_id)
mock_stats_incr.reset_mock()
executor = MockExecutor(do_update=False)
task_callback = mock.MagicMock()
mock_task_callback.return_value = task_callback
scheduler_job = Job(executor=executor)
self.job_runner = SchedulerJobRunner(scheduler_job)
self.job_runner.dagbag = mock.MagicMock()
self.job_runner.dagbag.get_dag.side_effect = Exception("failed")
self.job_runner.processor_agent = mock.MagicMock()
session = settings.Session()
ti1.state = State.QUEUED
session.merge(ti1)
session.commit()
executor.event_buffer[ti1.key] = State.FAILED, None
self.job_runner._process_executor_events(session=session)
ti1.refresh_from_db()
assert ti1.state == State.FAILED
@mock.patch("airflow.jobs.scheduler_job_runner.TaskCallbackRequest")
@mock.patch("airflow.jobs.scheduler_job_runner.Stats.incr")
def test_process_executor_events_ti_requeued(self, mock_stats_incr, mock_task_callback, dag_maker):
dag_id = "test_process_executor_events_ti_requeued"
task_id_1 = "dummy_task"
session = settings.Session()
with dag_maker(dag_id=dag_id, fileloc="/test_path1/"):
task1 = EmptyOperator(task_id=task_id_1)
ti1 = dag_maker.create_dagrun().get_task_instance(task1.task_id)
mock_stats_incr.reset_mock()
executor = MockExecutor(do_update=False)
task_callback = mock.MagicMock()
mock_task_callback.return_value = task_callback
scheduler_job = Job(executor=executor)
self.job_runner = SchedulerJobRunner(scheduler_job)
self.id = 1
self.job_runner.processor_agent = mock.MagicMock()
# ti is queued with another try number - do not fail it
ti1.state = State.QUEUED
ti1.queued_by_job_id = 1
ti1.try_number = 2
session.merge(ti1)
session.commit()
executor.event_buffer[ti1.key.with_try_number(1)] = State.SUCCESS, None
self.job_runner._process_executor_events(session=session)
ti1.refresh_from_db(session=session)
assert ti1.state == State.QUEUED
scheduler_job.executor.callback_sink.send.assert_not_called()
# ti is queued by another scheduler - do not fail it
ti1.state = State.QUEUED
ti1.queued_by_job_id = 2
session.merge(ti1)
session.commit()
executor.event_buffer[ti1.key] = State.SUCCESS, None
self.job_runner._process_executor_events(session=session)
ti1.refresh_from_db(session=session)
assert ti1.state == State.QUEUED
scheduler_job.executor.callback_sink.send.assert_not_called()
# ti is queued by this scheduler but it is handed back to the executor - do not fail it
ti1.state = State.QUEUED
ti1.queued_by_job_id = 1
session.merge(ti1)
session.commit()
executor.event_buffer[ti1.key] = State.SUCCESS, None
executor.has_task = mock.MagicMock(return_value=True)
self.job_runner._process_executor_events(session=session)
ti1.refresh_from_db(session=session)
assert ti1.state == State.QUEUED
scheduler_job.executor.callback_sink.send.assert_not_called()
mock_stats_incr.assert_not_called()
def test_execute_task_instances_is_paused_wont_execute(self, session, dag_maker):
dag_id = "SchedulerJobTest.test_execute_task_instances_is_paused_wont_execute"
task_id_1 = "dummy_task"
with dag_maker(dag_id=dag_id, session=session) as dag:
EmptyOperator(task_id=task_id_1)
assert isinstance(dag, SerializedDAG)
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull)
dr1 = dag_maker.create_dagrun(run_type=DagRunType.BACKFILL_JOB)
(ti1,) = dr1.task_instances
ti1.state = State.SCHEDULED
self.job_runner._critical_section_enqueue_task_instances(session)
session.flush()
ti1.refresh_from_db(session=session)
assert State.SCHEDULED == ti1.state
session.rollback()
def test_execute_task_instances_backfill_tasks_wont_execute(self, dag_maker):
"""
Tests that backfill tasks won't get executed.
"""
dag_id = "SchedulerJobTest.test_execute_task_instances_backfill_tasks_wont_execute"
task_id_1 = "dummy_task"
with dag_maker(dag_id=dag_id):
task1 = EmptyOperator(task_id=task_id_1)
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull)
session = settings.Session()
dr1 = dag_maker.create_dagrun(run_type=DagRunType.BACKFILL_JOB)
ti1 = TaskInstance(task1, run_id=dr1.run_id)
ti1.refresh_from_db()
ti1.state = State.SCHEDULED
session.merge(ti1)
session.flush()
assert dr1.is_backfill
self.job_runner._critical_section_enqueue_task_instances(session)
session.flush()
ti1.refresh_from_db()
assert State.SCHEDULED == ti1.state
session.rollback()
@conf_vars({("scheduler", "standalone_dag_processor"): "False"})
def test_setup_callback_sink_not_standalone_dag_processor(self):
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull, num_runs=1)
self.job_runner._execute()
assert isinstance(scheduler_job.executor.callback_sink, PipeCallbackSink)
@mock.patch("airflow.executors.executor_loader.ExecutorLoader.init_executors")
@mock.patch("airflow.executors.executor_loader.ExecutorLoader.get_default_executor")
@conf_vars({("scheduler", "standalone_dag_processor"): "False"})
def test_setup_callback_sink_not_standalone_dag_processor_multiple_executors(
self, get_default_executor_mock, init_executors_mock
):
default_executor = mock.MagicMock(slots_available=8)
second_executor = mock.MagicMock(slots_available=8)
init_executors_mock.return_value = [default_executor, second_executor]
get_default_executor_mock.return_value = default_executor
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull, num_runs=1)
self.job_runner._execute()
for executor in scheduler_job.executors:
assert isinstance(executor.callback_sink, PipeCallbackSink)
@conf_vars({("scheduler", "standalone_dag_processor"): "True"})
def test_setup_callback_sink_standalone_dag_processor(self):
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull, num_runs=1)
self.job_runner._execute()
assert isinstance(scheduler_job.executor.callback_sink, DatabaseCallbackSink)
@mock.patch("airflow.jobs.job.Job.executors", new_callable=PropertyMock)
@mock.patch("airflow.jobs.job.Job.executor", new_callable=PropertyMock)
@conf_vars({("scheduler", "standalone_dag_processor"): "True"})
def test_setup_callback_sink_standalone_dag_processor_multiple_executors(
self, executor_mock, executors_mock
):
default_executor = mock.MagicMock(slots_available=8)
second_executor = mock.MagicMock(slots_available=8)
executor_mock.return_value = default_executor
executors_mock.return_value = [default_executor, second_executor]
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull, num_runs=1)
self.job_runner._execute()
for executor in scheduler_job.executors:
assert isinstance(executor.callback_sink, DatabaseCallbackSink)
@mock.patch("airflow.jobs.job.Job.executors", new_callable=PropertyMock)
@mock.patch("airflow.jobs.job.Job.executor", new_callable=PropertyMock)
@conf_vars({("scheduler", "standalone_dag_processor"): "True"})
def test_executor_start_called(self, executor_mock, executors_mock):
default_executor = mock.MagicMock(slots_available=8)
second_executor = mock.MagicMock(slots_available=8)
executor_mock.return_value = default_executor
executors_mock.return_value = [default_executor, second_executor]
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull, num_runs=1)
self.job_runner._execute()
scheduler_job.executor.start.assert_called_once()
for executor in scheduler_job.executors:
executor.start.assert_called_once()
@mock.patch("airflow.jobs.job.Job.executors", new_callable=PropertyMock)
@mock.patch("airflow.jobs.job.Job.executor", new_callable=PropertyMock)
def test_executor_job_id_assigned(self, executor_mock, executors_mock):
default_executor = mock.MagicMock(slots_available=8)
second_executor = mock.MagicMock(slots_available=8)
executor_mock.return_value = default_executor
executors_mock.return_value = [default_executor, second_executor]
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull, num_runs=1)
self.job_runner._execute()
assert scheduler_job.executor.job_id == scheduler_job.id
for executor in scheduler_job.executors:
assert executor.job_id == scheduler_job.id
@mock.patch("airflow.jobs.job.Job.executors", new_callable=PropertyMock)
@mock.patch("airflow.jobs.job.Job.executor", new_callable=PropertyMock)
def test_executor_debug_dump(self, executor_mock, executors_mock):
default_executor = mock.MagicMock(slots_available=8)
second_executor = mock.MagicMock(slots_available=8)
executor_mock.return_value = default_executor
executors_mock.return_value = [default_executor, second_executor]
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull, num_runs=1)
self.job_runner._debug_dump(1, mock.MagicMock())
for executor in scheduler_job.executors:
executor.debug_dump.assert_called_once()
def test_find_executable_task_instances_backfill(self, dag_maker):
dag_id = "SchedulerJobTest.test_find_executable_task_instances_backfill"
task_id_1 = "dummy"
with dag_maker(dag_id=dag_id, max_active_tasks=16):
task1 = EmptyOperator(task_id=task_id_1)
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull)
session = settings.Session()
dr1 = dag_maker.create_dagrun(run_type=DagRunType.SCHEDULED)
dr2 = dag_maker.create_dagrun_after(dr1, run_type=DagRunType.BACKFILL_JOB, state=State.RUNNING)
ti_backfill = dr2.get_task_instance(task1.task_id)
ti_with_dagrun = dr1.get_task_instance(task1.task_id)
# ti_with_paused
ti_backfill.state = State.SCHEDULED
ti_with_dagrun.state = State.SCHEDULED
session.merge(dr2)
session.merge(ti_backfill)
session.merge(ti_with_dagrun)
session.flush()
res = self.job_runner._executable_task_instances_to_queued(max_tis=32, session=session)
assert 1 == len(res)
res_keys = (x.key for x in res)
assert ti_with_dagrun.key in res_keys
session.rollback()
def test_find_executable_task_instances_pool(self, dag_maker):
dag_id = "SchedulerJobTest.test_find_executable_task_instances_pool"
task_id_1 = "dummy"
task_id_2 = "dummydummy"
session = settings.Session()
with dag_maker(dag_id=dag_id, max_active_tasks=16, session=session):
EmptyOperator(task_id=task_id_1, pool="a", priority_weight=2)
EmptyOperator(task_id=task_id_2, pool="b", priority_weight=1)
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull)
dr1 = dag_maker.create_dagrun(run_type=DagRunType.SCHEDULED)
dr2 = dag_maker.create_dagrun_after(dr1, run_type=DagRunType.SCHEDULED)
tis = [
dr1.get_task_instance(task_id_1, session=session),
dr1.get_task_instance(task_id_2, session=session),
dr2.get_task_instance(task_id_1, session=session),
dr2.get_task_instance(task_id_2, session=session),
]
tis.sort(key=lambda ti: ti.key)
for ti in tis:
ti.state = State.SCHEDULED
session.merge(ti)
pool = Pool(pool="a", slots=1, description="haha", include_deferred=False)
pool2 = Pool(pool="b", slots=100, description="haha", include_deferred=False)
session.add(pool)
session.add(pool2)
session.flush()
res = self.job_runner._executable_task_instances_to_queued(max_tis=32, session=session)
session.flush()
assert 3 == len(res)
res_keys = []
for ti in res:
res_keys.append(ti.key)
assert tis[0].key in res_keys
assert tis[2].key in res_keys
assert tis[3].key in res_keys
session.rollback()
@pytest.mark.parametrize(
"state, total_executed_ti",
[
(DagRunState.SUCCESS, 0),
(DagRunState.FAILED, 0),
(DagRunState.RUNNING, 2),
(DagRunState.QUEUED, 0),
],
)
def test_find_executable_task_instances_only_running_dagruns(
self, state, total_executed_ti, dag_maker, session
):
"""Test that only task instances of 'running' dagruns are executed"""
dag_id = "SchedulerJobTest.test_find_executable_task_instances_only_running_dagruns"
task_id_1 = "dummy"
task_id_2 = "dummydummy"
with dag_maker(dag_id=dag_id, session=session):
EmptyOperator(task_id=task_id_1)
EmptyOperator(task_id=task_id_2)
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull)
dr = dag_maker.create_dagrun(state=state)
tis = dr.task_instances
for ti in tis:
ti.state = State.SCHEDULED
session.merge(ti)
session.flush()
res = self.job_runner._executable_task_instances_to_queued(max_tis=32, session=session)
session.flush()
assert total_executed_ti == len(res)
def test_find_executable_task_instances_order_execution_date(self, dag_maker):
"""
Test that task instances follow execution_date order priority. If two dagruns with
different execution dates are scheduled, tasks with earliest dagrun execution date will first
be executed
"""
dag_id_1 = "SchedulerJobTest.test_find_executable_task_instances_order_execution_date-a"
dag_id_2 = "SchedulerJobTest.test_find_executable_task_instances_order_execution_date-b"
task_id = "task-a"
session = settings.Session()
with dag_maker(dag_id=dag_id_1, max_active_tasks=16, session=session):
EmptyOperator(task_id=task_id)
dr1 = dag_maker.create_dagrun(execution_date=DEFAULT_DATE + timedelta(hours=1))
with dag_maker(dag_id=dag_id_2, max_active_tasks=16, session=session):
EmptyOperator(task_id=task_id)
dr2 = dag_maker.create_dagrun()
dr1 = session.merge(dr1, load=False)
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull)
tis = dr1.task_instances + dr2.task_instances
for ti in tis:
ti.state = State.SCHEDULED
session.merge(ti)
session.flush()
res = self.job_runner._executable_task_instances_to_queued(max_tis=1, session=session)
session.flush()
assert [ti.key for ti in res] == [tis[1].key]
session.rollback()
def test_find_executable_task_instances_order_priority(self, dag_maker):
dag_id_1 = "SchedulerJobTest.test_find_executable_task_instances_order_priority-a"
dag_id_2 = "SchedulerJobTest.test_find_executable_task_instances_order_priority-b"
task_id = "task-a"
session = settings.Session()
with dag_maker(dag_id=dag_id_1, max_active_tasks=16, session=session):
EmptyOperator(task_id=task_id, priority_weight=1)
dr1 = dag_maker.create_dagrun()
with dag_maker(dag_id=dag_id_2, max_active_tasks=16, session=session):
EmptyOperator(task_id=task_id, priority_weight=4)
dr2 = dag_maker.create_dagrun()
dr1 = session.merge(dr1, load=False)
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull)
tis = dr1.task_instances + dr2.task_instances
for ti in tis:
ti.state = State.SCHEDULED
session.merge(ti)
session.flush()
res = self.job_runner._executable_task_instances_to_queued(max_tis=1, session=session)
session.flush()
assert [ti.key for ti in res] == [tis[1].key]
session.rollback()
def test_find_executable_task_instances_order_priority_with_pools(self, dag_maker):
"""
The scheduler job should pick tasks with higher priority for execution
even if different pools are involved.
"""
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull)
session = settings.Session()
dag_id = "SchedulerJobTest.test_find_executable_task_instances_order_priority_with_pools"
session.add(Pool(pool="pool1", slots=32, include_deferred=False))
session.add(Pool(pool="pool2", slots=32, include_deferred=False))
with dag_maker(dag_id=dag_id, max_active_tasks=2):
op1 = EmptyOperator(task_id="dummy1", priority_weight=1, pool="pool1")
op2 = EmptyOperator(task_id="dummy2", priority_weight=2, pool="pool2")
op3 = EmptyOperator(task_id="dummy3", priority_weight=3, pool="pool1")
dag_run = dag_maker.create_dagrun(run_type=DagRunType.SCHEDULED)
ti1 = dag_run.get_task_instance(op1.task_id, session)
ti2 = dag_run.get_task_instance(op2.task_id, session)
ti3 = dag_run.get_task_instance(op3.task_id, session)
ti1.state = State.SCHEDULED
ti2.state = State.SCHEDULED
ti3.state = State.SCHEDULED
session.flush()
res = self.job_runner._executable_task_instances_to_queued(max_tis=32, session=session)
assert 2 == len(res)
assert ti3.key == res[0].key
assert ti2.key == res[1].key
session.rollback()
def test_find_executable_task_instances_order_execution_date_and_priority(self, dag_maker):
dag_id_1 = "SchedulerJobTest.test_find_executable_task_instances_order_execution_date_and_priority-a"
dag_id_2 = "SchedulerJobTest.test_find_executable_task_instances_order_execution_date_and_priority-b"
task_id = "task-a"
session = settings.Session()
with dag_maker(dag_id=dag_id_1, max_active_tasks=16, session=session):
EmptyOperator(task_id=task_id, priority_weight=1)
dr1 = dag_maker.create_dagrun()
with dag_maker(dag_id=dag_id_2, max_active_tasks=16, session=session):
EmptyOperator(task_id=task_id, priority_weight=4)
dr2 = dag_maker.create_dagrun(execution_date=DEFAULT_DATE + timedelta(hours=1))
dr1 = session.merge(dr1, load=False)
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull)
tis = dr1.task_instances + dr2.task_instances
for ti in tis:
ti.state = State.SCHEDULED
session.merge(ti)
session.flush()
res = self.job_runner._executable_task_instances_to_queued(max_tis=1, session=session)
session.flush()
assert [ti.key for ti in res] == [tis[1].key]
session.rollback()
def test_find_executable_task_instances_in_default_pool(self, dag_maker):
set_default_pool_slots(1)
dag_id = "SchedulerJobTest.test_find_executable_task_instances_in_default_pool"
with dag_maker(dag_id=dag_id):
op1 = EmptyOperator(task_id="dummy1")
op2 = EmptyOperator(task_id="dummy2")
scheduler_job = Job(executor=MockExecutor())
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull, num_runs=1)
session = settings.Session()
dr1 = dag_maker.create_dagrun(run_type=DagRunType.SCHEDULED)
dr2 = dag_maker.create_dagrun_after(dr1, run_type=DagRunType.SCHEDULED, state=State.RUNNING)
ti1 = dr1.get_task_instance(op1.task_id, session)
ti2 = dr2.get_task_instance(op2.task_id, session)
ti1.state = State.SCHEDULED
ti2.state = State.SCHEDULED
session.flush()
# Two tasks w/o pool up for execution and our default pool size is 1
res = self.job_runner._executable_task_instances_to_queued(max_tis=32, session=session)
assert 1 == len(res)
ti2.state = State.RUNNING
session.flush()
# One task w/o pool up for execution and one task running
res = self.job_runner._executable_task_instances_to_queued(max_tis=32, session=session)
assert 0 == len(res)
session.rollback()
session.close()
def test_queued_task_instances_fails_with_missing_dag(self, dag_maker, session):
"""Check that task instances of missing DAGs are failed"""
dag_id = "SchedulerJobTest.test_find_executable_task_instances_not_in_dagbag"
task_id_1 = "dummy"
task_id_2 = "dummydummy"
with dag_maker(dag_id=dag_id, session=session, default_args={"max_active_tis_per_dag": 1}):
EmptyOperator(task_id=task_id_1)
EmptyOperator(task_id=task_id_2)
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull)
self.job_runner.dagbag = mock.MagicMock()
self.job_runner.dagbag.get_dag.return_value = None
dr = dag_maker.create_dagrun(state=DagRunState.RUNNING)
tis = dr.task_instances
for ti in tis:
ti.state = State.SCHEDULED
session.merge(ti)
session.flush()
res = self.job_runner._executable_task_instances_to_queued(max_tis=32, session=session)
session.flush()
assert 0 == len(res)
tis = dr.get_task_instances(session=session)
assert len(tis) == 2
assert all(ti.state == State.FAILED for ti in tis)
def test_nonexistent_pool(self, dag_maker):
dag_id = "SchedulerJobTest.test_nonexistent_pool"
with dag_maker(dag_id=dag_id, max_active_tasks=16):
EmptyOperator(task_id="dummy_wrong_pool", pool="this_pool_doesnt_exist")
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull)
session = settings.Session()
dr = dag_maker.create_dagrun()
ti = dr.task_instances[0]
ti.state = State.SCHEDULED
session.merge(ti)
session.commit()
res = self.job_runner._executable_task_instances_to_queued(max_tis=32, session=session)
session.flush()
assert 0 == len(res)
session.rollback()
def test_infinite_pool(self, dag_maker):
dag_id = "SchedulerJobTest.test_infinite_pool"
with dag_maker(dag_id=dag_id, max_active_tasks=16):
EmptyOperator(task_id="dummy", pool="infinite_pool")
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull)
session = settings.Session()
dr = dag_maker.create_dagrun()
ti = dr.task_instances[0]
ti.state = State.SCHEDULED
session.merge(ti)
infinite_pool = Pool(
pool="infinite_pool",
slots=-1,
description="infinite pool",
include_deferred=False,
)