Work

Fusion: An Analytics Object Store Optimized for Query Pushdown

Year: 2025

Type: conference-paper

Abstract: The prevalence of disaggregated storage in public clouds has led to increased latency in modern OLAP cloud databases, particularly when handling ad-hoc and highly-selective queries on large objects. To address this, cloud databases have adopted computation pushdown, executing query predicates closer to the storage layer. However, existing pushdown solutions are inefficient in erasure-coded storage. Cloud storage employs erasure coding that partitions analytics file objects into fixed-sized blocks and distributes them across storage nodes. Consequently, when a specific part of the object is queried, the storage system must reassemble the object across nodes, incurring significant network latency.

Source: ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

DOI: 10.1145/3669940.3707234

Authors: Jianan Lu, Ashwini Raina, Asaf Cidon, Michael J. Freedman

Institutions: Princeton University, Columbia University

Language: English

Topic: Advanced Database Systems and Queries

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Universitas Brawijaya

Awards: Collaborative Research: CNS CORE: Medium: The Case for Blended Storage, G7000763757