Atlassian's new Jira migration tool slowed down cloudy moves

"This architecture, however, proved to be blocking and less scalable, and the customers in our migration pipeline were too large to migrate using this approach. So Atlassian built a new migration architecture that operates in a streamlined fashion, and gracefully avoids the bottlenecks and scalability issues that existed in the API-driven architecture."

"When Atlassian tested it, the company found the new migration pipeline took about 34 percent longer than its previous tools, and overall work item throughput dropped by roughly 60 percent on synthetic tests. For customers with tens of thousands of users and massive project portfolios, fixing this became non-negotiable."

"We benchmarked different worker node sizes and configurations. The original setup ran on small nodes; scaling them up yielded significantly better throughput, balancing cost vs performance. Atlassian also tightened autoscaling rules so that worker nodes spun up quickly whenever CPU usage spiked, maintaining high throughput from the start."

Atlassian discontinued its datacenter products and shifted users to cloud equivalents, but discovered its new migration pipeline performed significantly worse than expected. The API-driven architecture proved blocking and unscalable for large customers. A replacement architecture was built to address bottlenecks, yet testing revealed the new pipeline took 34 percent longer to complete migrations with 60 percent lower work item throughput. For customers with tens of thousands of users, this performance degradation became critical. Atlassian implemented multiple fixes including worker node scaling, autoscaling rule optimization, and polling timeout reconfigurations to improve performance and maintain high throughput during migrations.

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