# -*- coding: utf-8 -*-
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# to you under the Apache License, Version 2.0 (the
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from builtins import range
from collections import OrderedDict
# To avoid circular imports
import airflow.utils.dag_processing
from airflow import configuration
from airflow.settings import Stats
from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.utils.state import State
[docs]PARALLELISM = configuration.conf.getint('core', 'PARALLELISM')
[docs]class BaseExecutor(LoggingMixin):
def __init__(self, parallelism=PARALLELISM):
"""
Class to derive in order to interface with executor-type systems
like Celery, Mesos, Yarn and the likes.
:param parallelism: how many jobs should run at one time. Set to
``0`` for infinity
:type parallelism: int
"""
self.parallelism = parallelism
self.queued_tasks = OrderedDict()
self.running = {}
self.event_buffer = {}
[docs] def start(self): # pragma: no cover
"""
Executors may need to get things started. For example LocalExecutor
starts N workers.
"""
pass
[docs] def queue_command(self, simple_task_instance, command, priority=1, queue=None):
key = simple_task_instance.key
if key not in self.queued_tasks and key not in self.running:
self.log.info("Adding to queue: %s", command)
self.queued_tasks[key] = (command, priority, queue, simple_task_instance)
else:
self.log.info("could not queue task %s", key)
[docs] def queue_task_instance(
self,
task_instance,
mark_success=False,
pickle_id=None,
ignore_all_deps=False,
ignore_depends_on_past=False,
ignore_task_deps=False,
ignore_ti_state=False,
pool=None,
cfg_path=None):
pool = pool or task_instance.pool
# TODO (edgarRd): AIRFLOW-1985:
# cfg_path is needed to propagate the config values if using impersonation
# (run_as_user), given that there are different code paths running tasks.
# For a long term solution we need to address AIRFLOW-1986
command = task_instance.command_as_list(
local=True,
mark_success=mark_success,
ignore_all_deps=ignore_all_deps,
ignore_depends_on_past=ignore_depends_on_past,
ignore_task_deps=ignore_task_deps,
ignore_ti_state=ignore_ti_state,
pool=pool,
pickle_id=pickle_id,
cfg_path=cfg_path)
self.queue_command(
airflow.utils.dag_processing.SimpleTaskInstance(task_instance),
command,
priority=task_instance.task.priority_weight_total,
queue=task_instance.task.queue)
[docs] def has_task(self, task_instance):
"""
Checks if a task is either queued or running in this executor
:param task_instance: TaskInstance
:return: True if the task is known to this executor
"""
if task_instance.key in self.queued_tasks or task_instance.key in self.running:
return True
[docs] def sync(self):
"""
Sync will get called periodically by the heartbeat method.
Executors should override this to perform gather statuses.
"""
pass
[docs] def heartbeat(self):
# Triggering new jobs
if not self.parallelism:
open_slots = len(self.queued_tasks)
else:
open_slots = self.parallelism - len(self.running)
num_running_tasks = len(self.running)
num_queued_tasks = len(self.queued_tasks)
self.log.debug("%s running task instances", num_running_tasks)
self.log.debug("%s in queue", num_queued_tasks)
self.log.debug("%s open slots", open_slots)
Stats.gauge('executor.open_slots', open_slots)
Stats.gauge('executor.queued_tasks', num_queued_tasks)
Stats.gauge('executor.running_tasks', num_running_tasks)
sorted_queue = sorted(
[(k, v) for k, v in self.queued_tasks.items()],
key=lambda x: x[1][1],
reverse=True)
for i in range(min((open_slots, len(self.queued_tasks)))):
key, (command, _, queue, simple_ti) = sorted_queue.pop(0)
self.queued_tasks.pop(key)
self.running[key] = command
self.execute_async(key=key,
command=command,
queue=queue,
executor_config=simple_ti.executor_config)
# Calling child class sync method
self.log.debug("Calling the %s sync method", self.__class__)
self.sync()
[docs] def change_state(self, key, state):
self.log.debug("Changing state: %s", key)
self.running.pop(key, None)
self.event_buffer[key] = state
[docs] def fail(self, key):
self.change_state(key, State.FAILED)
[docs] def success(self, key):
self.change_state(key, State.SUCCESS)
[docs] def get_event_buffer(self, dag_ids=None):
"""
Returns and flush the event buffer. In case dag_ids is specified
it will only return and flush events for the given dag_ids. Otherwise
it returns and flushes all
:param dag_ids: to dag_ids to return events for, if None returns all
:return: a dict of events
"""
cleared_events = dict()
if dag_ids is None:
cleared_events = self.event_buffer
self.event_buffer = dict()
else:
for key in list(self.event_buffer.keys()):
dag_id, _, _, _ = key
if dag_id in dag_ids:
cleared_events[key] = self.event_buffer.pop(key)
return cleared_events
[docs] def execute_async(self,
key,
command,
queue=None,
executor_config=None): # pragma: no cover
"""
This method will execute the command asynchronously.
"""
raise NotImplementedError()
[docs] def end(self): # pragma: no cover
"""
This method is called when the caller is done submitting job and is
wants to wait synchronously for the job submitted previously to be
all done.
"""
raise NotImplementedError()
[docs] def terminate(self):
"""
This method is called when the daemon receives a SIGTERM
"""
raise NotImplementedError()