Based on the research, I'll provide you with a comprehensive guide on integrating observability into your Prefect flows. Here are the key approaches: 1. Built-in Logging

python
from prefect import flow, task

@task(log_prints=True)  # Captures print statements as logs
def my_task():
    print("This will be captured in Prefect logs")

@flow(log_prints=True)
def my_flow():
    print("Flow-level logging")
    my_task()

2. Custom Logging You can configure custom logging handlers to send logs to external systems:

python
import logging
from prefect import flow

# Configure custom logger
custom_handler = logging.StreamHandler()
custom_handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))

@flow
def my_flow():
    logger = get_run_logger()  # Get Prefect logger
    logger.addHandler(custom_handler)
    <http://logger.info|logger.info>("This will go to both Prefect and custom handler")

3. Metrics and Events - Prefect automatically tracks various metrics like flow run duration, task run counts, and state transitions - You can view these metrics in the Prefect UI - You can set up notifications based on these metrics using the UI or programmatically 4. Best Practices for Observability: - Use meaningful flow and task names for better tracking - Implement appropriate logging levels (DEBUG, INFO, WARNING, ERROR) - Set up notifications for critical state changes - Use tags to organize and filter flows - Leverage flow run metadata for custom metrics 5. Monitoring Through Prefect UI - Access detailed logs for each flow and task run - View flow run history and performance metrics - Monitor infrastructure health and dependencies - Track state changes and transitions 6. Integration with External Tools You can integrate Prefect with various observability platforms: - Send logs to ElasticSearch or Splunk - Export metrics to Prometheus - Set up custom webhooks for notifications - Integrate with APM tools Here's an example combining several observability features:

python
from prefect import flow, task, get_run_logger
from datetime import timedelta

@task(
    retries=3,
    retry_delay_seconds=60,
    log_prints=True,
    tags=["critical"]
)
def monitored_task():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Starting critical operation")
    # Your task logic here
    logger.debug("Operation details...")

@flow(
    log_prints=True,
    timeout_seconds=3600,  # Set timeout for monitoring
    description="Flow with enhanced observability"
)
def observable_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Flow started")
    monitored_task()

For more detailed information, you can refer to: - Troubleshooting Guide - Prefect Metrics Changelog Remember that Prefect Cloud provides additional observability features like: - Global flow run tracking - Team-wide analytics - Advanced notification settings - SLA monitoring - Infrastructure health metrics Would you like me to elaborate on any specific aspect of observability in Prefect, such as setting up specific integrations or configuring advanced monitoring features?