Here's an example of how to emit an event from a Prefect task:

python
from prefect import task
from prefect.events.utilities import emit_event
from datetime import datetime

@task
def process_data():
    # Your task logic here
    
    # Emit an event
    emit_event(
        event="data.processed",  # Name of your event
        resource={
            "prefect.resource.id": "my-task",  # Unique identifier for the resource
            "prefect.resource.name": "process_data_task",  # Name of the resource
        },
        payload={
            "records_processed": 100,
            "status": "success"
        }
    )
    
    return "Processing complete"

# Alternative example with more details
@task
def analyze_data():
    # Your analysis logic here
    
    emit_event(
        event="data.analyzed",
        resource={
            "prefect.resource.id": "analysis-task-001",
            "prefect.resource.name": "data_analyzer",
        },
        occurred=datetime.utcnow(),  # Explicitly set the occurrence time
        related=[
            {
                "prefect.resource.id": "dataset-123",
                "prefect.resource.name": "customer_dataset"
            }
        ],
        payload={
            "analysis_type": "customer_segmentation",
            "segments_found": 5,
            "total_records": 1000
        }
    )
    
    return "Analysis complete"

Key points about emitting events: 1. The

event

parameter should follow a dot-notation naming convention (e.g., "data.processed", "user.created") 2. The

resource

dictionary must contain at least: - `prefect.resource.id`: A unique identifier for the resource - `prefect.resource.name`: A human-readable name for the resource 3. The

payload

can contain any JSON-serializable data relevant to the event 4.

related

resources can be specified to link the event to other resources in your system 5. If you don't specify

occurred

, it defaults to the current time Events can be useful for: - Tracking important milestones in your data pipeline - Triggering downstream processes - Monitoring and observability - Creating audit trails You can find more details about events in the Prefect documentation.

Here's the correct code example for Prefect 2.19:

python
from prefect import task
from prefect.events import emit_event
from datetime import datetime

@task
def process_data():
    # Your task logic here
    
    # Emit an event
    emit_event(
        event="data.processed",  # Name of your event
        resource={
            "prefect.resource.id": "my-task"  # Unique identifier for the resource
        },
        payload={
            "records_processed": 100,
            "status": "success"
        }
    )
    
    return "Processing complete"

# Alternative example with more details
@task
def analyze_data():
    # Your analysis logic here
    
    emit_event(
        event="data.analyzed",
        resource={
            "prefect.resource.id": "analysis-task-001"
        },
        occurred=datetime.utcnow(),  # Explicitly set the occurrence time
        related=[
            {
                "prefect.resource.id": "dataset-123"
            }
        ],
        payload={
            "analysis_type": "customer_segmentation",
            "segments_found": 5,
            "total_records": 1000
        }
    )
    
    return "Analysis complete"

Key differences from Prefect 3.x: 1. The import is from

prefect.events

instead of

prefect.events.utilities

2. The

resource

dictionary only requires the

prefect.resource.id

field (the

name

field is not required) 3. Similarly, related resources only need the

prefect.resource.id

Events in Prefect 2.19 can be used to: - Track important milestones in your pipeline - Enable event-driven automation in Prefect Cloud - Create audit trails for your workflows - Trigger downstream processes based on task completion or other events You can find more details about events in the Prefect 2.x documentation.

Here's how to properly set resource IDs in your events, with examples showing different approaches:

python
from prefect import task, get_run_logger
from prefect.events import emit_event
import uuid

@task
def process_data(dataset_name: str):
    logger = get_run_logger()
    
    # Option 1: Using task name and a unique identifier
    resource_id = f"task.process_data.{uuid.uuid4()}"
    
    # Option 2: Using dataset name as part of the identifier
    resource_id_2 = f"dataset.{dataset_name}.processor"
    
    # Option 3: Using multiple components
    resource_id_3 = f"data.processing.{dataset_name}.{uuid.uuid4()}"
    
    emit_event(
        event="data.processed",
        resource={
            "prefect.resource.id": resource_id
        },
        payload={
            "dataset": dataset_name,
            "status": "complete"
        }
    )
    
    <http://logger.info|logger.info>(f"Emitted event with resource ID: {resource_id}")
    return "Processing complete"

# Example with multiple related resources
@task
def transform_data(input_dataset: str, output_dataset: str):
    # Main resource ID for the transformation
    main_resource_id = f"transform.{input_dataset}_to_{output_dataset}"
    
    emit_event(
        event="data.transformed",
        resource={
            "prefect.resource.id": main_resource_id
        },
        related=[
            {
                "prefect.resource.id": f"dataset.{input_dataset}"
            },
            {
                "prefect.resource.id": f"dataset.{output_dataset}"
            }
        ],
        payload={
            "input_dataset": input_dataset,
            "output_dataset": output_dataset
        }
    )

Best practices for setting resource IDs: 1. Use Consistent Patterns

python
   # Pattern: category.subcategory.identifier
   resource_id = "task.data_processing.daily_sales"
   resource_id = "flow.etl.customer_data"

2. Include Meaningful Information

python
   # Include relevant identifiers
   resource_id = f"processor.{dataset_name}.{process_type}"

3. Ensure Uniqueness When Needed

python
   # Combine static and dynamic elements
   resource_id = f"task.processor.{dataset_name}.{uuid.uuid4()}"

4. Hierarchical Structure

python
   # Use dots to create hierarchy
   resource_id = f"department.{dept_name}.process.{process_id}"

5. Keep IDs Stable for the Same Resource

python
   # Use stable identifiers when the resource is permanent
   resource_id = f"daily_report.{report_date}"

Key points to remember: - Resource IDs should be unique within your workspace - Use dot notation to create hierarchical structure - Make IDs descriptive and meaningful - Include stable identifiers when possible - Add unique identifiers (like UUIDs) when needed for one-time events - Keep the format consistent across your application The resource ID structure helps in: - Tracking event sources - Filtering and querying events - Organizing events hierarchically - Maintaining relationships between different resources - Creating meaningful automation rules Remember that resource IDs are used to track the source of events, so they should be chosen carefully to make event monitoring and debugging easier.