AWS Lambda Cold Start vs Hot Start

In this blog, we'll explore one of the most critical concepts in AWS Lambda—Cold Start and Hot Start. These terms significantly impact Lambda's performance, especially in real-time applications. By the end of this blog, you'll understand:

What Cold Start and Hot Start are.
Why they happen.
How to measure and handle them using Python.

What is a Cold Start?

When a Lambda function is triggered for the first time, AWS creates a new execution environment behind the scenes. This environment includes a virtual machine to deploy your code, initialize dependencies, and prepare the runtime. This initialization adds extra latency, leading to a Cold Start.

What is a Hot Start?

After the first invocation, AWS keeps the execution environment alive for subsequent requests. If another event triggers the function within this period, the same environment is reused, resulting in a Hot Start. This process is much faster as the initialization is already complete.

Why Cold Start Happens

When a new execution environment is created:

The runtime and dependencies are initialized.
Application code is loaded into memory.
This adds overhead, causing a delay in the first execution.

For example, the first execution might take 90 milliseconds, while subsequent executions could take only 2 milliseconds.

Practical Code Example

Here’s a simple Python Lambda function to demonstrate Cold Start and Hot Start:

import json
import time

cold_start_time = None

def lambda_handler(event, context):
    
    global cold_start_time

    if cold_start_time is None:
        time.sleep(3)  # Simulate initialization delay
        cold_start_time = time.time()
        start_type = "cold start"
    else:
        start_type = "hot start"
    
    current_time = time.time()
    response = {
        "current_time": current_time,
        "start_type": start_type,
        "cold_start_time": cold_start_time
    }

    return {
        'statusCode': 200,
        'body': json.dumps(response)
    }

How It Works

The global variable cold_start_time is initialized as None.
During the first invocation, the function detects that no environment exists, simulates a delay, and sets the cold_start_time.
Subsequent invocations reuse the same environment and skip the initialization.

Testing the Function

Deploy the Lambda function in AWS.
Trigger the function for the first time. You'll notice the response indicates a "cold start" and takes a longer time.
Trigger it again. The response will indicate a "hot start" and execute much faster.

Performance Optimization Tips

To minimize Cold Start impact:

Optimize Initialization: Keep your Lambda function lightweight and avoid heavy dependencies.
Increase Memory Allocation: Higher memory reduces startup time.
Warm-Up Functions: Use tools or schedulers to invoke the function periodically and keep the environment warm.

Real-Life Use Cases

Database Connections:
- Establish database connections during the Cold Start and reuse them during Hot Starts.
- Avoid creating new connections for every invocation.
Media Processing:
- Functions that encode or decode media files can initialize encoders during the Cold Start to save processing time for subsequent requests.
Cost Optimization:
- Since AWS Lambda charges for execution time, reducing Cold Start duration can lower your costs significantly.

Cold Start vs Hot Start in Numbers

Here’s an example from our test:

Cold Start: First execution took 5 seconds (includes simulated initialization time).
Hot Start: Subsequent executions took less than 100 milliseconds.

How Long Does the Execution Environment Stay Alive?

AWS does not specify the exact duration for which an environment remains alive. It could be minutes or hours, depending on resource utilization. If the function remains idle for an extended period, AWS may delete the environment, resulting in another Cold Start.

For more such tutorials, visit learning-ocean.com and explore a wide range of AWS and serverless topics.

Previous Next