# -*- coding: utf-8 -*-
#
# 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.
import distributed
import subprocess
import warnings
from airflow import configuration
from airflow.executors.base_executor import BaseExecutor
[docs]class DaskExecutor(BaseExecutor):
"""
DaskExecutor submits tasks to a Dask Distributed cluster.
"""
def __init__(self, cluster_address=None):
if cluster_address is None:
cluster_address = configuration.conf.get('dask', 'cluster_address')
if not cluster_address:
raise ValueError(
'Please provide a Dask cluster address in airflow.cfg')
self.cluster_address = cluster_address
# ssl / tls parameters
self.tls_ca = configuration.get('dask', 'tls_ca')
self.tls_key = configuration.get('dask', 'tls_key')
self.tls_cert = configuration.get('dask', 'tls_cert')
super(DaskExecutor, self).__init__(parallelism=0)
[docs] def start(self):
if self.tls_ca or self.tls_key or self.tls_cert:
from distributed.security import Security
security = Security(
tls_client_key=self.tls_key,
tls_client_cert=self.tls_cert,
tls_ca_file=self.tls_ca,
require_encryption=True,
)
else:
security = None
self.client = distributed.Client(self.cluster_address, security=security)
self.futures = {}
[docs] def execute_async(self, key, command, queue=None, executor_config=None):
if queue is not None:
warnings.warn(
'DaskExecutor does not support queues. '
'All tasks will be run in the same cluster'
)
def airflow_run():
return subprocess.check_call(command, close_fds=True)
future = self.client.submit(airflow_run, pure=False)
self.futures[future] = key
[docs] def _process_future(self, future):
if future.done():
key = self.futures[future]
if future.exception():
self.log.error("Failed to execute task: %s", repr(future.exception()))
self.fail(key)
elif future.cancelled():
self.log.error("Failed to execute task")
self.fail(key)
else:
self.success(key)
self.futures.pop(future)
[docs] def sync(self):
# make a copy so futures can be popped during iteration
for future in self.futures.copy():
self._process_future(future)
[docs] def end(self):
for future in distributed.as_completed(self.futures.copy()):
self._process_future(future)
[docs] def terminate(self):
self.client.cancel(self.futures.keys())
self.end()