airflow processor 源码
airflow processor 代码
文件路径：/airflow/dag_processing/processor.py
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations
import datetime
import logging
import multiprocessing
import os
import signal
import threading
import time
from contextlib import redirect_stderr, redirect_stdout, suppress
from datetime import timedelta
from multiprocessing.connection import Connection as MultiprocessingConnection
from typing import TYPE_CHECKING, Iterator
from setproctitle import setproctitle
from sqlalchemy import exc, func, or_
from sqlalchemy.orm.session import Session
from airflow import settings
from airflow.callbacks.callback_requests import (
CallbackRequest,
DagCallbackRequest,
SlaCallbackRequest,
TaskCallbackRequest,
)
from airflow.configuration import conf
from airflow.exceptions import AirflowException, TaskNotFound
from airflow.models import SlaMiss, errors
from airflow.models.dag import DAG, DagModel
from airflow.models.dagbag import DagBag
from airflow.models.dagrun import DagRun as DR
from airflow.models.dagwarning import DagWarning, DagWarningType
from airflow.models.taskinstance import TaskInstance as TI
from airflow.stats import Stats
from airflow.utils import timezone
from airflow.utils.email import get_email_address_list, send_email
from airflow.utils.log.logging_mixin import LoggingMixin, StreamLogWriter, set_context
from airflow.utils.mixins import MultiprocessingStartMethodMixin
from airflow.utils.session import NEW_SESSION, provide_session
from airflow.utils.state import State
if TYPE_CHECKING:
from airflow.models.operator import Operator
class DagFileProcessorProcess(LoggingMixin, MultiprocessingStartMethodMixin):
"""Runs DAG processing in a separate process using DagFileProcessor
:param file_path: a Python file containing Airflow DAG definitions
:param pickle_dags: whether to serialize the DAG objects to the DB
:param dag_ids: If specified, only look at these DAG ID's
:param callback_requests: failure callback to execute
"""
# Counter that increments every time an instance of this class is created
class_creation_counter = 0
def __init__(
self,
file_path: str,
pickle_dags: bool,
dag_ids: list[str] | None,
dag_directory: str,
callback_requests: list[CallbackRequest],
):
super().__init__()
self._file_path = file_path
self._pickle_dags = pickle_dags
self._dag_ids = dag_ids
self._dag_directory = dag_directory
self._callback_requests = callback_requests
# The process that was launched to process the given .
self._process: multiprocessing.process.BaseProcess | None = None
# The result of DagFileProcessor.process_file(file_path).
self._result: tuple[int, int] | None = None
# Whether the process is done running.
self._done = False
# When the process started.
self._start_time: datetime.datetime | None = None
# This ID is use to uniquely name the process / thread that's launched
# by this processor instance
self._instance_id = DagFileProcessorProcess.class_creation_counter
self._parent_channel: MultiprocessingConnection | None = None
DagFileProcessorProcess.class_creation_counter += 1
@property
def file_path(self) -> str:
return self._file_path
@staticmethod
def _run_file_processor(
result_channel: MultiprocessingConnection,
parent_channel: MultiprocessingConnection,
file_path: str,
pickle_dags: bool,
dag_ids: list[str] | None,
thread_name: str,
dag_directory: str,
callback_requests: list[CallbackRequest],
) -> None:
"""
Process the given file.
:param result_channel: the connection to use for passing back the result
:param parent_channel: the parent end of the channel to close in the child
:param file_path: the file to process
:param pickle_dags: whether to pickle the DAGs found in the file and
save them to the DB
:param dag_ids: if specified, only examine DAG ID's that are
in this list
:param thread_name: the name to use for the process that is launched
:param callback_requests: failure callback to execute
:return: the process that was launched
:rtype: multiprocessing.Process
"""
# This helper runs in the newly created process
log: logging.Logger = logging.getLogger("airflow.processor")
# Since we share all open FDs from the parent, we need to close the parent side of the pipe here in
# the child, else it won't get closed properly until we exit.
parent_channel.close()
del parent_channel
set_context(log, file_path)
setproctitle(f"airflow scheduler - DagFileProcessor {file_path}")
def _handle_dag_file_processing():
# Re-configure the ORM engine as there are issues with multiple processes
settings.configure_orm()
# Change the thread name to differentiate log lines. This is
# really a separate process, but changing the name of the
# process doesn't work, so changing the thread name instead.
threading.current_thread().name = thread_name
log.info("Started process (PID=%s) to work on %s", os.getpid(), file_path)
dag_file_processor = DagFileProcessor(dag_ids=dag_ids, dag_directory=dag_directory, log=log)
result: tuple[int, int] = dag_file_processor.process_file(
file_path=file_path,
pickle_dags=pickle_dags,
callback_requests=callback_requests,
)
result_channel.send(result)
try:
DAG_PROCESSOR_LOG_TARGET = conf.get_mandatory_value('logging', 'DAG_PROCESSOR_LOG_TARGET')
if DAG_PROCESSOR_LOG_TARGET == "stdout":
with Stats.timer() as timer:
_handle_dag_file_processing()
else:
# The following line ensures that stdout goes to the same destination as the logs. If stdout
# gets sent to logs and logs are sent to stdout, this leads to an infinite loop. This
# necessitates this conditional based on the value of DAG_PROCESSOR_LOG_TARGET.
with redirect_stdout(StreamLogWriter(log, logging.INFO)), redirect_stderr(
StreamLogWriter(log, logging.WARN)
), Stats.timer() as timer:
_handle_dag_file_processing()
log.info("Processing %s took %.3f seconds", file_path, timer.duration)
except Exception:
# Log exceptions through the logging framework.
log.exception("Got an exception! Propagating...")
raise
finally:
# We re-initialized the ORM within this Process above so we need to
# tear it down manually here
settings.dispose_orm()
result_channel.close()
def start(self) -> None:
"""Launch the process and start processing the DAG."""
start_method = self._get_multiprocessing_start_method()
context = multiprocessing.get_context(start_method)
_parent_channel, _child_channel = context.Pipe(duplex=False)
process = context.Process(
target=type(self)._run_file_processor,
args=(
_child_channel,
_parent_channel,
self.file_path,
self._pickle_dags,
self._dag_ids,
f"DagFileProcessor{self._instance_id}",
self._dag_directory,
self._callback_requests,
),
name=f"DagFileProcessor{self._instance_id}-Process",
)
self._process = process
self._start_time = timezone.utcnow()
process.start()
# Close the child side of the pipe now the subprocess has started -- otherwise this would prevent it
# from closing in some cases
_child_channel.close()
del _child_channel
# Don't store it on self until after we've started the child process - we don't want to keep it from
# getting GCd/closed
self._parent_channel = _parent_channel
def kill(self) -> None:
"""Kill the process launched to process the file, and ensure consistent state."""
if self._process is None:
raise AirflowException("Tried to kill before starting!")
self._kill_process()
def terminate(self, sigkill: bool = False) -> None:
"""
Terminate (and then kill) the process launched to process the file.
:param sigkill: whether to issue a SIGKILL if SIGTERM doesn't work.
"""
if self._process is None or self._parent_channel is None:
raise AirflowException("Tried to call terminate before starting!")
self._process.terminate()
# Arbitrarily wait 5s for the process to die
with suppress(TimeoutError):
self._process._popen.wait(5) # type: ignore
if sigkill:
self._kill_process()
self._parent_channel.close()
def _kill_process(self) -> None:
if self._process is None:
raise AirflowException("Tried to kill process before starting!")
if self._process.is_alive() and self._process.pid:
self.log.warning("Killing DAGFileProcessorProcess (PID=%d)", self._process.pid)
os.kill(self._process.pid, signal.SIGKILL)
# Reap the spawned zombie. We active wait, because in Python 3.9 `waitpid` might lead to an
# exception, due to change in Python standard library and possibility of race condition
# see https://bugs.python.org/issue42558
while self._process._popen.poll() is None: # type: ignore
time.sleep(0.001)
if self._parent_channel:
self._parent_channel.close()
@property
def pid(self) -> int:
"""
:return: the PID of the process launched to process the given file
:rtype: int
"""
if self._process is None or self._process.pid is None:
raise AirflowException("Tried to get PID before starting!")
return self._process.pid
@property
def exit_code(self) -> int | None:
"""
After the process is finished, this can be called to get the return code
:return: the exit code of the process
:rtype: int
"""
if self._process is None:
raise AirflowException("Tried to get exit code before starting!")
if not self._done:
raise AirflowException("Tried to call retcode before process was finished!")
return self._process.exitcode
@property
def done(self) -> bool:
"""
Check if the process launched to process this file is done.
:return: whether the process is finished running
:rtype: bool
"""
if self._process is None or self._parent_channel is None:
raise AirflowException("Tried to see if it's done before starting!")
if self._done:
return True
if self._parent_channel.poll():
try:
self._result = self._parent_channel.recv()
self._done = True
self.log.debug("Waiting for %s", self._process)
self._process.join()
self._parent_channel.close()
return True
except EOFError:
# If we get an EOFError, it means the child end of the pipe has been closed. This only happens
# in the finally block. But due to a possible race condition, the process may have not yet
# terminated (it could be doing cleanup/python shutdown still). So we kill it here after a
# "suitable" timeout.
self._done = True
# Arbitrary timeout -- error/race condition only, so this doesn't need to be tunable.
self._process.join(timeout=5)
if self._process.is_alive():
# Didn't shut down cleanly - kill it
self._kill_process()
if not self._process.is_alive():
self._done = True
self.log.debug("Waiting for %s", self._process)
self._process.join()
self._parent_channel.close()
return True
return False
@property
def result(self) -> tuple[int, int] | None:
"""
:return: result of running DagFileProcessor.process_file()
:rtype: tuple[int, int] or None
"""
if not self.done:
raise AirflowException("Tried to get the result before it's done!")
return self._result
@property
def start_time(self) -> datetime.datetime:
"""
:return: when this started to process the file
:rtype: datetime
"""
if self._start_time is None:
raise AirflowException("Tried to get start time before it started!")
return self._start_time
@property
def waitable_handle(self):
return self._process.sentinel
class DagFileProcessor(LoggingMixin):
"""
Process a Python file containing Airflow DAGs.
This includes:
1. Execute the file and look for DAG objects in the namespace.
2. Execute any Callbacks if passed to DagFileProcessor.process_file
3. Serialize the DAGs and save it to DB (or update existing record in the DB).
4. Pickle the DAG and save it to the DB (if necessary).
5. Record any errors importing the file into ORM
Returns a tuple of 'number of dags found' and 'the count of import errors'
:param dag_ids: If specified, only look at these DAG ID's
:param log: Logger to save the processing process
"""
UNIT_TEST_MODE: bool = conf.getboolean('core', 'UNIT_TEST_MODE')
def __init__(self, dag_ids: list[str] | None, dag_directory: str, log: logging.Logger):
super().__init__()
self.dag_ids = dag_ids
self._log = log
self._dag_directory = dag_directory
self.dag_warnings: set[tuple[str, str]] = set()
@provide_session
def manage_slas(self, dag: DAG, session: Session = None) -> None:
"""
Finding all tasks that have SLAs defined, and sending alert emails
where needed. New SLA misses are also recorded in the database.
We are assuming that the scheduler runs often, so we only check for
tasks that should have succeeded in the past hour.
"""
self.log.info("Running SLA Checks for %s", dag.dag_id)
if not any(isinstance(ti.sla, timedelta) for ti in dag.tasks):
self.log.info("Skipping SLA check for %s because no tasks in DAG have SLAs", dag)
return
qry = (
session.query(TI.task_id, func.max(DR.execution_date).label('max_ti'))
.join(TI.dag_run)
.filter(TI.dag_id == dag.dag_id)
.filter(or_(TI.state == State.SUCCESS, TI.state == State.SKIPPED))
.filter(TI.task_id.in_(dag.task_ids))
.group_by(TI.task_id)
.subquery('sq')
)
# get recorded SlaMiss
recorded_slas_query = set(
session.query(SlaMiss.dag_id, SlaMiss.task_id, SlaMiss.execution_date).filter(
SlaMiss.dag_id == dag.dag_id, SlaMiss.task_id.in_(dag.task_ids)
)
)
max_tis: Iterator[TI] = (
session.query(TI)
.join(TI.dag_run)
.filter(
TI.dag_id == dag.dag_id,
TI.task_id == qry.c.task_id,
DR.execution_date == qry.c.max_ti,
)
)
ts = timezone.utcnow()
for ti in max_tis:
task = dag.get_task(ti.task_id)
if not task.sla:
continue
if not isinstance(task.sla, timedelta):
raise TypeError(
f"SLA is expected to be timedelta object, got "
f"{type(task.sla)} in {task.dag_id}:{task.task_id}"
)
sla_misses = []
next_info = dag.next_dagrun_info(dag.get_run_data_interval(ti.dag_run), restricted=False)
while next_info and next_info.logical_date < ts:
next_info = dag.next_dagrun_info(next_info.data_interval, restricted=False)
if next_info is None:
break
if (ti.dag_id, ti.task_id, next_info.logical_date) in recorded_slas_query:
break
if next_info.logical_date + task.sla < ts:
sla_miss = SlaMiss(
task_id=ti.task_id,
dag_id=ti.dag_id,
execution_date=next_info.logical_date,
timestamp=ts,
)
sla_misses.append(sla_miss)
Stats.incr('sla_missed')
if sla_misses:
session.add_all(sla_misses)
session.commit()
slas: list[SlaMiss] = (
session.query(SlaMiss)
.filter(SlaMiss.notification_sent == False, SlaMiss.dag_id == dag.dag_id) # noqa
.all()
)
if slas:
sla_dates: list[datetime.datetime] = [sla.execution_date for sla in slas]
fetched_tis: list[TI] = (
session.query(TI)
.filter(TI.state != State.SUCCESS, TI.execution_date.in_(sla_dates), TI.dag_id == dag.dag_id)
.all()
)
blocking_tis: list[TI] = []
for ti in fetched_tis:
if ti.task_id in dag.task_ids:
ti.task = dag.get_task(ti.task_id)
blocking_tis.append(ti)
else:
session.delete(ti)
session.commit()
task_list = "\n".join(sla.task_id + ' on ' + sla.execution_date.isoformat() for sla in slas)
blocking_task_list = "\n".join(
ti.task_id + ' on ' + ti.execution_date.isoformat() for ti in blocking_tis
)
# Track whether email or any alert notification sent
# We consider email or the alert callback as notifications
email_sent = False
notification_sent = False
if dag.sla_miss_callback:
# Execute the alert callback
self.log.info('Calling SLA miss callback')
try:
dag.sla_miss_callback(dag, task_list, blocking_task_list, slas, blocking_tis)
notification_sent = True
except Exception:
Stats.incr('sla_callback_notification_failure')
self.log.exception("Could not call sla_miss_callback for DAG %s", dag.dag_id)
email_content = f"""\
Here's a list of tasks that missed their SLAs:
<pre><code>{task_list}\n<code></pre>
Blocking tasks:
<pre><code>{blocking_task_list}<code></pre>
Airflow Webserver URL: {conf.get(section='webserver', key='base_url')}
"""
tasks_missed_sla = []
for sla in slas:
try:
task = dag.get_task(sla.task_id)
except TaskNotFound:
# task already deleted from DAG, skip it
self.log.warning(
"Task %s doesn't exist in DAG anymore, skipping SLA miss notification.", sla.task_id
)
continue
tasks_missed_sla.append(task)
emails: set[str] = set()
for task in tasks_missed_sla:
if task.email:
if isinstance(task.email, str):
emails |= set(get_email_address_list(task.email))
elif isinstance(task.email, (list, tuple)):
emails |= set(task.email)
if emails:
try:
send_email(emails, f"[airflow] SLA miss on DAG={dag.dag_id}", email_content)
email_sent = True
notification_sent = True
except Exception:
Stats.incr('sla_email_notification_failure')
self.log.exception("Could not send SLA Miss email notification for DAG %s", dag.dag_id)
# If we sent any notification, update the sla_miss table
if notification_sent:
for sla in slas:
sla.email_sent = email_sent
sla.notification_sent = True
session.merge(sla)
session.commit()
@staticmethod
def update_import_errors(session: Session, dagbag: DagBag) -> None:
"""
For the DAGs in the given DagBag, record any associated import errors and clears
errors for files that no longer have them. These are usually displayed through the
Airflow UI so that users know that there are issues parsing DAGs.
:param session: session for ORM operations
:param dagbag: DagBag containing DAGs with import errors
"""
files_without_error = dagbag.file_last_changed - dagbag.import_errors.keys()
# Clear the errors of the processed files
# that no longer have errors
for dagbag_file in files_without_error:
session.query(errors.ImportError).filter(
errors.ImportError.filename.startswith(dagbag_file)
).delete(synchronize_session="fetch")
# files that still have errors
existing_import_error_files = [x.filename for x in session.query(errors.ImportError.filename).all()]
# Add the errors of the processed files
for filename, stacktrace in dagbag.import_errors.items():
if filename in existing_import_error_files:
session.query(errors.ImportError).filter(errors.ImportError.filename == filename).update(
dict(filename=filename, timestamp=timezone.utcnow(), stacktrace=stacktrace),
synchronize_session='fetch',
)
else:
session.add(
errors.ImportError(filename=filename, timestamp=timezone.utcnow(), stacktrace=stacktrace)
)
(
session.query(DagModel)
.filter(DagModel.fileloc == filename)
.update({'has_import_errors': True}, synchronize_session='fetch')
)
session.commit()
@provide_session
def _validate_task_pools(self, *, dagbag: DagBag, session: Session = NEW_SESSION):
"""
Validates and raise exception if any task in a dag is using a non-existent pool
:meta private:
"""
from airflow.models.pool import Pool
def check_pools(dag):
task_pools = {task.pool for task in dag.tasks}
nonexistent_pools = task_pools - pools
if nonexistent_pools:
return (
f"Dag '{dag.dag_id}' references non-existent pools: {list(sorted(nonexistent_pools))!r}"
)
pools = {p.pool for p in Pool.get_pools(session)}
for dag in dagbag.dags.values():
message = check_pools(dag)
if message:
self.dag_warnings.add(DagWarning(dag.dag_id, DagWarningType.NONEXISTENT_POOL, message))
for subdag in dag.subdags:
message = check_pools(subdag)
if message:
self.dag_warnings.add(DagWarning(subdag.dag_id, DagWarningType.NONEXISTENT_POOL, message))
def update_dag_warnings(self, *, session: Session, dagbag: DagBag) -> None:
"""
For the DAGs in the given DagBag, record any associated configuration warnings and clear
warnings for files that no longer have them. These are usually displayed through the
Airflow UI so that users know that there are issues parsing DAGs.
:param session: session for ORM operations
:param dagbag: DagBag containing DAGs with configuration warnings
"""
self._validate_task_pools(dagbag=dagbag)
stored_warnings = set(
session.query(DagWarning).filter(DagWarning.dag_id.in_(dagbag.dags.keys())).all()
)
for warning_to_delete in stored_warnings - self.dag_warnings:
session.delete(warning_to_delete)
for warning_to_add in self.dag_warnings:
session.merge(warning_to_add)
session.commit()
@provide_session
def execute_callbacks(
self, dagbag: DagBag, callback_requests: list[CallbackRequest], session: Session = NEW_SESSION
) -> None:
"""
Execute on failure callbacks. These objects can come from SchedulerJob or from
DagFileProcessorManager.
:param dagbag: Dag Bag of dags
:param callback_requests: failure callbacks to execute
:param session: DB session.
"""
for request in callback_requests:
self.log.debug("Processing Callback Request: %s", request)
try:
if isinstance(request, TaskCallbackRequest):
self._execute_task_callbacks(dagbag, request, session=session)
elif isinstance(request, SlaCallbackRequest):
self.manage_slas(dagbag.get_dag(request.dag_id), session=session)
elif isinstance(request, DagCallbackRequest):
self._execute_dag_callbacks(dagbag, request, session)
except Exception:
self.log.exception(
"Error executing %s callback for file: %s",
request.__class__.__name__,
request.full_filepath,
)
session.flush()
def execute_callbacks_without_dag(
self, callback_requests: list[CallbackRequest], session: Session
) -> None:
"""
Execute what callbacks we can as "best effort" when the dag cannot be found/had parse errors.
This is so important so that tasks that failed when there is a parse
error don't get stuck in queued state.
"""
for request in callback_requests:
self.log.debug("Processing Callback Request: %s", request)
if isinstance(request, TaskCallbackRequest):
self._execute_task_callbacks(None, request, session)
else:
self.log.info(
"Not executing %s callback for file %s as there was a dag parse error",
request.__class__.__name__,
request.full_filepath,
)
@provide_session
def _execute_dag_callbacks(self, dagbag: DagBag, request: DagCallbackRequest, session: Session):
dag = dagbag.dags[request.dag_id]
dag_run = dag.get_dagrun(run_id=request.run_id, session=session)
dag.handle_callback(
dagrun=dag_run, success=not request.is_failure_callback, reason=request.msg, session=session
)
def _execute_task_callbacks(self, dagbag: DagBag | None, request: TaskCallbackRequest, session: Session):
if not request.is_failure_callback:
return
simple_ti = request.simple_task_instance
ti: TI | None = (
session.query(TI)
.filter_by(
dag_id=simple_ti.dag_id,
run_id=simple_ti.run_id,
task_id=simple_ti.task_id,
map_index=simple_ti.map_index,
)
.one_or_none()
)
if not ti:
return
task: Operator | None = None
if dagbag and simple_ti.dag_id in dagbag.dags:
dag = dagbag.dags[simple_ti.dag_id]
if simple_ti.task_id in dag.task_ids:
task = dag.get_task(simple_ti.task_id)
else:
# We don't have the _real_ dag here (perhaps it had a parse error?) but we still want to run
# `handle_failure` so that the state of the TI gets progressed.
#
# Since handle_failure _really_ wants a task, we do our best effort to give it one
from airflow.models.serialized_dag import SerializedDagModel
try:
model = session.query(SerializedDagModel).get(simple_ti.dag_id)
if model:
task = model.dag.get_task(simple_ti.task_id)
except (exc.NoResultFound, TaskNotFound):
pass
if task:
ti.refresh_from_task(task)
ti.handle_failure(error=request.msg, test_mode=self.UNIT_TEST_MODE, session=session)
self.log.info('Executed failure callback for %s in state %s', ti, ti.state)
session.flush()
@provide_session
def process_file(
self,
file_path: str,
callback_requests: list[CallbackRequest],
pickle_dags: bool = False,
session: Session = NEW_SESSION,
) -> tuple[int, int]:
"""
Process a Python file containing Airflow DAGs.
This includes:
1. Execute the file and look for DAG objects in the namespace.
2. Execute any Callbacks if passed to this method.
3. Serialize the DAGs and save it to DB (or update existing record in the DB).
4. Pickle the DAG and save it to the DB (if necessary).
5. Mark any DAGs which are no longer present as inactive
6. Record any errors importing the file into ORM
:param file_path: the path to the Python file that should be executed
:param callback_requests: failure callback to execute
:param pickle_dags: whether serialize the DAGs found in the file and
save them to the db
:param session: Sqlalchemy ORM Session
:return: number of dags found, count of import errors
:rtype: Tuple[int, int]
"""
self.log.info("Processing file %s for tasks to queue", file_path)
try:
dagbag = DagBag(file_path, include_examples=False)
except Exception:
self.log.exception("Failed at reloading the DAG file %s", file_path)
Stats.incr('dag_file_refresh_error', 1, 1)
return 0, 0
if len(dagbag.dags) > 0:
self.log.info("DAG(s) %s retrieved from %s", dagbag.dags.keys(), file_path)
else:
self.log.warning("No viable dags retrieved from %s", file_path)
self.update_import_errors(session, dagbag)
if callback_requests:
# If there were callback requests for this file but there was a
# parse error we still need to progress the state of TIs,
# otherwise they might be stuck in queued/running for ever!
self.execute_callbacks_without_dag(callback_requests, session)
return 0, len(dagbag.import_errors)
self.execute_callbacks(dagbag, callback_requests, session)
session.commit()
# Save individual DAGs in the ORM
dagbag.sync_to_db(processor_subdir=self._dag_directory, session=session)
session.commit()
if pickle_dags:
paused_dag_ids = DagModel.get_paused_dag_ids(dag_ids=dagbag.dag_ids)
unpaused_dags: list[DAG] = [
dag for dag_id, dag in dagbag.dags.items() if dag_id not in paused_dag_ids
]
for dag in unpaused_dags:
dag.pickle(session)
# Record import errors into the ORM
try:
self.update_import_errors(session, dagbag)
except Exception:
self.log.exception("Error logging import errors!")
# Record DAG warnings in the metadatabase.
try:
self.update_dag_warnings(session=session, dagbag=dagbag)
except Exception:
self.log.exception("Error logging DAG warnings.")
return len(dagbag.dags), len(dagbag.import_errors)
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