Scaling serverless Postgres: How we implement autoscaling

The release of autoscaling in Feature-Preview allows databases to accommodate varying workloads by automatically increasing or decreasing CPU and memory allocation based on the current load. This feature provides several benefits, including accommodating varying and unexpected workloads, being highly scalable, saving time for manual management, scaling without restarting, simplified management, cost efficiency, and preventing overcommitting resources. The autoscaling system is implemented using a combination of Kubernetes, NeonVM, and cgroups to manage the allocation of resources to Postgres instances running in virtual machines. The system can handle live migrations of VMs to reduce load on nodes, and also includes a local file cache to speed up queries by utilizing additional memory when available.

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