# AWS Lambda Cold Starts
Today I learned about the performance implications of cold starts in AWS Lambda functions and how to mitigate them effectively.
## Understanding Cold Starts
A cold start occurs when a Lambda function is invoked after being idle or when a new instance is created to handle increased traffic. During a cold start, AWS must:
1. Initialize a new container
2. Load the runtime environment
3. Load the function code
4. Execute any initialization code outside the handler
This process can add significant latency, ranging from hundreds of milliseconds to several seconds depending on the runtime and dependencies.
## Optimization Strategies
After extensive testing, I implemented several techniques that reduced our cold start times by over 70%:
- **Provisioned Concurrency**: Pre-initialized instances for critical functions
- **Runtime Selection**: Switched from Node.js to Python for faster initialization
- **Code Optimization**: Moved heavy imports inside handlers when possible
- **Package Size Reduction**: Removed unnecessary dependencies and used layer sharing
- **Memory Allocation**: Increased memory allocation which also increases CPU power
- **Warm-Up Functions**: Implemented scheduled events to keep functions warm
## Results
Our API response times became much more consistent, with p99 latency dropping from 3.2 seconds to under 800ms, significantly improving user experience for our global customer base.
Code Example
// Optimize imports by moving them inside the handler when possible
exports.handler = async (event) => {
// Only load heavy dependencies when needed
const heavyDependency = event.needsHeavyProcessing
? require('heavy-library')
: null;
// Function logic here
return { statusCode: 200, body: 'Success' };
};AWSLambdaServerlessPerformance