# 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 importlib
import logging
import os
import signal
import threading
import time
import zipfile
from contextlib import contextmanager, redirect_stderr, redirect_stdout, suppress
from dataclasses import dataclass
from datetime import timedelta
from typing import TYPE_CHECKING, Generator, Iterable, Iterator
from setproctitle import setproctitle
from sqlalchemy import delete, event, func, or_, select
from airflow import settings
from airflow.api_internal.internal_api_call import InternalApiConfig, internal_api_call
from airflow.callbacks.callback_requests import (
DagCallbackRequest,
SlaCallbackRequest,
TaskCallbackRequest,
)
from airflow.configuration import conf
from airflow.exceptions import AirflowException, TaskNotFound
from airflow.listeners.listener import get_listener_manager
from airflow.models import SlaMiss
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.errors import ParseImportError
from airflow.models.serialized_dag import SerializedDagModel
from airflow.models.taskinstance import TaskInstance, TaskInstance as TI, _run_finished_callback
from airflow.stats import Stats
from airflow.utils import timezone
from airflow.utils.email import get_email_address_list, send_email
from airflow.utils.file import iter_airflow_imports, might_contain_dag
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 TaskInstanceState
if TYPE_CHECKING:
import multiprocessing
from datetime import datetime
from multiprocessing.connection import Connection as MultiprocessingConnection
from sqlalchemy.orm.session import Session
from airflow.callbacks.callback_requests import CallbackRequest
from airflow.models.operator import Operator
@dataclass
class _QueryCounter:
queries_number: int = 0
def inc(self):
self.queries_number += 1
@contextmanager
def count_queries(session: Session) -> Generator[_QueryCounter, None, None]:
# using list allows to read the updated counter from what context manager returns
counter: _QueryCounter = _QueryCounter()
@event.listens_for(session, "do_orm_execute")
def _count_db_queries(orm_execute_state):
nonlocal counter
counter.inc()
yield counter
event.remove(session, "do_orm_execute", _count_db_queries)
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, int] | None = None
# Whether the process is done running.
self._done = False
# When the process started.
self._start_time: 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
"""
# 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, 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.WARNING)
), 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."""
if conf.getboolean("scheduler", "parsing_pre_import_modules", fallback=True):
# Read the file to pre-import airflow modules used.
# This prevents them from being re-imported from zero in each "processing" process
# and saves CPU time and memory.
zip_file_paths = []
if zipfile.is_zipfile(self.file_path):
try:
with zipfile.ZipFile(self.file_path) as z:
zip_file_paths.extend(
[
os.path.join(self.file_path, info.filename)
for info in z.infolist()
if might_contain_dag(info.filename, True, z)
]
)
except zipfile.BadZipFile as err:
self.log.error("There was an err accessing %s, %s", self.file_path, err)
if zip_file_paths:
self.import_modules(zip_file_paths)
else:
self.import_modules(self.file_path)
context = self._get_multiprocessing_context()
_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:
"""PID of the process launched to process the given file."""
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
"""
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
"""
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, int] | None:
"""Result of running ``DagFileProcessor.process_file()``."""
if not self.done:
raise AirflowException("Tried to get the result before it's done!")
return self._result
@property
def start_time(self) -> datetime:
"""Time when this started to process the file."""
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
def import_modules(self, file_path: str | Iterable[str]):
def _import_modules(filepath):
for module in iter_airflow_imports(filepath):
try:
importlib.import_module(module)
except Exception as e:
# only log as warning because an error here is not preventing anything from working, and
# if it's serious, it's going to be surfaced to the user when the dag is actually parsed.
self.log.warning(
"Error when trying to pre-import module '%s' found in %s: %s",
module,
file_path,
e,
)
if isinstance(file_path, str):
_import_modules(file_path)
elif isinstance(file_path, Iterable):
for path in file_path:
_import_modules(path)
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()
self._last_num_of_db_queries = 0
@classmethod
@internal_api_call
@provide_session
def manage_slas(cls, dag_folder, dag_id: str, session: Session = NEW_SESSION) -> None:
"""
Find all tasks that have SLAs defined, and send alert emails when 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.
"""
dagbag = DagFileProcessor._get_dagbag(dag_folder)
dag = dagbag.get_dag(dag_id)
cls.logger().info("Running SLA Checks for %s", dag.dag_id)
if not any(isinstance(ti.sla, timedelta) for ti in dag.tasks):
cls.logger().info("Skipping SLA check for %s because no tasks in DAG have SLAs", dag)
return
qry = (
select(TI.task_id, func.max(DR.execution_date).label("max_ti"))
.join(TI.dag_run)
.where(TI.dag_id == dag.dag_id)
.where(or_(TI.state == TaskInstanceState.SUCCESS, TI.state == TaskInstanceState.SKIPPED))
.where(TI.task_id.in_(dag.task_ids))
.group_by(TI.task_id)
.subquery("sq")
)
# get recorded SlaMiss
recorded_slas_query = set(
session.execute(
select(SlaMiss.dag_id, SlaMiss.task_id, SlaMiss.execution_date).where(
SlaMiss.dag_id == dag.dag_id, SlaMiss.task_id.in_(dag.task_ids)
)
)
)
max_tis: Iterator[TI] = session.scalars(
select(TI)
.join(TI.dag_run)
.where(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:
continue
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", tags={"dag_id": ti.dag_id, "task_id": ti.task_id})
if sla_misses:
session.add_all(sla_misses)
session.commit()
slas: list[SlaMiss] = session.scalars(
select(SlaMiss).where(~SlaMiss.notification_sent, SlaMiss.dag_id == dag.dag_id)
).all()
if slas:
sla_dates: list[datetime] = [sla.execution_date for sla in slas]
fetched_tis: list[TI] = session.scalars(
select(TI).where(
TI.dag_id == dag.dag_id,
TI.execution_date.in_(sla_dates),
TI.state != TaskInstanceState.SUCCESS,
)
).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
callbacks = (
dag.sla_miss_callback
if isinstance(dag.sla_miss_callback, list)
else [dag.sla_miss_callback]
)
for callback in callbacks:
cls.logger().info("Calling SLA miss callback %s", callback)
try:
callback(dag, task_list, blocking_task_list, slas, blocking_tis)
notification_sent = True
except Exception:
Stats.incr(
"sla_callback_notification_failure",
tags={
"dag_id": dag.dag_id,
"func_name": callback.__name__,
},
)
cls.logger().exception(
"Could not call sla_miss_callback(%s) for DAG %s",
callback.__name__,
dag.dag_id,
)
email_content = f"""\
Here's a list of tasks that missed their SLAs:
{task_list}\n
Blocking tasks:
{blocking_task_list}
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
cls.logger().warning(
"Task %s doesn't exist in DAG anymore, skipping SLA miss notification.", sla.task_id
)
else:
tasks_missed_sla.append(task)
emails: set[str] = set()
for task in tasks_missed_sla:
if task.email:
if isinstance(task.email, str):
emails.update(get_email_address_list(task.email))
elif isinstance(task.email, (list, tuple)):
emails.update(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", tags={"dag_id": dag.dag_id})
cls.logger().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
@internal_api_call
@provide_session
def update_import_errors(
file_last_changed: dict[str, datetime],
import_errors: dict[str, str],
processor_subdir: str | None,
session: Session = NEW_SESSION,
) -> None:
"""
Update any import errors to be displayed in the UI.
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 file_last_changed: Dictionary containing the last changed time of the files
:param import_errors: Dictionary containing the import errors
:param session: session for ORM operations
"""
files_without_error = file_last_changed - import_errors.keys()
# Clear the errors of the processed files
# that no longer have errors
for dagbag_file in files_without_error:
session.execute(
delete(ParseImportError)
.where(ParseImportError.filename.startswith(dagbag_file))
.execution_options(synchronize_session="fetch")
)
# files that still have errors
existing_import_error_files = [x.filename for x in session.query(ParseImportError.filename).all()]
# Add the errors of the processed files
for filename, stacktrace in import_errors.items():
if filename in existing_import_error_files:
session.query(ParseImportError).filter(ParseImportError.filename == filename).update(
{"filename": filename, "timestamp": timezone.utcnow(), "stacktrace": stacktrace},
synchronize_session="fetch",
)
# sending notification when an existing dag import error occurs
get_listener_manager().hook.on_existing_dag_import_error(
filename=filename, stacktrace=stacktrace
)
else:
session.add(
ParseImportError(
filename=filename,
timestamp=timezone.utcnow(),
stacktrace=stacktrace,
processor_subdir=processor_subdir,
)
)
# sending notification when a new dag import error occurs
get_listener_manager().hook.on_new_dag_import_error(filename=filename, stacktrace=stacktrace)
(
session.query(DagModel)
.filter(DagModel.fileloc == filename)
.update({"has_import_errors": True}, synchronize_session="fetch")
)
session.commit()
session.flush()
@classmethod
def update_dag_warnings(cla, *, dagbag: DagBag) -> None:
"""Validate and raise exception if any task in a dag is using a non-existent pool."""
def get_pools(dag) -> dict[str, set[str]]:
return {dag.dag_id: {task.pool for task in dag.tasks}}
pool_dict: dict[str, set[str]] = {}
for dag in dagbag.dags.values():
pool_dict.update(get_pools(dag))
for subdag in dag.subdags:
pool_dict.update(get_pools(subdag))
dag_ids = {dag.dag_id for dag in dagbag.dags.values()}
return DagFileProcessor._validate_task_pools_and_update_dag_warnings(pool_dict, dag_ids)
@classmethod
@internal_api_call
@provide_session
def _validate_task_pools_and_update_dag_warnings(
cls, pool_dict: dict[str, set[str]], dag_ids: set[str], session: Session = NEW_SESSION
) -> None:
from airflow.models.pool import Pool
all_pools = {p.pool for p in Pool.get_pools(session)}
warnings: set[DagWarning] = set()
for dag_id, dag_pools in pool_dict.items():
nonexistent_pools = dag_pools - all_pools
if nonexistent_pools:
warnings.add(
DagWarning(
dag_id,
DagWarningType.NONEXISTENT_POOL,
f"Dag '{dag_id}' references non-existent pools: {sorted(nonexistent_pools)!r}",
)
)
stored_warnings = set(session.query(DagWarning).filter(DagWarning.dag_id.in_(dag_ids)).all())
for warning_to_delete in stored_warnings - warnings:
session.delete(warning_to_delete)
for warning_to_add in warnings:
session.merge(warning_to_add)
session.flush()
session.commit()
@classmethod
@internal_api_call
@provide_session
def execute_callbacks(
cls,
dagbag: DagBag,
callback_requests: list[CallbackRequest],
unit_test_mode: bool,
session: Session = NEW_SESSION,
) -> None:
"""
Execute on failure callbacks.
These objects can come from SchedulerJobRunner or from DagProcessorJobRunner.
:param dagbag: Dag Bag of dags
:param callback_requests: failure callbacks to execute
:param session: DB session.
:return: number of queries executed
"""
for request in callback_requests:
cls.logger().debug("Processing Callback Request: %s", request)
try:
if isinstance(request, TaskCallbackRequest):
cls._execute_task_callbacks(dagbag, request, unit_test_mode, session=session)
elif isinstance(request, SlaCallbackRequest):
if InternalApiConfig.get_use_internal_api():
cls.logger().warning(
"SlaCallbacks are not supported when the Internal API is enabled"
)
else:
DagFileProcessor.manage_slas(dagbag.dag_folder, request.dag_id, session=session)
elif isinstance(request, DagCallbackRequest):
cls._execute_dag_callbacks(dagbag, request, session=session)
except Exception:
cls.logger().exception(
"Error executing %s callback for file: %s",
request.__class__.__name__,
request.full_filepath,
)
session.flush()
session.commit()
@classmethod
@internal_api_call
@provide_session
def execute_callbacks_without_dag(
cls, callback_requests: list[CallbackRequest], unit_test_mode: bool, session: Session = NEW_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:
cls.logger().debug("Processing Callback Request: %s", request)
if isinstance(request, TaskCallbackRequest):
cls._execute_task_callbacks(None, request, unit_test_mode, session)
else:
cls.logger().info(
"Not executing %s callback for file %s as there was a dag parse error",
request.__class__.__name__,
request.full_filepath,
)
session.flush()
session.commit()
@classmethod
def _execute_dag_callbacks(cls, dagbag: DagBag, request: DagCallbackRequest, session: Session):
dag = dagbag.dags[request.dag_id]
callbacks, context = DAG.fetch_callback(
dag=dag,
dag_run_id=request.run_id,
success=not request.is_failure_callback,
reason=request.msg,
session=session,
) or (None, None)
if callbacks and context:
DAG.execute_callback(callbacks, context, dag.dag_id)
@classmethod
@internal_api_call
@provide_session
def _execute_task_callbacks(
cls, dagbag: DagBag | None, request: TaskCallbackRequest, unit_test_mode: bool, session: Session
) -> None:
"""
Execute the task callbacks.
:param dagbag: the DagBag to use to get the task instance
:param request: the task callback request
:param session: the session to use
"""
try:
callback_type = TaskInstanceState(request.task_callback_type)
except ValueError:
callback_type = None
is_remote = callback_type in (TaskInstanceState.SUCCESS, TaskInstanceState.FAILED)
# previously we ignored any request besides failures. now if given callback type directly,
# then we respect it and execute it. additionally because in this scenario the callback
# is submitted remotely, we assume there is no need to mess with state; we simply run
# the callback
if not is_remote and not request.is_failure_callback:
return
simple_ti = request.simple_task_instance
ti = TaskInstance.get_task_instance(
dag_id=simple_ti.dag_id,
run_id=simple_ti.run_id,
task_id=simple_ti.task_id,
map_index=simple_ti.map_index,
session=session,
)
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
task = SerializedDagModel.get_serialized_dag(
dag_id=simple_ti.dag_id, task_id=simple_ti.task_id, session=session
)
if task:
ti.refresh_from_task(task)
if callback_type is TaskInstanceState.SUCCESS:
context = ti.get_template_context(session=session)
if TYPE_CHECKING:
assert ti.task
callbacks = ti.task.on_success_callback
_run_finished_callback(callbacks=callbacks, context=context)
cls.logger().info("Executed callback for %s in state %s", ti, ti.state)
elif not is_remote or callback_type is TaskInstanceState.FAILED:
ti.handle_failure(error=request.msg, test_mode=unit_test_mode, session=session)
cls.logger().info("Executed callback for %s in state %s", ti, ti.state)
session.flush()
@classmethod
def _get_dagbag(cls, file_path: str):
try:
return DagBag(file_path, include_examples=False)
except Exception:
cls.logger().exception("Failed at reloading the DAG file %s", file_path)
Stats.incr("dag_file_refresh_error", tags={"file_path": file_path})
raise
@provide_session
def process_file(
self,
file_path: str,
callback_requests: list[CallbackRequest],
pickle_dags: bool = False,
session: Session = NEW_SESSION,
) -> tuple[int, 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
:return: number of dags found, count of import errors, last number of db queries
"""
self.log.info("Processing file %s for tasks to queue", file_path)
with count_queries(session) as query_counter:
try:
dagbag = DagFileProcessor._get_dagbag(file_path)
except Exception:
self.log.exception("Failed at reloading the DAG file %s", file_path)
Stats.incr("dag_file_refresh_error", 1, 1, tags={"file_path": file_path})
return 0, 0, self._cache_last_num_of_db_queries(query_counter)
if dagbag.dags:
self.log.info("DAG(s) %s retrieved from %s", ", ".join(map(repr, dagbag.dags)), file_path)
else:
self.log.warning("No viable dags retrieved from %s", file_path)
DagFileProcessor.update_import_errors(
file_last_changed=dagbag.file_last_changed,
import_errors=dagbag.import_errors,
processor_subdir=self._dag_directory,
)
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!
DagFileProcessor.execute_callbacks_without_dag(callback_requests, self.UNIT_TEST_MODE)
return 0, len(dagbag.import_errors), self._cache_last_num_of_db_queries(query_counter)
self.execute_callbacks(dagbag, callback_requests, self.UNIT_TEST_MODE)
serialize_errors = DagFileProcessor.save_dag_to_db(
dags=dagbag.dags,
dag_directory=self._dag_directory,
pickle_dags=pickle_dags,
)
dagbag.import_errors.update(dict(serialize_errors))
# Record import errors into the ORM
try:
DagFileProcessor.update_import_errors(
file_last_changed=dagbag.file_last_changed,
import_errors=dagbag.import_errors,
processor_subdir=self._dag_directory,
)
except Exception:
self.log.exception("Error logging import errors!")
# Record DAG warnings in the metadatabase.
try:
self.update_dag_warnings(dagbag=dagbag)
except Exception:
self.log.exception("Error logging DAG warnings.")
return len(dagbag.dags), len(dagbag.import_errors), self._cache_last_num_of_db_queries(query_counter)
def _cache_last_num_of_db_queries(self, query_counter: _QueryCounter | None = None):
if query_counter:
self._last_num_of_db_queries = query_counter.queries_number
return self._last_num_of_db_queries
@staticmethod
@internal_api_call
@provide_session
def save_dag_to_db(
dags: dict[str, DAG],
dag_directory: str,
pickle_dags: bool = False,
session=NEW_SESSION,
):
import_errors = DagBag._sync_to_db(dags=dags, processor_subdir=dag_directory, session=session)
session.commit()
dag_ids = list(dags)
if pickle_dags:
paused_dag_ids = DagModel.get_paused_dag_ids(dag_ids=dag_ids)
unpaused_dags: list[DAG] = [dag for dag_id, dag in dags.items() if dag_id not in paused_dag_ids]
for dag in unpaused_dags:
dag.pickle(session)
return import_errors