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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
import sys
from collections import Counter, deque
from datetime import timedelta
from importlib import reload
from typing import Generator
from unittest import mock
from unittest.mock import MagicMock, PropertyMock, patch
from uuid import uuid4
import pendulum
import psutil
import pytest
import time_machine
from pytest import param
from sqlalchemy import func, select, update
import airflow.example_dags
from airflow import settings
from airflow.assets import Asset
from airflow.assets.manager import AssetManager
from airflow.callbacks.callback_requests import DagCallbackRequest, 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.decorators import task
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.job import Job, run_job
from airflow.jobs.scheduler_job_runner import SchedulerJobRunner
from airflow.models.asset import AssetActive, AssetDagRunQueue, AssetEvent, AssetModel
from airflow.models.backfill import Backfill, _create_backfill
from airflow.models.dag import DAG, DagModel
from airflow.models.dag_version import DagVersion
from airflow.models.dagbag import DagBag
from airflow.models.dagrun import DagRun
from airflow.models.dagwarning import DagWarning
from airflow.models.db_callback_request import DbCallbackRequest
from airflow.models.log import Log
from airflow.models.pool import Pool
from airflow.models.serialized_dag import SerializedDagModel
from airflow.models.taskinstance import SimpleTaskInstance, TaskInstance
from airflow.operators.empty import EmptyOperator
from airflow.providers.standard.operators.bash import BashOperator
from airflow.serialization.serialized_objects import SerializedDAG
from airflow.timetables.base import DataInterval
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.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.utils.test_timezone import UTC
from tests_common.test_utils.asserts import assert_queries_count
from tests_common.test_utils.compat import AIRFLOW_V_3_0_PLUS
from tests_common.test_utils.config import conf_vars, env_vars
from tests_common.test_utils.db import (
clear_db_assets,
clear_db_backfills,
clear_db_dags,
clear_db_import_errors,
clear_db_jobs,
clear_db_pools,
clear_db_runs,
clear_db_serialized_dags,
set_default_pool_slots,
)
from tests_common.test_utils.mock_executor import MockExecutor
from tests_common.test_utils.mock_operators import CustomOperator
if AIRFLOW_V_3_0_PLUS:
from airflow.utils.types import DagRunTriggeredByType
pytestmark = [pytest.mark.db_test, pytest.mark.skip_if_database_isolation_mode]
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
def load_examples():
with conf_vars({("core", "load_examples"): "True"}):
yield
# Patch the MockExecutor into the dict of known executors in the Loader
@contextlib.contextmanager
def _loader_mock(mock_executors):
with mock.patch("airflow.executors.executor_loader.ExecutorLoader.load_executor") as loader_mock:
# The executors are mocked, so cannot be loaded/imported. Mock load_executor and return the
# correct object for the given input executor name.
loader_mock.side_effect = lambda *x: {
("default_exec",): mock_executors[0],
(None,): mock_executors[0],
("secondary_exec",): mock_executors[1],
}[x]
yield
@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_dags()
clear_db_runs()
clear_db_backfills()
clear_db_pools()
clear_db_import_errors()
clear_db_jobs()
clear_db_assets()
@pytest.fixture(autouse=True)
def per_test(self) -> Generator:
self.clean_db()
self.job_runner: SchedulerJobRunner | None = 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) -> Generator:
# Speed up some tests by not running the tasks, just look at what we
# enqueue!
self.null_exec: MockExecutor | None = MockExecutor()
yield
self.null_exec = None
@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:
with mock.patch(
"airflow.executors.executor_loader.ExecutorLoader.load_executor"
) as loader_mock:
# The executors are mocked, so cannot be loaded/imported. Mock load_executor and return
# the correct object for the given input executor name.
loader_mock.side_effect = lambda *x: {
(None,): default_executor,
("default_exec",): default_executor,
("default.exec.module.path",): default_executor,
("secondary_exec",): second_executor,
("secondary.exec.module.path",): second_executor,
}[x]
from airflow.jobs import scheduler_job_runner
# Reload the scheduler job runner so that it gets a fresh mocked copy of the exec loader
reload(scheduler_job_runner)
executor_mock.return_value = default_executor
executors_mock.return_value = [default_executor, second_executor]
yield [default_executor, second_executor]
@pytest.fixture
def mock_executor(self):
default_executor = mock.MagicMock(slots_available=8, slots_occupied=0)
default_executor.name = MagicMock(alias="default_exec", module_path="default.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:
with mock.patch(
"airflow.executors.executor_loader.ExecutorLoader.load_executor"
) as loader_mock:
executor_mock.return_value = default_executor
executors_mock.return_value = [default_executor]
# The executor is mocked, so cannot be loaded/imported. Mock load_executor and return the
# correct object for the given input executor name.
loader_mock.side_effect = lambda *x: {
("default_exec",): default_executor,
("default.exec.module.path",): default_executor,
(None,): default_executor,
}[x]
from airflow.jobs import scheduler_job_runner
# Reload the scheduler job runner so that it gets a fresh mocked copy of the exec loader
reload(scheduler_job_runner)
yield default_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(executor=executor, 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(executor=executor, 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, logical_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(executor=executor, 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(executor=executor, 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)
scheduler_job.executors = [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(executor=executor, 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=f"Executor {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(executor=executor, 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(executor=executor, 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(executor=executor, 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(executor=executor, 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_will_execute(self, dag_maker):
"""
Tests that backfill tasks won't get executed.
"""
dag_id = "SchedulerJobTest.test_execute_task_instances_backfill_tasks_will_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.run_type == DagRunType.BACKFILL_JOB
self.job_runner._critical_section_enqueue_task_instances(session)
session.flush()
ti1.refresh_from_db()
assert ti1.state == TaskInstanceState.QUEUED
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)
@conf_vars({("scheduler", "standalone_dag_processor"): "False"})
def test_setup_callback_sink_not_standalone_dag_processor_multiple_executors(self, mock_executors):
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)
@conf_vars({("scheduler", "standalone_dag_processor"): "True"})
def test_setup_callback_sink_standalone_dag_processor_multiple_executors(self, mock_executors):
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)
@conf_vars({("scheduler", "standalone_dag_processor"): "True"})
def test_executor_start_called(self, mock_executors):
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()
def test_executor_job_id_assigned(self, mock_executors):
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
def test_executor_heartbeat(self, mock_executors):
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:
executor.heartbeat.assert_called_once()
def test_executor_events_processed(self, mock_executors):
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:
executor.get_event_buffer.assert_called_once()
def test_executor_debug_dump(self, mock_executors):
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()
dr_non_backfill = dag_maker.create_dagrun(run_type=DagRunType.SCHEDULED)
dr_backfill = dag_maker.create_dagrun_after(
dr_non_backfill, run_type=DagRunType.BACKFILL_JOB, state=State.RUNNING
)
ti_backfill = dr_backfill.get_task_instance(task1.task_id)
ti_non_backfill = dr_non_backfill.get_task_instance(task1.task_id)
ti_backfill.state = State.SCHEDULED
ti_non_backfill.state = State.SCHEDULED
session.merge(dr_backfill)
session.merge(ti_backfill)
session.merge(ti_non_backfill)
session.flush()
queued_tis = self.job_runner._executable_task_instances_to_queued(max_tis=32, session=session)
assert len(queued_tis) == 2
assert {x.key for x in queued_tis} == {ti_non_backfill.key, ti_backfill.key}
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_logical_date(self, dag_maker):
"""
Test that task instances follow logical_date order priority. If two dagruns with
different logical dates are scheduled, tasks with earliest dagrun logical date will first
be executed
"""
dag_id_1 = "SchedulerJobTest.test_find_executable_task_instances_order_logical_date-a"
dag_id_2 = "SchedulerJobTest.test_find_executable_task_instances_order_logical_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(logical_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_executor(self, dag_maker, mock_executors):
"""
Test that tasks for all executors are set to queued, if space allows it
"""
scheduler_job = Job()
self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull)
session = settings.Session()
dag_id = "SchedulerJobTest.test_find_executable_task_instances_executor"
with dag_maker(dag_id=dag_id):
op1 = EmptyOperator(task_id="dummy1") # No executor specified, runs on default executor
op2 = EmptyOperator(task_id="dummy2", executor="default_exec")
op3 = EmptyOperator(task_id="dummy3", executor="default.exec.module.path")
op4 = EmptyOperator(task_id="dummy4", executor="secondary_exec")
op5 = EmptyOperator(task_id="dummy5", executor="secondary.exec.module.path")
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)
ti4 = dag_run.get_task_instance(op4.task_id, session)
ti5 = dag_run.get_task_instance(op5.task_id, session)
tis_tuple = (ti1, ti2, ti3, ti4, ti5)
for ti in tis_tuple:
ti.state = State.SCHEDULED
session.flush()
res = self.job_runner._executable_task_instances_to_queued(max_tis=32, session=session)
assert 5 == len(res)
res_ti_keys = [res_ti.key for res_ti in res]
for ti in tis_tuple:
assert ti.key in res_ti_keys
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_logical_date_and_priority(self, dag_maker):
dag_id_1 = "SchedulerJobTest.test_find_executable_task_instances_order_logical_date_and_priority-a"
dag_id_2 = "SchedulerJobTest.test_find_executable_task_instances_order_logical_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(logical_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, mock_executor):
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()
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)