Source code for airflow.sensors.smart_sensor

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import datetime
import json
import logging
import traceback
from logging.config import DictConfigurator  # type: ignore
from time import sleep

from sqlalchemy import and_, or_, tuple_

from airflow.exceptions import AirflowException, AirflowTaskTimeout
from airflow.models import BaseOperator, DagRun, SensorInstance, SkipMixin, TaskInstance
from airflow.settings import LOGGING_CLASS_PATH
from airflow.stats import Stats
from airflow.utils import helpers, timezone
from import send_email
from airflow.utils.log.logging_mixin import set_context
from airflow.utils.module_loading import import_string
from import get_hostname
from airflow.utils.session import provide_session
from airflow.utils.state import PokeState, State
from airflow.utils.timeout import timeout

[docs]config = import_string(LOGGING_CLASS_PATH)
[docs]handler_config = config['handlers']['task']
[docs] formatter_config = config['formatters'][handler_config['formatter']]
except Exception as err: formatter_config = None print(err)
[docs]dictConfigurator = DictConfigurator(config)
[docs]class SensorWork: """ This class stores a sensor work with decoded context value. It is only used inside of smart sensor. Create a sensor work based on sensor instance record. A sensor work object has the following attributes: `dag_id`: sensor_instance dag_id. `task_id`: sensor_instance task_id. `execution_date`: sensor_instance execution_date. `try_number`: sensor_instance try_number `poke_context`: Decoded poke_context for the sensor task. `execution_context`: Decoded execution_context. `hashcode`: This is the signature of poking job. `operator`: The sensor operator class. `op_classpath`: The sensor operator class path `encoded_poke_context`: The raw data from sensor_instance poke_context column. `log`: The sensor work logger which will mock the corresponding task instance log. :param si: The sensor_instance ORM object. """ def __init__(self, si): self.dag_id = si.dag_id self.task_id = si.task_id self.execution_date = si.execution_date self.try_number = si.try_number self.poke_context = json.loads(si.poke_context) if si.poke_context else {} self.execution_context = json.loads(si.execution_context) if si.execution_context else {} try: self.log = self._get_sensor_logger(si) except Exception as e: self.log = None print(e) self.hashcode = si.hashcode self.start_date = si.start_date self.operator = si.operator self.op_classpath = si.op_classpath self.encoded_poke_context = si.poke_context
[docs] def __eq__(self, other): if not isinstance(other, SensorWork): return NotImplemented return ( self.dag_id == other.dag_id and self.task_id == other.task_id and self.execution_date == other.execution_date and self.try_number == other.try_number
) @staticmethod
[docs] def create_new_task_handler(): """ Create task log handler for a sensor work. :return: log handler """ from airflow.utils.log.secrets_masker import _secrets_masker handler_config_copy = {k: handler_config[k] for k in handler_config} del handler_config_copy['filters'] formatter_config_copy = {k: formatter_config[k] for k in formatter_config} handler = dictConfigurator.configure_handler(handler_config_copy) formatter = dictConfigurator.configure_formatter(formatter_config_copy) handler.setFormatter(formatter) # We want to share the _global_ filterer instance, not create a new one handler.addFilter(_secrets_masker()) return handler
def _get_sensor_logger(self, si): """Return logger for a sensor instance object.""" # The created log_id is used inside of smart sensor as the key to fetch # the corresponding in memory log handler. si.raw = False # Otherwise set_context will fail log_id = "-".join( [si.dag_id, si.task_id, si.execution_date.strftime("%Y_%m_%dT%H_%M_%S_%f"), str(si.try_number)] ) logger = logging.getLogger('airflow.task' + '.' + log_id) if len(logger.handlers) == 0: handler = self.create_new_task_handler() logger.addHandler(handler) set_context(logger, si) line_break = "-" * 120 "Processing sensor task %s in smart sensor service on host: %s", self.ti_key, get_hostname() ) return logger
[docs] def close_sensor_logger(self): """Close log handler for a sensor work.""" for handler in self.log.handlers: try: handler.close() except Exception as e: print(e)
[docs] def ti_key(self): """Key for the task instance that maps to the sensor work.""" return self.dag_id, self.task_id, self.execution_date
[docs] def cache_key(self): """Key used to query in smart sensor for cached sensor work.""" return self.operator, self.encoded_poke_context
[docs]class CachedPokeWork: """ Wrapper class for the poke work inside smart sensor. It saves the sensor_task used to poke and recent poke result state. state: poke state. sensor_task: The cached object for executing the poke function. last_poke_time: The latest time this cached work being called. to_flush: If we should flush the cached work. """ def __init__(self): self.state = None self.sensor_task = None self.last_poke_time = None self.to_flush = False
[docs] def set_state(self, state): """ Set state for cached poke work. :param state: The sensor_instance state. """ self.state = state self.last_poke_time = timezone.utcnow()
[docs] def clear_state(self): """Clear state for cached poke work.""" self.state = None
[docs] def set_to_flush(self): """Mark this poke work to be popped from cached dict after current loop.""" self.to_flush = True
[docs] def is_expired(self): """ The cached task object expires if there is no poke for 20 minutes. :return: Boolean """ return self.to_flush or (timezone.utcnow() - self.last_poke_time).total_seconds() > 1200
[docs]class SensorExceptionInfo: """ Hold sensor exception information and the type of exception. For possible transient infra failure, give the task more chance to retry before fail it. """ def __init__( self, exception_info, is_infra_failure=False, infra_failure_retry_window=datetime.timedelta(minutes=130), ): self._exception_info = exception_info self._is_infra_failure = is_infra_failure self._infra_failure_retry_window = infra_failure_retry_window self._infra_failure_timeout = None self.set_infra_failure_timeout() self.fail_current_run = self.should_fail_current_run()
[docs] def set_latest_exception(self, exception_info, is_infra_failure=False): """ This function set the latest exception information for sensor exception. If the exception implies an infra failure, this function will check the recorded infra failure timeout which was set at the first infra failure exception arrives. There is a 6 hours window for retry without failing current run. :param exception_info: Details of the exception information. :param is_infra_failure: If current exception was caused by transient infra failure. There is a retry window _infra_failure_retry_window that the smart sensor will retry poke function without failing current task run. """ self._exception_info = exception_info self._is_infra_failure = is_infra_failure self.set_infra_failure_timeout() self.fail_current_run = self.should_fail_current_run()
[docs] def set_infra_failure_timeout(self): """ Set the time point when the sensor should be failed if it kept getting infra failure. :return: """ # Only set the infra_failure_timeout if there is no existing one if not self._is_infra_failure: self._infra_failure_timeout = None elif self._infra_failure_timeout is None: self._infra_failure_timeout = timezone.utcnow() + self._infra_failure_retry_window
[docs] def should_fail_current_run(self): """ :return: Should the sensor fail :type: boolean """ return not self.is_infra_failure or timezone.utcnow() > self._infra_failure_timeout
[docs] def exception_info(self): """:return: exception msg.""" return self._exception_info
[docs] def is_infra_failure(self): """ :return: If the exception is an infra failure :type: boolean """ return self._is_infra_failure
[docs] def is_expired(self): """ :return: If current exception need to be kept. :type: boolean """ if not self._is_infra_failure: return True return timezone.utcnow() > self._infra_failure_timeout + datetime.timedelta(minutes=30)
[docs]class SmartSensorOperator(BaseOperator, SkipMixin): """ Smart sensor operators are derived from this class. Smart Sensor operators keep refresh a dictionary by visiting DB. Taking qualified active sensor tasks. Different from sensor operator, Smart sensor operators poke for all sensor tasks in the dictionary at a time interval. When a criteria is met or fail by time out, it update all sensor task state in task_instance table :param soft_fail: Set to true to mark the task as SKIPPED on failure :type soft_fail: bool :param poke_interval: Time in seconds that the job should wait in between each tries. :type poke_interval: int :param smart_sensor_timeout: Time, in seconds before the internal sensor job times out if poke_timeout is not defined. :type smart_sensor_timeout: float :param shard_min: shard code lower bound (inclusive) :type shard_min: int :param shard_max: shard code upper bound (exclusive) :type shard_max: int :param poke_timeout: Time, in seconds before the task times out and fails. :type poke_timeout: float """
[docs] ui_color = '#e6f1f2'
def __init__( self, poke_interval=180, smart_sensor_timeout=60 * 60 * 24 * 7, soft_fail=False, shard_min=0, shard_max=100000, poke_timeout=6.0, *args, **kwargs, ): super().__init__(*args, **kwargs) # super(SmartSensorOperator, self).__init__(*args, **kwargs) self.poke_interval = poke_interval self.soft_fail = soft_fail self.timeout = smart_sensor_timeout self._validate_input_values() self.hostname = "" self.sensor_works = [] self.cached_dedup_works = {} self.cached_sensor_exceptions = {} self.max_tis_per_query = 50 self.shard_min = shard_min self.shard_max = shard_max self.poke_timeout = poke_timeout def _validate_input_values(self): if not isinstance(self.poke_interval, (int, float)) or self.poke_interval < 0: raise AirflowException("The poke_interval must be a non-negative number") if not isinstance(self.timeout, (int, float)) or self.timeout < 0: raise AirflowException("The timeout must be a non-negative number") @provide_session def _load_sensor_works(self, session=None): """ Refresh sensor instances need to be handled by this operator. Create smart sensor internal object based on the information persisted in the sensor_instance table. """ SI = SensorInstance with Stats.timer() as timer: query = ( session.query(SI) .filter(SI.state == State.SENSING) .filter(SI.shardcode < self.shard_max, SI.shardcode >= self.shard_min) ) tis = query.all()"Performance query %s tis, time: %.3f", len(tis), timer.duration) # Query without checking dagrun state might keep some failed dag_run tasks alive. # Join with DagRun table will be very slow based on the number of sensor tasks we # need to handle. We query all smart tasks in this operator # and expect scheduler correct the states in _change_state_for_tis_without_dagrun() sensor_works = [] for ti in tis: try: sensor_works.append(SensorWork(ti)) except Exception: self.log.exception("Exception at creating sensor work for ti %s", ti.key)"%d tasks detected.", len(sensor_works)) new_sensor_works = [x for x in sensor_works if x not in self.sensor_works] self._update_ti_hostname(new_sensor_works) self.sensor_works = sensor_works @provide_session def _update_ti_hostname(self, sensor_works, session=None): """ Update task instance hostname for new sensor works. :param sensor_works: Smart sensor internal object for a sensor task. :param session: The sqlalchemy session. """ DR = DagRun TI = TaskInstance def update_ti_hostname_with_count(count, sensor_works): # Using or_ instead of in_ here to prevent from full table scan. if == 'mssql': ti_filter = or_( and_( TI.dag_id == ti_key.dag_id, TI.task_id == ti_key.task_id, DR.execution_date == ti_key.execution_date, ) for ti_key in sensor_works ) else: ti_keys = [(x.dag_id, x.task_id, x.execution_date) for x in sensor_works] ti_filter = or_( tuple_(TI.dag_id, TI.task_id, DR.execution_date) == ti_key for ti_key in ti_keys ) for ti in session.query(TI).join(TI.dag_run).filter(ti_filter): ti.hostname = self.hostname session.commit() return count + len(sensor_works) count = helpers.reduce_in_chunks( update_ti_hostname_with_count, sensor_works, 0, self.max_tis_per_query ) if count:"Updated hostname on %s tis.", count) @provide_session def _mark_multi_state(self, operator, poke_hash, encoded_poke_context, state, session=None): """ Mark state for multiple tasks in the task_instance table to a new state if they have the same signature as the poke_hash. :param operator: The sensor's operator class name. :param poke_hash: The hash code generated from sensor's poke context. :param encoded_poke_context: The raw encoded poke_context. :param state: Set multiple sensor tasks to this state. :param session: The sqlalchemy session. """ def mark_state(ti, sensor_instance): ti.state = state sensor_instance.state = state if state in State.finished: ti.end_date = end_date ti.set_duration() SI = SensorInstance TI = TaskInstance count_marked = 0 query_result = [] try: query_result = ( session.query(TI, SI) .join( TI, and_( TI.dag_id == SI.dag_id, TI.task_id == SI.task_id, TI.execution_date == SI.execution_date, ), ) .filter(SI.state == State.SENSING) .filter(SI.hashcode == poke_hash) .filter(SI.operator == operator) .with_for_update() .all() ) end_date = timezone.utcnow() for ti, sensor_instance in query_result: if sensor_instance.poke_context != encoded_poke_context: continue ti.hostname = self.hostname if ti.state == State.SENSING: mark_state(ti=ti, sensor_instance=sensor_instance) count_marked += 1 else: # ti.state != State.SENSING sensor_instance.state = ti.state session.commit() except Exception: self.log.warning( "Exception _mark_multi_state in smart sensor for hashcode %s", str(poke_hash), # cast to str in advance for highlighting exc_info=True, )"Marked %s tasks out of %s to state %s", count_marked, len(query_result), state) @provide_session def _retry_or_fail_task(self, sensor_work, error, session=None): """ Change single task state for sensor task. For final state, set the end_date. Since smart sensor take care all retries in one process. Failed sensor tasks logically experienced all retries and the try_number should be set to max_tries. :param sensor_work: The sensor_work with exception. :type sensor_work: SensorWork :param error: The error message for this sensor_work. :type error: str. :param session: The sqlalchemy session. """ def email_alert(task_instance, error_info): try: subject, html_content, _ = task_instance.get_email_subject_content(error_info) email = sensor_work.execution_context.get('email') send_email(email, subject, html_content) except Exception: sensor_work.log.warning("Exception alerting email.", exc_info=True) def handle_failure(sensor_work, ti): if sensor_work.execution_context.get('retries') and ti.try_number <= ti.max_tries: # retry ti.state = State.UP_FOR_RETRY if sensor_work.execution_context.get('email_on_retry') and sensor_work.execution_context.get( 'email' ):"%s sending email alert for retry", sensor_work.ti_key) email_alert(ti, error) else: ti.state = State.FAILED if sensor_work.execution_context.get( 'email_on_failure' ) and sensor_work.execution_context.get('email'):"%s sending email alert for failure", sensor_work.ti_key) email_alert(ti, error) try: dag_id, task_id, execution_date = sensor_work.ti_key TI = TaskInstance SI = SensorInstance sensor_instance = ( session.query(SI) .filter(SI.dag_id == dag_id, SI.task_id == task_id, SI.execution_date == execution_date) .with_for_update() .first() ) if sensor_instance.hashcode != sensor_work.hashcode: # Return without setting state return ti = ( session.query(TI) .filter(TI.dag_id == dag_id, TI.task_id == task_id, TI.execution_date == execution_date) .with_for_update() .first() ) if ti: if ti.state == State.SENSING: ti.hostname = self.hostname handle_failure(sensor_work, ti) sensor_instance.state = State.FAILED ti.end_date = timezone.utcnow() ti.set_duration() else: sensor_instance.state = ti.state session.merge(sensor_instance) session.merge(ti) session.commit() "Task %s got an error: %s. Set the state to failed. Exit.", str(sensor_work.ti_key), error ) sensor_work.close_sensor_logger() except AirflowException: sensor_work.log.warning("Exception on failing %s", sensor_work.ti_key, exc_info=True) def _check_and_handle_ti_timeout(self, sensor_work): """ Check if a sensor task in smart sensor is timeout. Could be either sensor operator timeout or general operator execution_timeout. :param sensor_work: SensorWork """ task_timeout = sensor_work.execution_context.get('timeout', self.timeout) task_execution_timeout = sensor_work.execution_context.get('execution_timeout') if task_execution_timeout: task_timeout = min(task_timeout, task_execution_timeout) if (timezone.utcnow() - sensor_work.start_date).total_seconds() > task_timeout: error = "Sensor Timeout" sensor_work.log.exception(error) self._retry_or_fail_task(sensor_work, error) def _handle_poke_exception(self, sensor_work): """ Fail task if accumulated exceptions exceeds retries. :param sensor_work: SensorWork """ sensor_exception = self.cached_sensor_exceptions.get(sensor_work.cache_key) error = sensor_exception.exception_info sensor_work.log.exception("Handling poke exception: %s", error) if sensor_exception.fail_current_run: if sensor_exception.is_infra_failure: sensor_work.log.exception( "Task %s failed by infra failure in smart sensor.", sensor_work.ti_key ) # There is a risk for sensor object cached in smart sensor keep throwing # exception and cause an infra failure. To make sure the sensor tasks after # retry will not fall into same object and have endless infra failure, # we mark the sensor task after an infra failure so that it can be popped # before next poke loop. cache_key = sensor_work.cache_key self.cached_dedup_works[cache_key].set_to_flush() else: sensor_work.log.exception("Task %s failed by exceptions.", sensor_work.ti_key) self._retry_or_fail_task(sensor_work, error) else:"Exception detected, retrying without failing current run.") self._check_and_handle_ti_timeout(sensor_work) def _process_sensor_work_with_cached_state(self, sensor_work, state): if state == PokeState.LANDED:"Task %s succeeded", str(sensor_work.ti_key)) sensor_work.close_sensor_logger() if state == PokeState.NOT_LANDED: # Handle timeout if connection valid but not landed yet self._check_and_handle_ti_timeout(sensor_work) elif state == PokeState.POKE_EXCEPTION: self._handle_poke_exception(sensor_work) def _execute_sensor_work(self, sensor_work): ti_key = sensor_work.ti_key log = sensor_work.log or self.log"Sensing ti: %s", str(ti_key))"Poking with arguments: %s", sensor_work.encoded_poke_context) cache_key = sensor_work.cache_key if cache_key not in self.cached_dedup_works: # create an empty cached_work for a new cache_key self.cached_dedup_works[cache_key] = CachedPokeWork() cached_work = self.cached_dedup_works[cache_key] if cached_work.state is not None: # Have a valid cached state, don't poke twice in certain time interval self._process_sensor_work_with_cached_state(sensor_work, cached_work.state) return try: with timeout(seconds=self.poke_timeout): if self.poke(sensor_work): # Got a landed signal, mark all tasks waiting for this partition cached_work.set_state(PokeState.LANDED) self._mark_multi_state( sensor_work.operator, sensor_work.hashcode, sensor_work.encoded_poke_context, State.SUCCESS, )"Task %s succeeded", str(ti_key)) sensor_work.close_sensor_logger() else: # Not landed yet. Handle possible timeout cached_work.set_state(PokeState.NOT_LANDED) self._check_and_handle_ti_timeout(sensor_work) self.cached_sensor_exceptions.pop(cache_key, None) except Exception as e: # The retry_infra_failure decorator inside hive_hooks will raise exception with # is_infra_failure == True. Long poking timeout here is also considered an infra # failure. Other exceptions should fail. is_infra_failure = getattr(e, 'is_infra_failure', False) or isinstance(e, AirflowTaskTimeout) exception_info = traceback.format_exc() cached_work.set_state(PokeState.POKE_EXCEPTION) if cache_key in self.cached_sensor_exceptions: self.cached_sensor_exceptions[cache_key].set_latest_exception( exception_info, is_infra_failure=is_infra_failure ) else: self.cached_sensor_exceptions[cache_key] = SensorExceptionInfo( exception_info, is_infra_failure=is_infra_failure ) self._handle_poke_exception(sensor_work)
[docs] def flush_cached_sensor_poke_results(self): """Flush outdated cached sensor states saved in previous loop.""" for key, cached_work in self.cached_dedup_works.copy().items(): if cached_work.is_expired(): self.cached_dedup_works.pop(key, None) else: cached_work.state = None for ti_key, sensor_exception in self.cached_sensor_exceptions.copy().items(): if sensor_exception.fail_current_run or sensor_exception.is_expired(): self.cached_sensor_exceptions.pop(ti_key, None)
[docs] def poke(self, sensor_work): """ Function that the sensors defined while deriving this class should override. """ cached_work = self.cached_dedup_works[sensor_work.cache_key] if not cached_work.sensor_task: init_args = dict(list(sensor_work.poke_context.items()) + [('task_id', sensor_work.task_id)]) operator_class = import_string(sensor_work.op_classpath) cached_work.sensor_task = operator_class(**init_args) return cached_work.sensor_task.poke(sensor_work.poke_context)
def _emit_loop_stats(self): try: count_poke = 0 count_poke_success = 0 count_poke_exception = 0 count_exception_failures = 0 count_infra_failure = 0 for cached_work in self.cached_dedup_works.values(): if cached_work.state is None: continue count_poke += 1 if cached_work.state == PokeState.LANDED: count_poke_success += 1 elif cached_work.state == PokeState.POKE_EXCEPTION: count_poke_exception += 1 for cached_exception in self.cached_sensor_exceptions.values(): if cached_exception.is_infra_failure and cached_exception.fail_current_run: count_infra_failure += 1 if cached_exception.fail_current_run: count_exception_failures += 1 Stats.gauge("smart_sensor_operator.poked_tasks", count_poke) Stats.gauge("smart_sensor_operator.poked_success", count_poke_success) Stats.gauge("smart_sensor_operator.poked_exception", count_poke_exception) Stats.gauge("smart_sensor_operator.exception_failures", count_exception_failures) Stats.gauge("smart_sensor_operator.infra_failures", count_infra_failure) except Exception: self.log.exception("Exception at getting loop stats %s")
[docs] def execute(self, context): started_at = timezone.utcnow() self.hostname = get_hostname() while True: poke_start_time = timezone.utcnow() self.flush_cached_sensor_poke_results() self._load_sensor_works()"Loaded %s sensor_works", len(self.sensor_works)) Stats.gauge("smart_sensor_operator.loaded_tasks", len(self.sensor_works)) for sensor_work in self.sensor_works: self._execute_sensor_work(sensor_work) duration = (timezone.utcnow() - poke_start_time).total_seconds()"Taking %s to execute %s tasks.", duration, len(self.sensor_works)) Stats.timing("smart_sensor_operator.loop_duration", duration) Stats.gauge("smart_sensor_operator.executed_tasks", len(self.sensor_works)) self._emit_loop_stats() if duration < self.poke_interval: sleep(self.poke_interval - duration) if (timezone.utcnow() - started_at).total_seconds() > self.timeout:"Time is out for smart sensor.") return
[docs] def on_kill(self): pass
if __name__ == '__main__': SmartSensorOperator(task_id='test').execute({})

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