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Scheduled scaling of Amazon Aurora Serverless with Amazon EventBridge Scheduler
- Amazon Aurora Serverless automatically scales based on metrics like CPU and memory usage, but some applications need predictable capacity adjustments at specific times.
- To demonstrate scheduled scaling for Aurora Serverless, Amazon EventBridge Scheduler can be used to adjust minimum Aurora Capacity Units proactively.
- Use cases for this pattern include daily usage with traffic spikes, special events like flash sales, system failures, and specific application usage patterns.
- Scheduled scaling optimizes performance and cost efficiency compared to provisioning for peak capacity with traditional relational databases.
- Implementing scheduled scaling with EventBridge involves setting predetermined minimum and maximum capacity levels for peak usage periods.
- Changing Aurora Serverless capacity configuration is non-disruptive and can be adjusted at any time without interrupting existing workloads.
- The solution architecture includes Aurora Serverless, EventBridge Scheduler, and an IAM role for interacting with Aurora through the RDS API.
- Prerequisites for implementing scheduled scaling include an Aurora cluster with at least one Amazon Serverless instance and familiarity with AWS CDK.
- Steps to implement the solution involve defining the scaling schedule, deploying using AWS CDK, and testing the scheduled scaling actions.
- Operational considerations include cost management, observability, logging, security, and governance to optimize the scheduled scaling solution.
- By integrating EventBridge, scheduled scaling for Aurora Serverless allows for better performance at specific times, enhancing end-user experiences.
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