Adam
06/29/2025, 3:55 PMMarvin
06/29/2025, 3:55 PMMarvin
06/29/2025, 3:56 PMmax_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.Adam
06/29/2025, 3:59 PMMarvin
06/29/2025, 3:59 PMMarvin
06/29/2025, 4:00 PMpython
# 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.Adam
06/29/2025, 4:23 PMMarvin
06/29/2025, 4:23 PMMarvin
06/29/2025, 4:24 PMsql
-- 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.