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This Guidance shows how to handle sudden traffic spikes in Amazon Aurora using a mixed-configuration architecture that combines a provisioned Aurora cluster with Aurora Serverless v2 instances and custom auto-scaling. It demonstrates near real-time response to unpredictable traffic fluctuations, preventing database overload and service disruptions. With this Guidance, you can promote service continuity and protect against revenue loss from database-related outages, even with unexpected traffic spikes from marketing events or new product launches.
Please note: [Disclaimer]
Architecture Diagram
[Architecture diagram description]
Step 1
Add Amazon Aurora Serverless v2 (replica) to the Aurora Provisioned Cluster, and create each custom endpoint pointing to the replica.
Step 2
Configure a private hosted zone on Amazon Route 53, and create a weight-based record with the same record name for each endpoint. Set the weight of the record pointing to the Aurora Serverless v2 to zero.
Step 3
Enable the Enhanced Monitoring feature on all instances to automatically collect RDSOSMetrics information in Amazon CloudWatch.
Step 4
Create an AWS Lambda function that refines the information collected in the RDSOSMetrics log group to create custom metrics for the target instances for monitoring.
Step 5
Use aws-embedded-metric to push the refined custom metrics to CloudWatch in near-real time.
Step 6
Based on your stored custom metrics, configure CloudWatch alarms for weight adjustment.
Step 7
When the alarm for weight adjustment occurs, it calls AWS Step Functions. Step Functions adjusts the weight of the Route 53 record to distribute and recover traffic to Aurora Serverless v2.
Step 8
Set up an autoscaling policy based on the custom metric alarms collected using AWS Auto Scaling. When an autoscaling alarm occurs, it invokes autoscaling for the provisioned replica instance.
Step 1
Add Amazon Aurora Serverless v2 (replica) to the Aurora Provisioned Cluster, and create each custom endpoint pointing to the replica.
Step 2
Configure a private hosted zone on Amazon Route 53, and create a weight-based record with the same record name for each endpoint. Set the weight of the record pointing to the Aurora Serverless v2 to zero.
Step 3
Enable the Enhanced Monitoring feature on all instances to automatically collect RDSOSMetrics information in Amazon CloudWatch.
Step 4
Create an AWS Lambda function that refines the information collected in the RDSOSMetrics log group to create custom metrics for the target instances for monitoring.
Step 5
Use aws-embedded-metric to push the refined custom metrics to CloudWatch in near-real time.
Step 6
Based on your stored custom metrics, configure CloudWatch alarms for weight adjustment.
Step 7
When the alarm for weight adjustment occurs, it calls AWS Step Functions. Step Functions adjusts the weight of the Route 53 record to distribute and recover traffic to Aurora Serverless v2.
Step 8
Set up an autoscaling policy based on the custom metric alarms collected using AWS Auto Scaling. When an autoscaling alarm occurs, it invokes autoscaling for the provisioned replica instance.
Well-Architected Pillars
The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.
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Operational ExcellenceRead the Operational Excellence whitepaper
Aurora Serverless v2 enables automatic scaling through its serverless architecture, while AWS Auto Scaling dynamically adds provisioned instances to scale out read replicas. This Guidance allows for elastic scaling of resources up or down based on actual traffic demand, achieving cost efficiency and system stability simultaneously. Even during sudden traffic spikes, stable database performance can be maintained.
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SecurityRead the Security whitepaper
CloudWatch logs encrypt and securely transfer log data, while AWS Identity and Access Management (IAM) grants the minimum required permissions to services and resources, following the principle of least privilege. By protecting log information and restricting access to only authorized entities, you can reduce the risk of security breaches and protect data integrity.
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ReliabilityRead the Reliability whitepaper
Aurora and Route 53 provide high availability and fault tolerance for databases through multi-Availability Zone (AZ) deployments and automatic failover capabilities. Route 53 weight-based routing distributes traffic efficiently to only healthy instances and enables rapid automated recovery and service continuity in the event of infrastructure failures.
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Performance EfficiencyRead the Performance Efficiency whitepaper
Aurora Serverless v2 automatically scales computing resources during traffic spikes, improving read performance. Route 53 weight-based records dynamically route traffic to Aurora Serverless v2 instances, reducing load on provisioned instances. High-resolution CloudWatch custom metrics detect traffic spikes within 10 seconds, allowing for quick response and maintenance of application responsiveness and stability.
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Cost OptimizationRead the Cost Optimization whitepaper
Aurora Serverless automatically responds to actual traffic patterns, eliminating costs from overprovisioning. AWS Auto Scaling provisions only the required number of Aurora read replicas. Custom CloudWatch metrics enable precise scaling decisions at the right time, preventing unnecessary resource wastage. Lambda functions incur costs only on an event-driven basis, further optimizing costs.
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SustainabilityRead the Sustainability whitepaper
The dynamic scaling capabilities of Aurora and Aurora Serverless v2 minimize environmental impact. These services provision only the resources required for actual workloads, preventing overprovisioning and wasted resources. By elastically scaling resources up and down based on actual demands, you can reduce energy consumption and associated carbon emissions.
Implementation Resources
The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.
Related Content
How ktown4u built a custom auto scaling architecture using an Amazon Aurora mixed-configuration cluster to respond to sudden traffic spikes
Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.