Starling: A Scalable Query Engine on Cloud Functions
Name
3318464.3380609.pdf
Description
Submitted version
Size
1.54 MB
Format
Adobe PDF
Checksum (MD5)
45adc69d39ce7c822da13293f4879486
Author(s)
Perron, Matthew
Castro Fernandez, Raul
DeWitt, David
Madden, Samuel
Date Issued
2020
Journal
Proceedings of the ACM SIGMOD International Conference on Management of Data
Publisher
ACM
Version
Original manuscript
Abstract
© 2020 Association for Computing Machinery. 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 underutilized. 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 a 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1145/3318464.3380609
