Client Overview
Humanoo (Acquired by Telus) operating in the healthcare industry, has developed an innovative platform that caters to employee retention and corporate wellness. The platform is designed to provide comprehensive wellness solutions to insurance companies and employers.
By implementing these wellness programs, companies can improve employee satisfaction, reduce absenteeism, and enhance productivity. By leveraging cutting-edge technology and customised wellness programs, the start-up helps companies create a healthier and more supportive work environment that benefits both employees and employers.
The platform operates on AWS and relies on managed cloud services to support scalable, production-grade workloads.
Challenge
As the platform scaled, the client encountered the following key challenges:
Performance Optimisation
The client needed to improve the performance efficiency of their RDS MySQL instances, with a particular focus on optimising MySQL query execution and maintaining consistent database responsiveness across services.
Cost Efficiency
Running multiple Amazon RDS instances introduced increasing operational costs. The client aimed to reduce infrastructure spend while continuing to operate stable, production-grade database workloads without service disruption.
Solution
To enable AWS Graviton for Amazon RDS MySQL workloads, the migration required both a database engine upgrade and a carefully controlled cutover strategy. Since Graviton-based RDS MySQL instances require MySQL version 8, the solution was designed as a phased migration that prioritised compatibility, stability, and minimal service disruption.
The implementation followed these key steps:
- MySQL Version Upgrade: The existing RDS MySQL instances were upgraded from MySQL 5 to MySQL 8 to ensure compatibility with Graviton-based instance types and align with AWS-supported database engine requirements.
- Low-Downtime Migration Using RDS Blue/Green Deployments: To minimise operational impact during the transition, the migration leveraged Amazon RDS Blue/Green Deployments. This approach allowed the team to prepare and validate changes in parallel before switching production traffic.
- Graviton Instance Adoption: A Green environment was created from the existing RDS instances, including associated read replicas. The RDS instance types in the Green environment were then updated to their Graviton equivalents.
- Controlled Cutover: Once validation was complete, traffic was switched from the Blue environment to the Green environment, completing the migration with minimal downtime and maintaining application availability throughout the process.
This approach aligns with AWS best practices for database modernisation and Graviton adoption.
Results
Migration to Graviton
All Amazon RDS MySQL instances were successfully migrated to AWS Graviton-based instance types. This transition aligned the database platform with AWS-native ARM architecture, enabling improved price-performance characteristics while maintaining compatibility with managed RDS services.
Improved Resource Utilisation
Post-migration observations showed more efficient utilisation of CPU and memory resources across RDS instances. Improved workload efficiency enabled the platform to deliver consistent or improved performance while consuming fewer infrastructure resources.
Cost Savings
Following the migration to AWS Graviton-based RDS instances and subsequent rightsizing, the client achieved approximately 30% reduction in RDS-related infrastructure costs. Improved performance efficiency enabled the use of smaller instance types without compromising stability, resulting in lower ongoing operational spend while maintaining production-grade database performance.
Technology Stack
- Database Service: Amazon RDS MySQL on AWS Graviton-based instance types
- Migration & Deployment: Amazon RDS Blue/Green Deployments
- Monitoring and Performance Tracking: Amazon CloudWatch
By leveraging this technology stack, the migration to Graviton architecture was executed in a controlled and low-risk manner, enabling improved efficiency and reduced operational costs while maintaining stable, production-grade database workloads.
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