分布式调度框架。
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# Licensed to the Apache Software Foundation (ASF) under one
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# 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.
r"""
A tutorial example take you to experience pydolphinscheduler.
After tutorial.py file submit to Apache DolphinScheduler server a DAG would be create,
and workflow DAG graph as below:
--> task_child_one
/ \
task_parent --> --> task_union
\ /
--> task_child_two
it will instantiate and run all the task it have.
"""
from pydolphinscheduler.core.process_definition import ProcessDefinition
from pydolphinscheduler.tasks.shell import Shell
with ProcessDefinition(
name="tutorial",
schedule="0 0 0 * * ? *",
start_time="2021-01-01",
tenant="tenant_exists",
) as pd:
task_parent = Shell(name="task_parent", command="echo hello pydolphinscheduler")
task_child_one = Shell(name="task_child_one", command="echo 'child one'")
task_child_two = Shell(name="task_child_two", command="echo 'child two'")
task_union = Shell(name="task_union", command="echo union")
task_group = [task_child_one, task_child_two]
task_parent.set_downstream(task_group)
task_union << task_group
pd.run()