FlexPushdownDB: Hybrid Pushdown and Caching in a Cloud DBMS
Yifei Yang, Matt Youill, Matthew E. Woicik, Yizhou Liu, Xiangyao Yu, Marco Serafini, Ashraf Aboulnaga, Michael Stonebraker
Abstract
Modern cloud databases adopt a storage-disaggregation architecture that separates the management of computation and storage. A major bottleneck in such an architecture is the network connecting the computation and storage layers. Two solutions have been explored to mitigate the bottleneck: caching and computation pushdown. While both techniques can significantly reduce network traffic, existing DBMSs consider them as orthogonal techniques and support only one or the other, leaving potential performance benefits unexploited.
In this paper we present FlexPushdownDB (FPDB) , an OLAP cloud DBMS prototype that supports fine-grained hybrid query execution to combine the benefits of caching and computation pushdown in a storage-disaggregation architecture. We build a hybrid query executor based on a new concept called separable operators to combine the data from the cache and results from the pushdown processing. We also propose a novel Weighted-LFU cache replacement policy that takes into account the cost of pushdown computation. Our experimental evaluation on the Star Schema Benchmark shows that the hybrid execution outperforms both the conventional caching-only architecture and pushdown-only architecture by 2.2X. In the hybrid architecture, our experiments show that Weighted-LFU can outperform the baseline LFU by 37%.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext cfcf84cf-4abb-43b7-af16-84be6870668aCited by top-tier papers23
- The Case for Distributed Shared-Memory Databases with RDMA-Enabled Memory DisaggregationRuihong Wang, Jianguo Wang, Stratos Idreos, M. Tamer Özsu et al.VLDB 2023 · 49 citations
- Exploiting Cloud Object Storage for High-Performance AnalyticsDominik Durner, Viktor Leis, Thomas NeumannVLDB 2023 · 45 citations
- Profiling Hyperscale Big Data ProcessingAbraham Gonzalez, Aasheesh Kolli, Samira Manabi Khan, Sihang Liu et al.ISCA 2023 · 30 citations
- A Deep Dive into Common Open Formats for Analytical DBMSsChunwei Liu, Anna Pavlenko, Matteo Interlandi, Brandon HaynesVLDB 2023 · 23 citations
- Understanding the Performance Implications of the Design Principles in Storage-Disaggregated DatabasesXi Pang, Jianguo WangSIGMOD 2024 · 19 citations
Builds on3
- Building An Elastic Query Engine on Disaggregated StorageMidhul Vuppalapati, Justin Miron, Rachit Agarwal, Dan Truong et al.NSDI 2020 · 142 citations
- AQUOMAN: An Analytic-Query Offloading MachineShuotao Xu, Thomas Bourgeat, Tianhao Huang, Hojun Kim et al.MICRO 2020 · 26 citations
- Database Processing-in-Memory: An Experimental StudyTiago Rodrigo Kepe, Eduardo C. de Almeida, Marco A. Z. AlvesVLDB 2020 · 22 citations
Related papers
- Understanding and Optimizing Database Pushdown on Disaggregated StorageHua Zhang, Xiao Li, Yuebin Bai, Ming LiuASPLOS 2026 · 1 citation
- Crystal: A Unified Cache Storage System for Analytical DatabasesDominik Durner, Badrish Chandramouli, Yinan LiVLDB 2021 · 11 citations
- Fusion: An Analytics Object Store Optimized for Query PushdownJianan Lu, Ashwini Raina, Asaf Cidon, Michael J. FreedmanASPLOS 2025 · 4 citations
- O3-LSM: Maximizing Disaggregated LSM Write Performance via Three-Layer OffloadingQi Lin, Gangqi Huang, Te Guo, Chang Guo et al.SIGMOD 2026 · 2 citations
- Understanding the Effect of Data Center Resource Disaggregation on Production DBMSsQizhen Zhang, Yifan Cai, Xinyi Chen, Sebastian Angel et al.VLDB 2020 · 64 citations
