Edge Computing with AWS: CloudFront and Lambda@Edge
Edge computing reduces latency by processing closer to users. This guide covers AWS edge services.
Resources:
Distribution:
Type: AWS::CloudFront::Distribution
Properties:
DistributionConfig:
Origins:
- DomainName: example.com
Id: origin1
DefaultCacheBehavior:
TargetOriginId: origin1
ViewerProtocolPolicy: redirect-to-https
CachePolicyId: 658327ea-f89d-4fab-a63d-7e88639e58f6
exports.handler = async (event) => {
const request = event.Records[0].cf.request;
// Modify request
request.headers['x-custom-header'] = [{
key: 'X-Custom-Header',
value: 'custom-value'
}];
return request;
};
- A/B testing at edge
- Request/response modification
- Authentication
- Content personalization
- Bot detection
- Use CloudFront for static content
- Use Lambda@Edge for dynamic processing
- Minimize Lambda@Edge execution time
- Cache when possible
- Monitor edge performance
Use CloudFront for CDN and Lambda@Edge for edge computing to reduce latency and improve user experience.
For Edge Computing with AWS: CloudFront and Lambda@Edge, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Edge Computing with AWS: CloudFront and Lambda@Edge, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Edge Computing with AWS: CloudFront and Lambda@Edge, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Edge Computing with AWS: CloudFront and Lambda@Edge, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
