Learn how to implement concurrent handlers to process multiple requests simultaneously with a single worker.

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What you’ll learn

In this guide you will learn how to:
  • Create an asynchronous handler function.
  • Create a concurrency modifier to dynamically adjust concurrency levels.
  • Optimize worker resources based on request patterns.
  • Test your concurrent handler locally.

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Requirements

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Step 1: Set up your environment

First, set up a virtual environment and install the necessary packages: 
# Create a Python virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install runpod asyncio

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Step 2: Create a concurrent handler file

Create a file named concurrent_handler.py and add the following code:
concurrent_handler.py
import runpod
import asyncio
import random

# Global variable to simulate a varying request rate
request_rate = 0

async def process_request(job):
    # This function processes incoming requests concurrently.
    #
    # Args:
    #     job (dict): Contains the input data and request metadata
    #
    # Returns:
    #     str: The processed result
 
    
    # Extract input data
    job_input = job["input"]
    delay = job_input.get("delay", 1)
    
    # Simulate an asynchronous task (like a database query or API call)
    await asyncio.sleep(delay)
    
    return f"Processed: {job_input}"

# Placeholder code for a dynamic concurrency adjustment function 
def adjust_concurrency(current_concurrency):
    return 50

def update_request_rate():
    """Simulates changes in the request rate to mimic real-world scenarios."""
    global request_rate
    request_rate = random.randint(20, 100)

# Start the Serverless function when the script is run
if __name__ == "__main__":
    runpod.serverless.start({
        "handler": process_request,
        "concurrency_modifier": adjust_concurrency
    })
The process_request function uses the async keyword, enabling it to use non-blocking I/O operations with await. This allows the function to pause during I/O operations (simulated with asyncio.sleep()) and handle other requests while waiting. The update_request_rate function simulates monitoring request patterns for adaptive scaling. This example uses a simple random number generator to simulate changing request patterns. In a production environment, you would:
  • Track actual request counts and response times.
  • Monitor system resource usage, such as CPU and memory.
  • Adjust concurrency based on real performance metrics.

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Step 3: Implement dynamic concurrency adjustment

Let’s enhance our handler with dynamic concurrency adjustment. This will allow your worker to handle more requests during high traffic periods and conserve resources during low traffic periods. Replace the placeholder adjust_concurrency function with this improved version: 
def adjust_concurrency(current_concurrency):
    # Dynamically adjust the worker's concurrency level based on request load.
    #
    # Args:
    #     current_concurrency (int): The current concurrency level
    #
    # Returns:
    #     int: The new concurrency level

    global request_rate
    
    # In production, this would use real metrics
    update_request_rate()
    
    max_concurrency = 10  # Maximum allowable concurrency
    min_concurrency = 1   # Minimum concurrency to maintain
    high_request_rate_threshold = 50  # Threshold for high request volume
    
    # Increase concurrency if under max limit and request rate is high
    if (request_rate > high_request_rate_threshold and 
        current_concurrency < max_concurrency):
        return current_concurrency + 1
    # Decrease concurrency if above min limit and request rate is low
    elif (request_rate <= high_request_rate_threshold and 
          current_concurrency > min_concurrency):
        return current_concurrency - 1
    
    return current_concurrency
Let’s break down how this function works:
  1. Control parameters:
    • max_concurrency = 10: Sets an upper limit on concurrency to prevent resource exhaustion.
    • min_concurrency = 1: Ensures at least one request can be processed at a time.
    • high_request_rate_threshold = 50: Defines when to consider traffic “high”.
    You can adjust these parameters based on your specific workload.
  2. Scaling up logic: 
    if (request_rate > high_request_rate_threshold and 
    current_concurrency < max_concurrency):
    return current_concurrency + 1
    This increases concurrency by 1 when:
    • The request rate exceeds our threshold (50 requests).
    • We haven’t reached our maximum concurrency limit.
  3. Scaling down logic: 
    elif (request_rate <= high_request_rate_threshold and 
      current_concurrency > min_concurrency):
    return current_concurrency - 1
    This decreases concurrency by 1 when:
    • The request rate is at or below our threshold.
    • We’re above our minimum concurrency level.
  4. Default behavior: 
    return current_concurrency
    If neither condition is met, maintain the current concurrency level.
With these enhancements, your concurrent handler will now dynamically adjust its concurrency level based on the observed request rate, optimizing resource usage and responsiveness.

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Step 4: Create a test input file

Now we’re ready to test our handler. Create a file named test_input.json to test your handler locally:
test_input.json
{
    "input": {
        "message": "Test concurrent processing",
        "delay": 0.5
    }
}

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Step 5: Test your handler locally

Run your handler to verify that it works correctly: 
python concurrent_handler.py
You should see output similar to this: 
--- Starting Serverless Worker |  Version 1.7.9 ---
INFO   | Using test_input.json as job input.
DEBUG  | Retrieved local job: {'input': {'message': 'Test concurrent processing', 'delay': 0.5}, 'id': 'local_test'}
INFO   | local_test | Started.
DEBUG  | local_test | Handler output: Processed: {'message': 'Test concurrent processing', 'delay': 0.5}
DEBUG  | local_test | run_job return: {'output': "Processed: {'message': 'Test concurrent processing', 'delay': 0.5}"}
INFO   | Job local_test completed successfully.
INFO   | Job result: {'output': "Processed: {'message': 'Test concurrent processing', 'delay': 0.5}"}
INFO   | Local testing complete, exiting.

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(Optional) Step 6: Implement real metrics collection

In a production environment, you should to replace the update_request_rate function with real metrics collection. Here is an example how you could build this functionality: 
def update_request_rate(request_history):
    # Collects real metrics about request patterns.
    #
    # Args:
    #     request_history (list): A list of request timestamps
    #
    # Returns:
    #     int: The new request rate

    global request_rate
    
    # Option 1: Track request count over a time window
    current_time = time.time()
    # Count requests in the last minute
    recent_requests = [r for r in request_history if r > current_time - 60]
    request_rate = len(recent_requests)
    
    # Option 2: Use an exponential moving average
    # request_rate = 0.9 * request_rate + 0.1 * new_requests
    
    # Option 3: Read from a shared metrics service like Redis
    # request_rate = redis_client.get('recent_request_rate')

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Next steps

Now that you’ve created a concurrent handler, you’re ready to: