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. In this work, we present Fusion, an object store for analytics that is optimized for query pushdown on erasure-coded data. It co-designs its erasure coding and file placement topologies, taking into account popular analytics file formats (e.g., Parquet). Fusion employs a novel stripe construction algorithm that prevents fragmentation of computable units within an object, and minimizes storage overhead during erasure coding. Compared to existing erasure-coded stores, Fusion improves median and tail latency by 64% and 81%, respectively, on TPC-H, and up to 40% and 48% respectively, on real-world SQL queries. Fusion achieves this while incurring a modest 1.2% storage overhead compared to the optimal.

@inproceedings{LuRCF25,
  title = {Fusion: An Analytics Object Store Optimized for Query Pushdown},
  author = {Jianan Lu and Ashwini Raina and Asaf Cidon and Michael J. Freedman},
  booktitle = {ASPLOS},
  year = {2025},
  doi = {10.1145/3669940.3707234}
}