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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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r"""
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A tutorial example take you to experience pydolphinscheduler.
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After tutorial.py file submit to Apache DolphinScheduler server a DAG would be create,
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and workflow DAG graph as below:
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--> task_child_one
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/ \
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task_parent --> --> task_union
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\ /
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--> task_child_two
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it will instantiate and run all the task it have.
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"""
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from pydolphinscheduler.core.process_definition import ProcessDefinition
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from pydolphinscheduler.tasks.shell import Shell
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with ProcessDefinition(name="tutorial", tenant="tenant_exists") as pd:
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task_parent = Shell(name="task_parent", command="echo hello pydolphinscheduler")
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task_child_one = Shell(name="task_child_one", command="echo 'child one'")
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task_child_two = Shell(name="task_child_two", command="echo 'child two'")
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task_union = Shell(name="task_union", command="echo union")
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task_group = [task_child_one, task_child_two]
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task_parent.set_downstream(task_group)
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task_union << task_group
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pd.run()
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