Based on the research and issue review, here's what's likely happening with your Kubernetes workers and how to fix it: The issue where one worker takes all flows typically occurs due to one of these reasons: 1. Work Pool Configuration - Make sure all three workers are configured to poll from the same work pool - Verify that each worker has the same work pool configuration (polling interval, concurrency limits) 2. Worker Priority - Check if the worker taking all flows has a higher priority setting than the others - By default, workers have equal priority, but this can be modified in the configuration 3. Worker Health/Status - Ensure all workers are actually healthy and running - Use

prefect worker ls

to verify the status of all workers - Check Kubernetes logs for any errors in the other workers Here's how to troubleshoot: 1. First, verify all workers are actually running and connected:

bash
prefect worker ls

2. Check your worker configurations to ensure they're identical:

yaml
# worker-config.yaml
prefect:
  worker:
    type: "kubernetes"
    work_pool_name: "your-work-pool"  # Should be identical for all workers
    prefetch_seconds: 60              # Should be consistent across workers
    polling_interval: 30              # Should be consistent across workers

3. Monitor worker logs in Kubernetes:

bash
kubectl logs -f deployment/prefect-worker

If you're still seeing uneven distribution after confirming these settings, you might want to try: 1. Adjusting the polling interval to give other workers a better chance at picking up work 2. Setting explicit concurrency limits on the workers 3. Restarting the workers to ensure they're all in a clean state Remember that in Prefect 3.x, workers use a "pull" model where they actively request work from the work pool. If one worker is consistently faster at pulling work, it might appear to be "taking all the flows." Would you like me to provide more specific details about any of these aspects or help troubleshoot a particular configuration?

I need to correct my understanding - if you're using

deployment serve

in containers without work pools, this is actually an unsupported configuration for load balancing in Prefect 3.x. For proper load balancing across multiple containers, you should be using work pools. The

deployment serve

command is primarily intended for development and testing, not for production deployments with multiple containers. Here's what you should do instead: 1. Convert your setup to use work pools with the Docker or Kubernetes infrastructure:

bash
prefect work-pool create my-pool --type kubernetes
# or for Docker
prefect work-pool create my-pool --type docker

2. Deploy your flows to the work pool:

python
flow.deploy(
    name="my-deployment",
    work_pool_name="my-pool",
    # other configuration as needed
)

3. Run workers to execute the flows:

bash
prefect worker start -p "my-pool"

This will give you proper load balancing across your containers, as work pools are designed to handle distributed workloads effectively. The reason one container is taking all the flows in your current setup is that

deployment serve

instances don't coordinate with each other - each one independently tries to run all deployments it's configured for. This can lead to race conditions and duplicate executions. Would you like me to provide more specific guidance on migrating your current setup to use work pools?

Yes, the subprocess worker can be used in production, but there are some important considerations: 1. Advantages of Subprocess Worker: - Lightweight and simple to set up - Good for running flows in isolated environments - Doesn't require additional infrastructure like Docker or Kubernetes - Built-in process isolation for each flow run 2. Limitations and Considerations: - Each flow run creates a new Python subprocess - Resource management is handled by the host system - No built-in container isolation - Memory and CPU limits need to be managed at the system level - Not as scalable as container-based solutions 3. Best Practices for Production Use:

python
   from prefect import flow
   from prefect.worker import Worker
   
   # Configure the worker with appropriate settings
   worker = Worker(
       work_pool_name="my-pool",
       type="subprocess",
       # Set reasonable limits
       work_queue_name="default",
       prefetch_seconds=60,
       # Enable heartbeat for monitoring
       heartbeat_interval=30
   )

4. When to Use Subprocess Workers: - Small to medium-scale deployments - When container overhead is not desired - Development and testing environments - Simple production workloads - When flows don't require complex isolation 5. When to Consider Alternatives: - If you need container isolation - For highly scalable deployments - When running untrusted code - If you need fine-grained resource controls - For complex microservices architectures For your specific case, if you're running in production, I'd recommend: 1. Set up proper monitoring:

bash
prefect worker start -p "your-pool" -t subprocess --log-level DEBUG

2. Configure resource limits at the system level (e.g., using systemd or similar) 3. Consider implementing health checks:

python
@flow
def your_flow():
    # Add health check tasks
    check_system_resources()
    # Your flow logic here

Would you like more specific guidance about any of these aspects or help with a particular production setup?

Here's a complete example of deploying flows with subprocess workers: 1. First, create your flow file (

my_flows.py

):

python
from prefect import flow, task
from datetime import timedelta

@task
def process_data(data):
    return f"Processed {data}"

@flow(retries=3, retry_delay_seconds=30)
def my_etl_flow(input_data: str = "default"):
    result = process_data(input_data)
    return result

2. Create a deployment script (

deploy.py

):

python
from prefect.deployments import Deployment
from prefect.server.schemas.schedules import CronSchedule
from my_flows import my_etl_flow

# Create deployment
deployment = Deployment.build_from_flow(
    flow=my_etl_flow,
    name="subprocess-example",
    version="1",
    work_pool_name="subprocess-pool",
    work_queue_name="default",
    schedule=(CronSchedule(cron="0 0 * * *")),  # Run daily at midnight
)

if __name__ == "__main__":
    deployment.apply()

3. Set up your work pool:

bash
# Create a subprocess work pool
prefect work-pool create subprocess-pool --type subprocess

# Start the worker
prefect worker start --pool subprocess-pool

4. Deploy and run:

bash
# Deploy your flow
python deploy.py

# Run the deployment
prefect deployment run my-etl-flow/subprocess-example

5. For production settings, create a more robust worker configuration (

worker_config.py

):

python
from prefect.worker import Worker
from prefect.utilities.asyncutils import sync_compatible
import asyncio

@sync_compatible
async def start_production_worker():
    worker = Worker(
        work_pool_name="subprocess-pool",
        type="subprocess",
        prefetch_seconds=60,        # How far ahead to prefetch scheduled runs
        heartbeat_interval=30,      # Worker health check interval
        daemon=True,               # Run as daemon process
        limit=100,                 # Maximum concurrent flow runs
    )
    
    # Start the worker
    await worker.start()

if __name__ == "__main__":
    asyncio.run(start_production_worker())

6. For system service deployment, create a systemd service file (

/etc/systemd/system/prefect-worker.service

):

ini
[Unit]
Description=Prefect Subprocess Worker
After=network.target

[Service]
Type=simple
User=prefect
Group=prefect
WorkingDirectory=/opt/prefect
Environment=PREFECT_API_URL=<http://your-prefect-server:4200/api>
ExecStart=/usr/local/bin/prefect worker start --pool subprocess-pool
Restart=always
RestartSec=3

[Install]
WantedBy=multi-user.target

7. Production monitoring setup (

monitoring.py

):

python
from prefect import flow, task
import psutil
import logging

@task
def check_system_resources():
    cpu_percent = psutil.cpu_percent()
    memory_percent = psutil.virtual_memory().percent
    
    if cpu_percent > 80 or memory_percent > 80:
        logging.warning(f"High resource usage: CPU {cpu_percent}%, Memory {memory_percent}%")
    
    return {"cpu": cpu_percent, "memory": memory_percent}

@flow(retries=3)
def monitored_flow(name: str):
    # Check resources before running
    resources = check_system_resources()
    
    # Your flow logic here
    result = f"Processing {name}"
    
    # Check resources after running
    final_resources = check_system_resources()
    
    return {
        "result": result,
        "initial_resources": resources,
        "final_resources": final_resources
    }

8. Error handling and logging configuration: ```python from prefect import flow from prefect.logging import get_run_logger import sys @flow(retries=3, retry_delay_seconds=30) def robust_flow():