Based on the research and reported issues, I can provide some insights about your situation: 1. Database Communication Changes While Prefect 3.x doesn't inherently communicate "more" with PostgreSQL compared to 2.x, there are architectural differences that can affect database interaction patterns: - Prefect 3.x has a different architecture around work pools and workers (replacing the agent system from 2.x) - The database schema and interaction patterns have been optimized for better scalability - PostgreSQL is more explicitly recommended as the production database in 3.x 2. Hanging Flow Runs Issue The "Running" status issue you're experiencing is a known challenge, particularly when dealing with: - Large numbers of concurrent tasks - High-frequency database operations - Connection pool exhaustion Several factors could be contributing to your hanging flows: a) Connection Pool Timeouts - Flow runs can hang when the connection pool to the PostgreSQL database becomes exhausted - This is more likely to occur with large numbers of concurrent tasks or rapid task creation b) Resource Constraints - Ensure your PostgreSQL instance has adequate resources (CPU, memory, connections) - Check the

max_connections

setting in PostgreSQL - Monitor for connection pool exhaustion 3. Recommendations to Address the Issues a) Database Optimization

sql
-- Check and potentially increase max_connections
ALTER SYSTEM SET max_connections = '200';  -- adjust based on your needs

-- Enable the pg_trgm extension if not already enabled
CREATE EXTENSION IF NOT EXISTS pg_trgm;

b) Application Configuration - Consider implementing batching for task creation - Add appropriate timeouts in your flow configurations - Monitor and potentially adjust the connection pool size c) Infrastructure Considerations - Ensure your PostgreSQL instance is properly sized for your workload - Consider implementing connection pooling (like PgBouncer) if dealing with many concurrent connections - Monitor database performance metrics (connections, query times, etc.) 4. Best Practices for Large-Scale Flows - Break large flows into smaller, more manageable subflows - Implement proper error handling and retry mechanisms - Use batching when dealing with large numbers of concurrent tasks - Consider using work pools with multiple workers to distribute the load If you continue experiencing issues, you might want to: 1. Monitor PostgreSQL logs for connection-related issues 2. Check the Prefect server logs for any timeout or connection pool errors 3. Consider implementing a connection pooler like PgBouncer if you're dealing with many concurrent connections 4. Review your flow architecture to ensure it's optimized for your scale For additional support, you can also check out the Prefect documentation on server deployment and database configuration.

Based on the research and issue analysis, I can explain why this issue is more prevalent in Prefect 3.x compared to 2.x: 1. Architectural Changes in State Management - Prefect 3.x introduced a new state management system that is more aggressive in tracking task and flow states - The new system maintains more active connections to the database to track state changes - This can lead to more frequent connection pool exhaustion, especially with large numbers of concurrent tasks 2. Worker vs Agent Architecture - In Prefect 2.x, agents were simpler and had less responsibility for state management - Prefect 3.x workers are more sophisticated but also maintain more state information - This increased state tracking can lead to situations where flows appear to "hang" in the Running state 3. Connection Pool Management - Prefect 3.x uses a different connection pooling strategy that can be more susceptible to exhaustion - When many tasks are running concurrently, the system can hit connection limits more easily - This is particularly evident when using task runners like DaskTaskRunner with many concurrent tasks To mitigate these issues in Prefect 3.x, you can: 1. Adjust Database Connection Settings

python
# In your PostgreSQL configuration:
max_connections = 500  # Adjust based on your needs

2. Implement Task Batching

python
from prefect import flow, task
from prefect.tasks import task_input_hash

@task(cache_key_fn=task_input_hash)
def process_batch(items):
    # Process a batch of items instead of individual tasks
    pass

@flow
def main_flow():
    # Break large number of tasks into batches
    batches = [items[i:i+100] for i in range(0, len(items), 100)]
    for batch in batches:
        process_batch.submit(batch)

3. Configure Worker Settings

python
# When starting your worker, adjust concurrency limits:
prefect worker start -p 'your-pool' --limit-concurrent-runs 50

4. Monitor and Reset If you notice flows hanging in the "Running" state: - Check the worker logs for connection pool errors - Consider implementing health checks to restart workers if they become unresponsive - Use the Prefect API to manually reset stuck flows if necessary The key difference is that Prefect 3.x's more sophisticated state management system requires more careful configuration and monitoring, especially in high-concurrency scenarios. While this provides better visibility and control, it can also lead to these hanging issues if not properly configured.

Based on the research and practical experience, a db.t4g.small instance (2 vCPU, 2GB RAM) is likely insufficient for Prefect 3.x in a production environment. Here's why: 1. Increased Database Load in 3.x - Prefect 3.x maintains more active database connections - The state tracking system is more sophisticated and requires more database resources - More frequent database operations for task and flow state management 2. db.t4g.small Limitations - Only 2GB of RAM, which can be insufficient for connection pooling - Uses burstable CPU credits, which can be depleted during peak workloads - Limited IOPS which can cause performance bottlenecks 3. Minimum Recommended Specifications For Prefect 3.x in production, you should consider at least: - db.t4g.medium (2 vCPU, 4GB RAM) for small deployments - db.t4g.large (2 vCPU, 8GB RAM) for medium deployments - db.r6g.large (2 vCPU, 16GB RAM) for larger deployments 4. Key PostgreSQL Settings for Prefect 3.x

sql
-- Recommended minimum settings for Prefect 3.x
max_connections = 100
shared_buffers = '1GB'
work_mem = '64MB'
maintenance_work_mem = '256MB'
effective_cache_size = '3GB'

5. Signs You Need to Upgrade If you're seeing any of these with db.t4g.small: - Flows hanging in "Running" state - Database connection timeouts - Slow query performance - High CPU credit utilization - Memory pressure alerts To monitor if your database is undersized, watch for: - High CPU utilization - Memory constraints - IOPS throttling - Connection pool exhaustion - Slow query logs The reason it worked well in Prefect 2.x but struggles with 3.x is due to the architectural changes in how Prefect manages state and connections. Prefect 3.x's more robust state management system requires more database resources to maintain optimal performance. If you need to stay on db.t4g.small for cost reasons, consider: 1. Implementing aggressive task batching 2. Reducing concurrent flow runs 3. Implementing shorter retention periods for flow run history 4. Using connection pooling (like PgBouncer) However, the most straightforward solution would be to upgrade to at least a db.t4g.medium instance to ensure stable operation with Prefect 3.x.