Debug: AWS Lambda cold start optimization

AWS Lambda functions have high latency on first invocation (cold start), affecting user experience.

Diagnose and reduce cold start times:

# 1. Measure cold starts in CloudWatch
# Look for REPORT lines with 'Init Duration'
# Init Duration = cold start time

# 2. Check current performance
aws lambda get-function-configuration --function-name myFunc \
  --query '{Memory: MemorySize, Runtime: Runtime, Size: CodeSize}'

Optimization strategies:

# Move initialization OUTSIDE the handler
import boto3

# These run during cold start (Init phase)
db = boto3.resource('dynamodb')
table = db.Table('users')  # Connection reused across invocations

def handler(event, context):
    # Hot path - only this runs on warm invocations
    return table.get_item(Key={'id': event['id']})

Reduce package size:

• Use Lambda layers for shared dependencies
• Exclude dev dependencies: pip install --target ./package -r requirements.txt
• Use lightweight alternatives (e.g., urllib3 instead of requests)

Runtime choices (cold start impact):

• Python/Node.js: ~100-300ms
• Java/C#: ~500-2000ms (use SnapStart for Java)
• Rust/Go: ~10-50ms

Other techniques:

• Increase memory (also increases CPU proportionally)
• Use provisioned concurrency for latency-critical functions
• Use ARM (Graviton2) - 20% cheaper, similar performance
• Avoid VPC unless needed (VPC adds ~1s to cold start)
• Use Lambda SnapStart (Java) for near-zero cold starts

Cold starts happen when Lambda creates a new execution environment. The init phase loads your code, runtime, and dependencies, so minimizing these reduces latency.

AWS Lambda functions with latency requirements