Josix is Only Joking

Aider 應用場景與 Airflow TaskFlow API 筆記

December 17, 2024

沒什麼組織、一些倏忽即逝有趣或不有趣的想法

  • Aider use case sharing

    • Use the doc from Aider to build slides from Aider
      • e.g. generate a slides elaborate on what is aider, its main features, saperate and illustrate the commands we could use in different usage group in marp
      • Aider in IDE
        • // XXX AI!, // XXX AI?
      • Commit/ Shell Integration
      • It event can change color theme
    • Use Aider to understand Airflow’s codebase
      • /architect what is the main components of airflow, show the class diagram in mermaidjs with their responsibility and interaction relationship
      • /architect show the sequence diagram of how OpenAITriggerBatchOperator works in mermaidjs
      • /architect show the sequence diagram of how deferrable operator works in mermaidjs
    • Use Aider to add unit tests
      • e.g. generate unit tests for the functionality of XXX with the coding style of YYY
    • Use Aider to consolidate the ideas, let Aider be your real second brain to help you think, recall and organize your thoughts
      • e.g. generate the slides for the design of pollm according to the concepts about why we need llm translation, Aider, and the pollm design (with added all the random-thoughts notes)
    • Use Aider to do inline translation for po files
      • # 將這句翻譯成中文 AI!

  • How TaskFlow API works in Airflow

    • Overview provided by Aider:

      1. Decorator Creation Phase

        @task
def my_function():
    pass
      • TaskDecoratorCollection.__call__ routes to python_task
      • task_decorator_factory creates a _TaskDecorator instance
      • Wraps the original function with task configuration

      1. Task Definition Phase

        result = my_function()
      • Creates _PythonDecoratedOperator instance
      • Sets up task parameters and configuration
      • Returns XComArg for dependency management
      1. Execution Phase
        # When DAG runs
ti = TaskInstance(task)
ti.execute(context)

      • Executes the wrapped Python function

      • Handles result pushing to XCom

      • Manages task state and dependencies

        Key Components:

      • TaskDecoratorCollection: Entry point for @task decorator

      • _TaskDecorator: Handles task creation and configuration

      • _PythonDecoratedOperator: Executes the actual Python function

      • XComArg: Manages task dependencies and results

        The architecture allows for:

      • Clean separation between definition and execution

      • Flexible task configuration

      • Integration with Airflow’s DAG system

      • XCom-based data passing

      • Task mapping and dynamic task generation

      Class Diagram:

      classDiagram
    class TaskDecoratorCollection {
        +python: staticmethod
        +__call__: Any
        +__getattr__(name): TaskDecorator
    }

    class TaskDecorator {
        <<interface>>
        +__call__(python_callable): Task
        +__call__(**kwargs): Callable
    }

    class _TaskDecorator {
        +function: Callable
        +operator_class: type[BaseOperator]
        +multiple_outputs: bool
        +kwargs: dict
        +__call__(*args, **kwargs): XComArg
        +expand(**kwargs): XComArg
        +expand_kwargs(kwargs): XComArg
        +partial(**kwargs): _TaskDecorator
        +override(**kwargs): _TaskDecorator
    }

    class BaseOperator {
        +task_id: str
        +dag: DAG
        +execute(context): Any
    }

    class DecoratedOperator {
        +python_callable: Callable
        +op_kwargs: dict
        +op_args: list
        +execute(context): Any
        +_handle_output(return_value, context)
    }

    class PythonOperator {
        +execute(context): Any
    }

    class _PythonDecoratedOperator {
        +template_fields: Sequence[str]
        +custom_operator_name: str
        +__init__(python_callable, op_args, op_kwargs, **kwargs)
    }

    class XComArg {
        +operator: BaseOperator
        +key: str
    }

    TaskDecoratorCollection ..> TaskDecorator
    TaskDecorator ..> _TaskDecorator
    BaseOperator <|-- DecoratedOperator
    BaseOperator <|-- PythonOperator
    DecoratedOperator <|-- _PythonDecoratedOperator
    PythonOperator <|-- _PythonDecoratedOperator
    _TaskDecorator ..> _PythonDecoratedOperator
    _PythonDecoratedOperator ..> XComArg

      Sequence Diagram:

      sequenceDiagram
    participant User
    participant task as @task
    participant TaskDecFactory as task_decorator_factory
    participant TaskDecorator as _TaskDecorator
    participant PythonOp as _PythonDecoratedOperator
    participant XCom
    
    Note over User: Definition Time
    User->>task: @task(task_id="my_task")
    activate task
    task->>TaskDecFactory: task_decorator_factory(python_callable=None, **kwargs)
    TaskDecFactory->>TaskDecorator: create _TaskDecorator(multiple_outputs, operator_class, kwargs)
    TaskDecorator-->>User: return decorator
    deactivate task
    
    Note over User: Function Definition
    User->>TaskDecorator: decorator(python_function)
    activate TaskDecorator
    TaskDecorator-->>User: return _TaskDecorator instance
    deactivate TaskDecorator
    
    Note over User: Execution Time
    User->>TaskDecorator: decorated_function(*args, **kwargs)
    activate TaskDecorator
    TaskDecorator->>PythonOp: create operator(python_callable, op_args, op_kwargs)
    PythonOp->>PythonOp: prepare_for_execution()
    PythonOp->>XCom: create XComArg(operator)
    XCom-->>TaskDecorator: return XComArg
    TaskDecorator-->>User: return XComArg
    deactivate TaskDecorator
    
    Note over User: DAG Runtime
    User->>PythonOp: execute(context)
    activate PythonOp
    PythonOp->>PythonOp: _execute_task_with_callbacks(context)
    PythonOp->>PythonOp: python_callable(*op_args, **op_kwargs)
    PythonOp->>XCom: xcom_push(result)
    PythonOp-->>User: return result
    deactivate PythonOp
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