Source code for airflow.contrib.executors.mesos_executor

# -*- coding: utf-8 -*-
# Licensed 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from future import standard_library

from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.www.utils import LoginMixin

from builtins import str
from queue import Queue

import mesos.interface
from mesos.interface import mesos_pb2
import mesos.native

from airflow import configuration
from airflow.executors.base_executor import BaseExecutor
from airflow.settings import Session
from airflow.utils.state import State
from airflow.exceptions import AirflowException

FRAMEWORK_CONNID_PREFIX = 'mesos_framework_'

def get_framework_name():
    if not configuration.get('mesos', 'FRAMEWORK_NAME'):
    return configuration.get('mesos', 'FRAMEWORK_NAME')

# AirflowMesosScheduler, implements Mesos Scheduler interface
# To schedule airflow jobs on mesos
class AirflowMesosScheduler(mesos.interface.Scheduler, LoggingMixin):
    Airflow Mesos scheduler implements mesos scheduler interface
    to schedule airflow tasks on mesos.
    Basically, it schedules a command like
    'airflow run <dag_id> <task_instance_id> <start_date> --local -p=<pickle>'
    to run on a mesos slave.
    def __init__(self,
        self.task_queue = task_queue
        self.result_queue = result_queue
        self.task_cpu = task_cpu
        self.task_mem = task_mem
        self.task_counter = 0
        self.task_key_map = {}

    def registered(self, driver, frameworkId, masterInfo):"AirflowScheduler registered to Mesos with framework ID %s", frameworkId.value)

        if configuration.getboolean('mesos', 'CHECKPOINT') and configuration.get('mesos', 'FAILOVER_TIMEOUT'):
            # Import here to work around a circular import error
            from airflow.models import Connection

            # Update the Framework ID in the database.
            session = Session()
            conn_id = FRAMEWORK_CONNID_PREFIX + get_framework_name()
            connection = Session.query(Connection).filter_by(conn_id=conn_id).first()
            if connection is None:
                connection = Connection(conn_id=conn_id, conn_type='mesos_framework-id',
                connection.extra = frameworkId.value


    def reregistered(self, driver, masterInfo):"AirflowScheduler re-registered to mesos")

    def disconnected(self, driver):"AirflowScheduler disconnected from mesos")

    def offerRescinded(self, driver, offerId):"AirflowScheduler offer %s rescinded", str(offerId))

    def frameworkMessage(self, driver, executorId, slaveId, message):"AirflowScheduler received framework message %s", message)

    def executorLost(self, driver, executorId, slaveId, status):
        self.log.warning("AirflowScheduler executor %s lost", str(executorId))

    def slaveLost(self, driver, slaveId):
        self.log.warning("AirflowScheduler slave %s lost", str(slaveId))

    def error(self, driver, message):
        self.log.error("AirflowScheduler driver aborted %s", message)
        raise AirflowException("AirflowScheduler driver aborted %s" % message)

    def resourceOffers(self, driver, offers):
        for offer in offers:
            tasks = []
            offerCpus = 0
            offerMem = 0
            for resource in offer.resources:
                if == "cpus":
                    offerCpus += resource.scalar.value
                elif == "mem":
                    offerMem += resource.scalar.value

  "Received offer %s with cpus: %s and mem: %s",, offerCpus, offerMem)

            remainingCpus = offerCpus
            remainingMem = offerMem

            while (not self.task_queue.empty()) and \
                  remainingCpus >= self.task_cpu and \
                  remainingMem >= self.task_mem:
                key, cmd = self.task_queue.get()
                tid = self.task_counter
                self.task_counter += 1
                self.task_key_map[str(tid)] = key

      "Launching task %d using offer %s", tid,

                task = mesos_pb2.TaskInfo()
                task.task_id.value = str(tid)
                task.slave_id.value = offer.slave_id.value
       = "AirflowTask %d" % tid

                cpus = task.resources.add()
       = "cpus"
                cpus.type = mesos_pb2.Value.SCALAR
                cpus.scalar.value = self.task_cpu

                mem = task.resources.add()
       = "mem"
                mem.type = mesos_pb2.Value.SCALAR
                mem.scalar.value = self.task_mem

                command = mesos_pb2.CommandInfo()
       = True
                command.value = cmd


                remainingCpus -= self.task_cpu
                remainingMem -= self.task_mem

            driver.launchTasks(, tasks)

    def statusUpdate(self, driver, update):
            "Task %s is in state %s, data %s",
            update.task_id.value, mesos_pb2.TaskState.Name(update.state), str(

            key = self.task_key_map[update.task_id.value]
        except KeyError:
            # The map may not contain an item if the framework re-registered after a failover.
            # Discard these tasks.
            self.log.warning("Unrecognised task key %s", update.task_id.value)

        if update.state == mesos_pb2.TASK_FINISHED:
            self.result_queue.put((key, State.SUCCESS))

        if update.state == mesos_pb2.TASK_LOST or \
           update.state == mesos_pb2.TASK_KILLED or \
           update.state == mesos_pb2.TASK_FAILED:
            self.result_queue.put((key, State.FAILED))

[docs]class MesosExecutor(BaseExecutor, LoginMixin): """ MesosExecutor allows distributing the execution of task instances to multiple mesos workers. Apache Mesos is a distributed systems kernel which abstracts CPU, memory, storage, and other compute resources away from machines (physical or virtual), enabling fault-tolerant and elastic distributed systems to easily be built and run effectively. See """ def start(self): self.task_queue = Queue() self.result_queue = Queue() framework = mesos_pb2.FrameworkInfo() framework.user = '' if not configuration.get('mesos', 'MASTER'): self.log.error("Expecting mesos master URL for mesos executor") raise AirflowException("mesos.master not provided for mesos executor") master = configuration.get('mesos', 'MASTER') = get_framework_name() if not configuration.get('mesos', 'TASK_CPU'): task_cpu = 1 else: task_cpu = configuration.getint('mesos', 'TASK_CPU') if not configuration.get('mesos', 'TASK_MEMORY'): task_memory = 256 else: task_memory = configuration.getint('mesos', 'TASK_MEMORY') if configuration.getboolean('mesos', 'CHECKPOINT'): framework.checkpoint = True if configuration.get('mesos', 'FAILOVER_TIMEOUT'): # Import here to work around a circular import error from airflow.models import Connection # Query the database to get the ID of the Mesos Framework, if available. conn_id = FRAMEWORK_CONNID_PREFIX + session = Session() connection = session.query(Connection).filter_by(conn_id=conn_id).first() if connection is not None: # Set the Framework ID to let the scheduler reconnect with running tasks. = connection.extra framework.failover_timeout = configuration.getint('mesos', 'FAILOVER_TIMEOUT') else: framework.checkpoint = False 'MesosFramework master : %s, name : %s, cpu : %s, mem : %s, checkpoint : %s', master,, str(task_cpu), str(task_memory), str(framework.checkpoint) ) implicit_acknowledgements = 1 if configuration.getboolean('mesos', 'AUTHENTICATE'): if not configuration.get('mesos', 'DEFAULT_PRINCIPAL'): self.log.error("Expecting authentication principal in the environment") raise AirflowException("mesos.default_principal not provided in authenticated mode") if not configuration.get('mesos', 'DEFAULT_SECRET'): self.log.error("Expecting authentication secret in the environment") raise AirflowException("mesos.default_secret not provided in authenticated mode") credential = mesos_pb2.Credential() credential.principal = configuration.get('mesos', 'DEFAULT_PRINCIPAL') credential.secret = configuration.get('mesos', 'DEFAULT_SECRET') framework.principal = credential.principal driver = mesos.native.MesosSchedulerDriver( AirflowMesosScheduler(self.task_queue, self.result_queue, task_cpu, task_memory), framework, master, implicit_acknowledgements, credential) else: framework.principal = 'Airflow' driver = mesos.native.MesosSchedulerDriver( AirflowMesosScheduler(self.task_queue, self.result_queue, task_cpu, task_memory), framework, master, implicit_acknowledgements) self.mesos_driver = driver self.mesos_driver.start() def execute_async(self, key, command, queue=None): self.task_queue.put((key, command)) def sync(self): while not self.result_queue.empty(): results = self.result_queue.get() self.change_state(*results) def end(self): self.task_queue.join() self.mesos_driver.stop()