Elastic cloud services: scaling snowflake's control plane

ABSTRACTSnowflake's "Data Cloud", provided as Software-as-a-Service (SaaS), enables data storage, processing, and analytic solutions in a performant, easy to use, and flexible manner. Although cloud service providers provide the foundational infrastructure to run and scale a variety of workloads, operating Snowflake on cloud infrastructure presents interesting challenges. Customers expect Snowflake to be available at all times and to run their workloads with high performance. Behind the scenes, the software that runs customer workloads needs to be serviced and managed. Additionally, failures in individual components such as Virtual Machines (VM) need to be handled without disrupting running workloads. As a result, lifecycle management of compute artifacts, their scheduling and placement, software rollout (and rollback), replication, failure detection, automatic scaling, and load balancing become extremely important. In this paper, we describe the design and operation of Snowflake's Elastic Cloud Services (ECS) layer that manages cloud resources at global scale to meet the needs of the Snowflake Data Cloud. It provides the control plane to enable elasticity, availability, fault tolerance and efficient execution of customer workloads. ECS runs on multiple cloud service providers and provides capabilities such as cluster management, safe code rollout and rollback, management of pre-started pools of running VMs, horizontal and vertical autoscaling, throttling of incoming requests, VM placement, load-balancing across availability zones and cross-cloud and cross-region replication. We showcase the effect of these capabilities through empirical results on systems that execute millions of queries over petabytes of data on a daily basis.