Matthew Perron

MIT CSAIL

Cambridge, MA

I am a PhD Student in the MIT Data Systems Group advised by Sam Madden. I am interested in making analytical databases much easier to use. Before MIT, I earned a Master’s degree in Computer Science at Carnegie Mellon University where I worked with Andy Pavlo. I also worked as a software engineer at Softbank.

My current research interest is making analytical database systems better at responding to end users needs with minimal configuration. In particular, my recent work considers how best to use elastic pools of resources like AWS Lambda, responding to workload shifts while minimizing costs.

I am currently seeking a position as an Assistant Professor.

News

Aug 28, 2023	I attended VLDB 2023.
Aug 23, 2023	Cackle: Analytical Workload Cost and Performance Stability With Elastic Pools has been accepted at SIGMOD 2024.

Selected Publications

2023

Cackle: Analytical Workload Cost and Performance Stability With Elastic Pools (To Appear)

Matthew Perron, Michael Cafarella, Raul Castro Fernandez, David DeWitt, and Samuel Madden

Proc. ACM Manag. Data, Dec 2023

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Analytical query workloads are prone to rapid fluctuations in resource demands. These rapid, hard to predict resource demand changes make provisioning a challenge. Users must either over provision at excessive cost or suffer poor query latency when demand spikes. Prior work shows the viability of using cloud functions to match the supply of compute to the workload demand without provisioning resources ahead of time. For low query volumes, this approach is less costly at reasonable performance compared to provisioned systems, but as query volumes increase the cost overhead of cloud functions outweighs the benefit gained by rapid elasticity. In this work, we propose a novel strategy combining rapidly scalable but expensive resources with slow to start but inexpensive virtual machines to gain the benefit of elasticity without losing out on the cost savings of provisioned resources. We demonstrate a technique that minimizes cost over a wide range of workloads, environmental conditions, and compute costs while providing stable query performance. We implement these ideas in Cackle and demonstrate that it achieves similar performance and cost per query across a wide range of workloads, avoiding the cost and performance cliffs of alternative approaches.

2020

Starling: A Scalable Query Engine on Cloud Functions

Matthew Perron, Raul Castro Fernandez, David DeWitt, and Samuel Madden

In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, Dec 2020

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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.

2019

How I learned to stop worrying and love re-optimization

Matthew Perron, Zeyuan Shang, Tim Kraska, and Michael Stonebraker

In 2019 IEEE 35th International Conference on Data Engineering (ICDE), Dec 2019

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Cost-based query optimizers remain one of the most important components of database management systems for analytic workloads. Though modern optimizers select plans close to optimal performance in the common case, a small number of queries are an order of magnitude slower than they could be. In this paper we investigate why this is still the case, despite decades of improvements to cost models, plan enumeration, and cardinality estimation. We demonstrate why we believe that a re-optimization mechanism is likely the most cost-effective way to improve end-to-end query performance. We find that even a simple re-optimization scheme can improve the latency of many poorly performing queries. We demonstrate that re-optimization improves the end-to-end latency of the top 20 longest running queries in the Join Order Benchmark by 27%, realizing most of the benefit of perfect cardinality estimation.