Here are the key recommendations to stabilize your server: 1. Implement Concurrency Controls:

bash
   # Create a global concurrency limit
   prefect gcl create "api-calls" --slot-count=10  # Adjust number based on your EC2 capacity

This will limit the number of concurrent flow runs to prevent overwhelming your server. 2. EC2 Instance Configuration: - Ensure your EC2 instance has adequate resources: - Use an appropriate instance type with sufficient CPU and RAM - Consider using t3.medium or larger for production workloads - Configure proper auto-scaling groups if needed - Set up proper monitoring with CloudWatch 3. Infrastructure Best Practices: - Implement proper security groups to manage incoming traffic - Ensure your EC2 instance has the necessary IAM roles and permissions - Set up proper networking with internet gateway access - Consider using an Application Load Balancer for better request distribution 4. Application-Level Optimizations: - Implement retry logic in your API calls with exponential backoff - Add error handling for failed flow run creations - Consider batching flow run creations instead of creating them individually - Use async operations where possible to improve performance 5. Monitoring and Logging: - Set up CloudWatch metrics to monitor: - CPU utilization - Memory usage - Network I/O - Configure alerts for when resource usage exceeds certain thresholds If you're still experiencing issues after implementing these suggestions, it would be helpful to know: 1. What size EC2 instance are you using? 2. How many concurrent API calls are you typically making? 3. What's the average resource utilization before the server goes down? This information would help me provide more specific recommendations for your use case. Also, consider using Prefect Cloud instead of self-hosting if you want to avoid managing server infrastructure entirely. It handles all these scaling concerns automatically and provides enterprise-grade reliability. Would you like me to provide more specific details about any of these recommendations?

Here's what you need to address: 1. Immediate Instance Upgrade Required: - T2.medium is a burstable instance with only 2 vCPUs and 4GB RAM - For 1000 requests/minute, recommend upgrading to at least: -

t3.xlarge

(4 vCPUs, 16GB RAM) as a minimum -

c5.2xlarge

(8 vCPUs, 16GB RAM) would be better for consistent performance - Consider

c5.4xlarge

if you need room for growth 2. Concurrency Control Implementation:

bash
# Create a concurrency limit that matches your infrastructure
prefect gcl create "flow-run-creation" --slot-count=50  # Start with 50 and adjust based on performance

3. Architecture Recommendations: - Load Balancer Setup: - Add an Application Load Balancer (ALB) in front of your Prefect server - Configure health checks to monitor server status - Database Considerations: - Move PostgreSQL to a separate RDS instance if you haven't already - Recommend at least db.t3.large for your RDS instance - Auto Scaling:

AWS Architecture:
     Client Requests → ALB → Auto Scaling Group (2+ EC2 instances) → RDS

4. Request Management: - Implement request queuing using Redis or AWS SQS - Example pattern:

python
     from prefect import flow
     from prefect.client import get_client
     import asyncio
     
     async def create_flow_runs_batch(deployment_id, params_list, batch_size=50):
         async with get_client() as client:
             tasks = []
             for i in range(0, len(params_list), batch_size):
                 batch = params_list[i:i + batch_size]
                 tasks.extend([
                     client.create_flow_run(
                         deployment_id=deployment_id,
                         parameters=params
                     ) for params in batch
                 ])
             return await asyncio.gather(*tasks, return_exceptions=True)

5. Critical EC2 Configuration: - Increase the number of allowed file descriptors:

bash
     # Add to /etc/security/limits.conf
     * soft nofile 65536
     * hard nofile 65536

- Tune kernel parameters in `/etc/sysctl.conf`:

net.core.somaxconn = 65536
     net.ipv4.tcp_max_syn_backlog = 65536
     net.core.netdev_max_backlog = 65536

6. Monitoring Setup:

bash
   # CloudWatch metrics to set up
   - CPUUtilization > 75% for 5 minutes
   - MemoryUtilization > 80% for 5 minutes
   - NetworkIn/NetworkOut spikes

7. Cost-Effective Alternative: - Consider using Prefect Cloud instead of self-hosting - It automatically handles this scale of requests and removes infrastructure management overhead - Especially cost-effective when considering the total cost of ownership (infrastructure + maintenance) To implement these changes safely: 1. First, set up the new infrastructure with larger instances 2. Implement concurrency controls 3. Gradually migrate traffic to the new setup 4. Monitor performance and adjust resources as needed Would you like me to: 1. Provide more detailed configuration for any of these components? 2. Share specific CloudWatch monitoring configurations? 3. Help with implementing the batch processing code? Also, I'm curious - are these 1000 requests/minute consistent throughout the day, or do you have specific peak periods? This would affect the auto-scaling strategy I'd recommend.

Let me provide you with a complete example of how to implement batch processing for flow run creation. I'll show you both synchronous and asynchronous approaches: 1. Asynchronous Batch Creation (Recommended for better performance):

python
from prefect import flow
from prefect.client import get_client
import asyncio
from typing import List, Dict, Any

async def create_flow_runs_batch(
    deployment_id: str,
    parameters_list: List[Dict[Any, Any]],
    batch_size: int = 20
) -> List:
    """
    Create flow runs in batches asynchronously.
    
    Args:
        deployment_id: The ID of the deployment to create runs for
        parameters_list: List of parameter dictionaries for each flow run
        batch_size: Number of flow runs to create in each batch
    
    Returns:
        List of created flow run objects
    """
    async with get_client() as client:
        all_results = []
        
        # Process in batches
        for i in range(0, len(parameters_list), batch_size):
            batch = parameters_list[i:i + batch_size]
            
            # Create tasks for this batch
            batch_tasks = []
            for params in batch:
                task = client.create_flow_run(
                    deployment_id=deployment_id,
                    parameters=params,
                    state=None,  # Optional: Set initial state
                    name=f"Batch run - {params.get('run_id', 'unnamed')}"  # Optional: Custom naming
                )
                batch_tasks.append(task)
            
            # Execute batch
            try:
                batch_results = await asyncio.gather(*batch_tasks, return_exceptions=True)
                all_results.extend(batch_results)
                
                # Optional: Add a small delay between batches to prevent overwhelming the server
                await asyncio.sleep(0.5)
                
            except Exception as e:
                print(f"Error in batch {i//batch_size}: {str(e)}")
                
        return all_results

# Example usage in an async context
@flow
async def main_batch_flow():
    # Example parameters list
    params_list = [
        {"param1": f"value_{i}", "run_id": f"run_{i}"} 
        for i in range(100)  # Creating 100 parameter sets
    ]
    
    deployment_id = "your-deployment-id"
    results = await create_flow_runs_batch(
        deployment_id=deployment_id,
        parameters_list=params_list,
        batch_size=20  # Adjust based on your needs
    )
    
    return results

# Run the flow
if __name__ == "__main__":
    asyncio.run(main_batch_flow())

2. Synchronous Version (Simpler but less performant):

python
from prefect import flow
from prefect.client import get_client
from typing import List, Dict, Any

@flow
def create_flow_runs_sync(
    deployment_id: str,
    parameters_list: List[Dict[Any, Any]],
    batch_size: int = 20
) -> List:
    """
    Create flow runs in batches synchronously.
    """
    results = []
    
    with get_client() as client:
        for i in range(0, len(parameters_list), batch_size):
            batch = parameters_list[i:i + batch_size]
            
            for params in batch:
                try:
                    result = client.create_flow_run_sync(
                        deployment_id=deployment_id,
                        parameters=params,
                        name=f"Batch run - {params.get('run_id', 'unnamed')}"
                    )
                    results.append(result)
                except Exception as e:
                    print(f"Error creating flow run: {str(e)}")
                    results.append(None)
            
            # Optional: Add a small delay between batches
            time.sleep(0.5)
    
    return results

3. Best Practices for Batch Processing: - Batch Size: - Start with a batch size of 20-30 - Monitor server performance and adjust accordingly - You can increase if your server handles it well

- Error Handling: - The code above includes basic error handling - Consider adding retry logic for failed runs - Log failures for later investigation - Rate Limiting: - The small sleep between batches helps prevent server overload - Adjust the sleep duration based on your server's performance 4. Server Configuration for Your Load: Given your actual load of ~100 requests/minute: - A t2.medium might actually be sufficient, but t3.large would be more comfortable - Set a global concurrency limit:

bash
prefect gcl create "flow-run-creation" --slot-count=30

Would you like me to: 1. Explain any part of the code in more detail? 2. Show how to add more sophisticated error handling or retry logic? 3. Help with implementing specific parameter patterns for your use case? Also, what kind of parameters are you passing to your flow runs? This might help me provide more specific examples tailored to your use case.

Ah, now I understand - you're receiving webhook-style notifications that you need to process! This is a different pattern from batch processing since you don't control when the requests arrive. Let me help you design a more appropriate solution. Here's how you can handle incoming notifications efficiently: 1. Queue-Based Approach (Recommended):

python
from prefect import flow, task
from prefect.client import get_client
import asyncio
from fastapi import FastAPI, BackgroundTasks
from typing import Dict, Any
import redis  # You'll need to: pip install redis

app = FastAPI()

# Initialize Redis connection (you can also use AWS SQS, RabbitMQ, etc.)
redis_client = redis.Redis(host='localhost', port=6379, decode_responses=True)

@task
async def process_notification(notification_data: Dict[Any, Any]):
    """Process a single notification by creating a flow run"""
    async with get_client() as client:
        try:
            # Create the flow run
            flow_run = await client.create_flow_run(
                deployment_id="your-deployment-id",
                parameters=notification_data,
                name=f"Notification-{notification_data.get('id', 'unknown')}"
            )
            return flow_run
        except Exception as e:
            print(f"Error creating flow run: {str(e)}")
            # You might want to store failed notifications for retry
            redis_client.rpush('failed_notifications', str(notification_data))
            raise

@app.post("/webhook")
async def handle_webhook(
    notification: Dict[Any, Any],
    background_tasks: BackgroundTasks
):
    """Endpoint that receives notifications"""
    try:
        # Option 1: Queue the notification for processing
        redis_client.rpush('notifications_queue', str(notification))
        return {"status": "queued", "message": "Notification accepted"}
        
        # Option 2: Process immediately in background
        # background_tasks.add_task(process_notification, notification)
        # return {"status": "processing", "message": "Notification accepted"}
    except Exception as e:
        return {"status": "error", "message": str(e)}

# Separate worker process to handle queued notifications
@flow
async def process_notification_queue():
    """Process notifications from the queue"""
    while True:
        try:
            # Get notification from queue
            notification_data = redis_client.lpop('notifications_queue')
            if notification_data:
                await process_notification(eval(notification_data))
            else:
                await asyncio.sleep(0.1)  # Prevent tight loop when queue is empty
        except Exception as e:
            print(f"Error processing queue: {str(e)}")
            await asyncio.sleep(1)  # Back off on errors

# Run the queue processor
if __name__ == "__main__":
    asyncio.run(process_notification_queue())

2. Rate Limiting Middleware (Add this to your FastAPI app):

python
from fastapi import Request
import time

class RateLimiter:
    def __init__(self, requests_per_second=50):
        self.requests_per_second = requests_per_second
        self.requests = []
        
    async def check_rate(self):
        now = time.time()
        # Remove old requests
        self.requests = [req_time for req_time in self.requests 
                        if now - req_time < 1.0]
        
        if len(self.requests) >= self.requests_per_second:
            return False
        self.requests.append(now)
        return True

rate_limiter = RateLimiter(requests_per_second=50)  # Adjust as needed

@app.middleware("http")
async def rate_limit_middleware(request: Request, call_next):
    if not await rate_limiter.check_rate():
        return JSONResponse(
            status_code=429,
            content={"error": "Too many requests"}
        )
    return await call_next(request)

3. Configuration for Your EC2 Instance:

For 100 requests/minute (roughly 1.7 requests/second), add these configurations:

bash
# /etc/nginx/nginx.conf (if using Nginx)
http {
    limit_req_zone $binary_remote_addr zone=mylimit:10m rate=120r/m;
    
    server {
        location /webhook {
            limit_req zone=mylimit burst=20 nodelay;
            proxy_pass <http://localhost:8000;>
        }
    }
}

4. Monitoring and Error Recovery:

python
@task
async def monitor_queue_health():
    """Monitor queue length and failed notifications"""
    queue_length = redis_client.llen('notifications_queue')
    failed_count = redis_client.llen('failed_notifications')
    
    # Alert if queue is getting too long
    if queue_length > 1000:  # Adjust threshold as needed
        # Send alert (email, Slack, etc.)
        pass
    
    # Process failed notifications
    if failed_count > 0:
        # Implement retry logic
        pass

@flow(retries=3, retry_delay_seconds=60)
async def queue_monitor_flow():
    """Run monitoring periodically"""
    while True:
        await monitor_queue_health()
        await asyncio.sleep(300)  # Check every 5 minutes

To implement this: 1. Install required packages:

bash
pip install fastapi uvicorn redis

2. Run the services:

bash
# Terminal 1: Run the API server
uvicorn your_app:app --host 0.0.0.0 --port 8000

# Terminal 2: Run the queue processor
python your_app.py

This solution provides several benefits: - Handles incoming requests immediately without blocking - Prevents server overload through rate limiting - Provides retry capability for failed notifications - Scales well as notification volume increases Would you like me to: 1. Explain how to implement specific error handling for your notification format? 2. Show how to set up monitoring alerts? 3. Provide configuration for a different queue system (like AWS SQS)? 4. Help with deployment configuration for this setup? Also, what kind of notifications are you receiving? Understanding their structure would help me provide more specific error handling and validation examples.