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- # 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.
- 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()
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