workflow_dag

workflow_dag(workflow: Workflow, title: str = "Workflow Dependency Graph") -> Dict

The workflow_dag function renders a Directed Acyclic Graph (DAG) visualization for a given workflow. This helps you visualize dependencies, execution status, and relationships within the workflow, making it easier to understand and debug complex processes.

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Parameters

• workflow (Workflow): A workflow object representing the process or pipeline to be visualized.
• title (str): (Optional) The title displayed above the visualization. Defaults to "Workflow Dependency Graph".

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Returns

• Dict: A component containing the DAG visualization data with node information.

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Node Information

Each node in the DAG contains the following information:

• name: The name of the task/node
• status: Current execution status
• execution_time: Time taken for execution
• attempts: Number of execution attempts
• error: Any error information if failed
• dependencies: List of dependent tasks
• force_recompute: Whether the task is marked for recomputation

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Usage Example

Here’s an example of how to visualize a workflow using workflow_dag:

from preswald import workflow_dag, Workflow

# Create a workflow object
workflow = Workflow()

# Add some tasks to the workflow
@workflow.task()
def task_a():
    return "A"

@workflow.task(depends_on=[task_a])
def task_b():
    return "B"

# Render the workflow DAG
workflow_dag(workflow, title="Example Workflow")

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Key Features

1. Rich Node Data: Each node shows detailed execution information
2. Dependency Visualization: Clear representation of task dependencies
3. Status Tracking: Visual indicators for task status (success/failure/pending)
4. Performance Insights: Shows execution time and attempt counts
5. Error Visibility: Displays error information for failed tasks

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Common Use Cases

Workflow DAGs are essential for:

• Debugging complex workflows
• Monitoring task execution
• Understanding dependency chains
• Identifying bottlenecks
• Tracking execution progress
• Visualizing pipeline structure

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Why Use workflow_dag?

The workflow DAG visualization helps you:

• Understand complex task relationships
• Monitor workflow execution status
• Debug failed tasks
• Optimize workflow structure
• Share workflow insights with team members

Perfect for both development and production monitoring of your workflow pipelines! 🔍