Based on the research and reported issues, there are several potential causes and solutions for your UI performance issues: 1. Database Load - The UI's performance is often tied to database query performance - Consider adjusting the database connection pool settings in your configuration:

yaml
     server:
       database:
         sqlalchemy:
           pool_size: 60  # Adjust based on your needs
           max_overflow: 10

2. Time Range Impact - The UI defaults to showing 7 days of flow runs, which can be excessive if you have many runs - Reduce the time range when viewing flow runs to improve performance - Use filters to limit the number of runs being displayed 3. Server Configuration - Ensure your server has adequate resources (CPU/memory) - Consider adjusting these server settings:

yaml
     server:
       api:
         default_limit: 500  # Adjust this lower if needed
       flow_run_graph:
         max_nodes: 10000
         max_artifacts: 10000

4. Optimization Recommendations: - Use tags to organize and filter your flows more efficiently - Implement pagination when viewing large sets of flow runs - Consider cleaning up old flow runs that are no longer needed - If possible, host the Prefect server closer to where you're accessing the UI to reduce network latency 5. Database Maintenance - Regularly clean up old flow runs and task runs - Consider implementing a retention policy for your flow run history - Ensure your database is properly indexed If these solutions don't help, you might want to check: - The server logs for any specific errors or warnings - The database performance metrics (if you're using a managed database) - Network latency between your browser and the Prefect server Would you like me to provide more specific details about any of these areas?

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Since you're accessing the UI on a remote machine, here are the most likely causes and solutions for the performance issues: 1. Network Latency - The connection between your local machine and the remote server could be causing delays - Try accessing the UI through SSH tunneling with port forwarding for better performance:

bash
     ssh -L 4200:localhost:4200 your-remote-machine

Then access the UI at

<http://localhost:4200>

2. Browser Resource Usage - The UI defaults to showing 7 days of flow runs, which can be resource-intensive - Reduce the time range when viewing flow runs (e.g., last 24 hours) - Use filters to limit the number of runs being displayed - Try clearing your browser cache or using an incognito window 3. Server Resources - Check if the remote machine has sufficient resources:

bash
     top  # Check CPU usage
     free -h  # Check memory usage
     df -h  # Check disk space

- Consider increasing the resources allocated to the Prefect server 4. Database Configuration If you're using SQLite (the default): - Consider switching to PostgreSQL for better performance - Regularly clean up old flow runs to prevent database bloat - Implement a retention policy for your flow run history 5. Server Configuration Optimizations Add these settings to your Prefect configuration:

yaml
   server:
     api:
       default_limit: 200  # Reduce from default 500
     database:
       sqlalchemy:
         pool_size: 20
         max_overflow: 5
     flow_run_graph:
       max_nodes: 5000  # Reduce if you don't need large graphs

Would you like me to provide more specific guidance about any of these areas? For example, I can provide detailed steps for setting up SSH tunneling or implementing a specific optimization.

Based on the research and reported issues, this ReadTimeout error typically occurs when there's a communication issue between your flow and the Prefect server. Here are the likely causes and solutions: 1. API Request Timeout Settings - The default timeout might be too short for your network conditions - Add this to your Prefect configuration:

yaml
     api:
       request_timeout: 300  # Increase from default 60 seconds
     client:
       max_retries: 5

2. Network Connectivity - Since you're running on a remote machine, network latency or instability could be causing the timeout - Ensure stable network connection between your flow runner and the Prefect server - Consider using SSH tunneling if network conditions are poor:

bash
     ssh -L 4200:localhost:4200 your-remote-machine

3. Server Resource Constraints - The server might be overwhelmed processing requests - Check server resources:

bash
     top  # Check CPU usage
     free -h  # Check memory usage

- Consider adjusting database connection settings:

yaml
     server:
       database:
         sqlalchemy:
           pool_size: 20
           max_overflow: 5

4. Flow Configuration - Large task results or logs can cause timeouts - Consider using result persistence for large outputs - Implement proper error handling and retries in your flows Would you like me to provide more specific guidance about any of these areas? For example, I can provide detailed steps for configuring timeouts or setting up result persistence.

In the Prefect UI, you can reduce the number of displayed flow runs in several ways: 1. Time Range Filter - Click the calendar icon in the top right of the flow runs page - Select a shorter time range (e.g., "Last 24 hours" instead of "Last 7 days") - You can also set a custom time range for more precise filtering 2. State Filters - Use the "State" dropdown to filter by run states - Common filters include: - Completed - Failed - Running - Scheduled - You can select multiple states at once 3. Tag Filters - If your flows use tags, click the "Tags" dropdown - Select specific tags to show only flows with those tags - This is especially useful if you organize your flows with meaningful tags 4. Flow Name Filter - Use the search bar to filter by flow name - This supports partial matches 5. Deployment Filter - Use the "Deployment" dropdown to show runs from specific deployments only 6. Advanced Filters - Click "Add Filter" for additional options like: - Flow ID - Deployment ID - Work Queue - Work Pool - Start/End time - Duration Best Practices: - Use tags consistently in your flows for easier filtering - Set a reasonable time range as your default view - Combine multiple filters for more precise results - Consider cleaning up old flow runs that are no longer needed Would you like more specific details about any of these filtering methods?