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Atlassian’s migration of 4 million Postgres databases to AWS Aurora represents a significant shift in cloud infrastructure strategy, driven by performance, cost, and reliability goals. This move involved transitioning from AWS RDS on shared infrastructure to Aurora PostgreSQL, a decision that required careful optimization of resource allocation and system architecture [1]. Below is a detailed breakdown of the technical and operational aspects of this migration:
Technical Overview of the Migration
Instance Size Optimization
Atlassian reduced AWS instance sizes from m5.4xlarge to r6.2xlarge, achieving a 50% reduction in instance size while maintaining memory capacity. This adjustment allowed for more efficient resource utilization, balancing CPU and memory requirements to align with Aurora’s architecture [1].Performance and Uptime Improvements
The migration aimed to elevate system uptime from 99.95% to 99.99%, a critical enhancement for mission-critical applications. Aurora’s distributed architecture and enhanced autoscaling capabilities were central to achieving this reliability target, ensuring minimal downtime even during peak workloads [1].Autoscaling and Scalability
Aurora’s advanced autoscaling features enabled Atlassian to dynamically adjust resources based on demand, reducing manual intervention and improving responsiveness to traffic spikes. This scalability is particularly vital for handling the vast scale of 4 million databases [1].Database Architecture Transition
By leveraging Aurora’s PostgreSQL compatibility, Atlassian retained existing application compatibility while benefiting from Aurora’s performance optimizations, such as enhanced read replicas and reduced latency for write operations [2].
Challenges and Considerations
- Technical Complexity: The migration required addressing challenges related to data consistency, replication, and workload distribution across Aurora’s distributed architecture. Pat Rubis, Atlassian’s principal site reliability engineer, noted that these hurdles were mitigated through rigorous testing and incremental deployment [1].
- Cost Efficiency: The reduction in instance size and improved resource utilization directly contributed to cost savings, a key driver for the migration [1].
Strategic Implications
This migration underscores Atlassian’s commitment to modernizing its cloud infrastructure to support its global user base. By adopting Aurora, the company aligns with broader industry trends toward scalable, high-availability databases, while also addressing specific pain points in its previous RDS setup [2].
Conclusion
Atlassian’s transition to AWS Aurora exemplifies how cloud-native databases can enhance performance, reliability, and cost efficiency for large-scale operations. The technical adjustments in instance sizing, autoscaling, and architecture optimization highlight the importance of tailored cloud strategies in managing complex, distributed systems. As the tech landscape evolves, such migrations will likely become more common, driven by the need for agility and scalability in data management.
This case study also reflects the broader industry shift toward open-source and hybrid cloud solutions, as seen in PostgreSQL’s ongoing enhancements and Aurora’s integration of open-source capabilities [2].
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