.. 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. Concepts ======== In this section, you would know the core concepts of *PyDolphinScheduler*. Workflow -------- Workflow describes the whole things except `tasks`_ and `tasks dependence`_, which includes name, schedule interval, schedule start time and end time. You would know scheduler Workflow could be initialized in a normal assignment statement or within a context manger. .. code-block:: python # Initialization with assign statement workflow = Workflow(name="my first workflow") # Or context manger with Workflow(name="my first workflow") as workflow: workflow.submit() Workflow is the main object communicating between *PyDolphinScheduler* and DolphinScheduler daemon. After workflow and task is declared, you could use `submit` and `run` to notify server your definition. If you just want to submit your definition and create workflow, without running it, you should use attribute `submit`. But if you want to run the workflow after you submit it, you could use attribute `run`. .. code-block:: python # Just submit definition, without run it workflow.submit() # Both submit and run definition workflow.run() Schedule ~~~~~~~~ We use parameter `schedule` to determine the schedule interval of workflow, *PyDolphinScheduler* supports seven asterisks expression, and each of the meaning of position is as below .. code-block:: text * * * * * * * ┬ ┬ ┬ ┬ ┬ ┬ ┬ │ │ │ │ │ │ │ │ │ │ │ │ │ └─── year │ │ │ │ │ └───── day of week (1 - 7) (1 to 7 are Sunday to Saturday, or use names; 7 is for Sunday, or use `SUN`) │ │ │ │ └─────── month (1 - 12) │ │ │ └───────── day of month (1 - 31) │ │ └─────────── hour (0 - 23) │ └───────────── min (0 - 59) └─────────────── second (0 - 59) Here we add some example crontab: - `0 0 0 * * ? *`: Workflow execute every day at 00:00:00. - `10 2 * * * ? *`: Workflow execute hourly day at ten pass two. - `10,11 20 0 1,2 * ? *`: Workflow execute first and second day of month at 00:20:10 and 00:20:11. Tenant ~~~~~~ Tenant is the user who run task command in machine or in virtual machine. it could be assign by simple string. You should change the tenant value to exists tenant in your host, it config in `config.yaml` in your pydolphinscheduler ``PYDS_HOME``, or via :doc:`CLI ` .. code-block:: bash pydolphinscheduler config --set default.user.tenant .. note:: Make should tenant exists in target machine, otherwise it will raise an error when you try to run command Execution Type ~~~~~~~~~~~~~~ Decision which behavior to run when workflow have multiple instances. when workflow schedule interval is too short, it may cause multiple instances run at the same time. We can use this parameter to control the behavior about how to run those workflow instances. Currently we have four execution type: * ``parallel`` (default value): it means all instances will allow to run even though the previous instance is not finished. * ``serial_wait``: it means the all instance will wait for the previous instance to finish, and all the waiting instances will be executed base on scheduling order. * ``serial_discard``: it means the all instance will be discard(abandon) if the previous instance is not finished. * ``serial_priority``: it means the all instance will wait for the previous instance to finish, and all the waiting instances will be executed base on workflow priority order. Parameter ``execution type`` can be set in * Direct assign statement. You can pick execute type from above and direct assign to parameter ``execution_type``. .. code-block:: python workflow = Workflow( name="workflow_name", execution_type="parallel" ) * Via environment variables, configurations file setting, for more detail about those way setting, you can see you can read :doc:`config` section. Alert ~~~~~ Alert is the way to notify user when workflow instance is success or failed. We can set alert with parameter ``warning_type`` and ``warning_group_id`` in workflow definition. * ``warning_type`` represent the type of alert, when workflow instance in those status, it will trigger alert. The value of ``warning_type`` could be one of ``failure``, ``success``, ``all``, ``none``. * ``warning_group_id`` represent the group of alert, you can get the group id from DolphinScheduler web UI. Tasks ----- Task is the minimum unit running actual job, and it is a node of DAG, aka directed acyclic graph. You could define what you want in the task. It has some required parameters to make uniqueness and definition. Here we use :py:meth:`pydolphinscheduler.tasks.Shell` as example, parameter `name` and `command` is required and must be provider. Parameter `name` set name to the task, and parameter `command` declare the command you wish to run in this task. .. code-block:: python # We named this task as "shell", and just run command `echo shell task` shell_task = Shell(name="shell", command="echo shell task") If you want to see all types of tasks, you could see :doc:`tasks/index`. Tasks Dependence ~~~~~~~~~~~~~~~~ You could define many tasks in on single `Workflow`_. If all those tasks are in parallel processing, then you could leave them alone without adding any additional information. But if there are some tasks that should not be run unless pre task in workflow has been done, we should set task dependence to them. Set task dependence have two main ways and both of them are easy. You could use bitwise operator `>>` and `<<`, or task attribute `set_downstream` and `set_upstream` to do it. .. code-block:: python # Set task1 as task2 upstream task1 >> task2 # You could use attribute `set_downstream` too, is same as `task1 >> task2` task1.set_downstream(task2) # Set task1 as task2 downstream task1 << task2 # It is same as attribute `set_upstream` task1.set_upstream(task2) # Beside, we could set dependence between task and sequence of tasks, # we set `task1` is upstream to both `task2` and `task3`. It is useful # for some tasks have same dependence. task1 >> [task2, task3] Task With Workflow ~~~~~~~~~~~~~~~~~~ In most of data orchestration cases, you should assign attribute `workflow` to task instance to decide workflow of task. You could set `workflow` in both normal assign or in context manger mode .. code-block:: python # Normal assign, have to explicit declaration and pass `Workflow` instance to task workflow = Workflow(name="my first workflow") shell_task = Shell(name="shell", command="echo shell task", workflow=workflow) # Context manger, `Workflow` instance workflow would implicit declaration to task with Workflow(name="my first workflow") as workflow: shell_task = Shell(name="shell", command="echo shell task", With both `Workflow`_, `Tasks`_ and `Tasks Dependence`_, we could build a workflow with multiple tasks. Task Group ~~~~~~~~~~ A task group can manage and control the maximum number of concurrently running tasks. This is particularly useful when you want to limit the simultaneous execution of various task types. For instance, in an ETL (Extract, Transform, Load) job where data is extracted from a source database, it's crucial to control the parallelism of extract tasks to prevent an excessive number of connections to the source database. This is where a task group comes into play. There are two key parameters, ``task_group_id`` and ``task_group_priority`` that determine the behavior of the task group. Task group can control the maximum number of tasks running at the same time. It is useful when you don't want to run too many type of tasks at the same time. For example when you extract data from source database in ELT job, you want to control the parallelism of extract task to avoid too many connections to source database. Then task group can help you. There are two major parameters ``task_group_id`` and ``task_group_priority`` to control the behavior of task group. * ``task_group_id``: is an integer used to identify the task group. You can set a ``task_group_id`` to restrict the parallelism of tasks. The ``task_group_id`` can be find in the DolphinScheduler web UI. The default value is ``0``, which means there are no restrictions for this task group. * ``task_group_priority``: is an integer used to define the priority of the task group. When different tasks share the same ``task_group_id``, the task group's priority comes into play, controlling the order in which they run. Higher values indicate higher priority. The default value is ``0``, which means there's no specific priority for this task group, and tasks will run in the order they were created. Here's an example in Python: .. code-block:: python extract = Shell( name="extract", command="echo 'Some extract command here'", task_group_id=1, task_group_priority=123 ) Resource Files -------------- During workflow running, we may need some resource files to help us run task usually. One of a common situation is that we already have some executable files locally, and we need to schedule a specific time, or add them to existing workflow by adding the new tasks. Of course, we can upload those files to target machine and run them in :doc:`shell task ` by reference the absolute path of file. But if we have more than one machine to run task, we have to upload those files to each of them. And it is not convenient and not flexible, because we may need to change our resource files sometimes. One more pydolphinscheduler way is to upload those files together with `workflow`_, and use them in task to run. For example, you have a bash script named ``echo-ten.sh`` locally, and it contains some code like this: .. code-block:: bash #!/bin/env bash max=10 for ((i=1; i <= $max; ++i)); do echo "$i" done and you want to use it in workflow but do not want to copy-paste it to shell task parameter ``command``. You could use this mechanism to upload it to resource center when you create workflow .. code-block:: python # Read file content file_name = "echo-ten.sh" with open(file_name, "r") as f: content = f.read() with Workflow( name="upload_and_run", resource_list=[ Resource(name=file_name, content=content), ], ) as workflow: And when we call :code:`workflow.run()` the new file named ``echo-ten.sh`` would be uploaded to dolphinscheduler resource center. After that we can use this file in our task by reference it by name, in this case we could use :doc:`shell task ` to run it. .. code-block:: python # We use `shell` task to run `echo-ten.sh` file shell_task = Shell( name="run", command=f"bash {file_name}", resource_list=[ file_name ], ) During workflow running, the file would be downloaded to the task runtime working directory which mean you could execute them. We execute file by ``bash`` but reference it by name directly. Please notice that we could also reference the resource file already in dolphinscheduler resource center, which mean we could use resource center files in task by name without upload it again. And we can upload files to resource center bare without workflow. .. code-block:: python # Upload file to resource center from pydolphinscheduler.core.resource import Resource resource = Resource(name="bare-create.py", user_name="", content="print('Bareh create resource')") resource.create_or_update_resource() After that, we could see new file named ``bare-create.py`` is be created in resource center. .. note:: Both parameter ``resource_list`` in workflow and task is list of string which mean you could upload and reference multiple files. For more complex usage, you could read :doc:`howto/multi-resources`. Local Parameters ---------------- In DolphinScheduler, we can define parameter in task, aka Local Parameter. We can set parameters to variables in tasks to better manage our tasks. For example: .. literalinclude:: ../../src/pydolphinscheduler/examples/local_parameter_example.py :start-after: [start parameter example] :end-before: [end parameter example] :language: python There are two ways to define local parameters: .. literalinclude:: ../../src/pydolphinscheduler/examples/local_parameter_example.py :start-after: [start parameter define] :end-before: [end parameter define] :language: python Full example: .. literalinclude:: ../../src/pydolphinscheduler/examples/local_parameter_example.py :start-after: [start workflow_declare] :end-before: [end workflow_declare] :language: python Authentication Token -------------------- pydolphinscheduler use token as authentication when communication with dolphinscheduler server, and we have a default auth token to make it out-of-box. For security reason, we highly recommend you to change your own auth token when you deploy in production environment or test dolphinscheduler in public network. The auth token keyword in ``auth_token`` and it can be set in multiple ways which you can read :doc:`config` section for more detail.