Starling: A Scalable Query Engine on Cloud Function Services

TL;DR

Starling is a scalable, cost-effective query engine built on cloud functions that delivers interactive latency and handles large datasets efficiently, outperforming traditional provisioned systems for bursty workloads.

Contribution

It introduces a novel query engine on cloud functions that manages stateless workers and data shuffling, reducing costs and latency for cloud-based analytics.

Findings

Starling is less expensive than provisioned systems for infrequent queries.

It achieves lower latency than systems reading from cloud object stores.

It scales effectively to larger datasets.

Abstract

Much like on-premises systems, the natural choice for running database analytics workloads in the cloud is to provision a cluster of nodes to run a database instance. However, analytics workloads are often bursty or low volume, leaving clusters idle much of the time, meaning customers pay for compute resources even when unused. The ability of cloud function services, such as AWS Lambda or Azure Functions, to run small, fine granularity tasks make them appear to be a natural choice for query processing in such settings. But implementing an analytics system on cloud functions comes with its own set of challenges. These include managing hundreds of tiny stateless resource-constrained workers, handling stragglers, and shuffling data through opaque cloud services. In this paper we present Starling, a query execution engine built on cloud function services that employs number of techniques to mitigate these challenges, providing interactive query latency at a lower total cost than provisioned systems with low-to-moderate utilization. In particular, on a 1TB TPC-H dataset in cloud storage, Starling is less expensive than the best provisioned systems for workloads when queries arrive 1 minute apart or more. Starling also has lower latency than competing systems reading from cloud object stores and can scale to larger datasets.