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! 🔍