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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 ine!cient 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 speci"c 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.
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Brief Announcement: Racos: a Leaderless Erasure Coding State Machine Replication
Cloud storage systems often use state machine replication (SMR) to ensure reliability and availability. Erasure coding has recently been integrated with SMR to reduce disk and network I/O costs. This brief announcement shares our experience in developing a leaderless erasure coding SMR system. We integrate our system Racos with etcd, a distributed key-value storage that powers Kubernetes. Racos outperforms competitors by up to 3.36x in throughput.
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- Award ID(s):
- 2334021
- PAR ID:
- 10508473
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the 36th ACM Symposium on Parallelism in Algorithms and Architecture
- ISBN:
- 979-8-4007-0416-1
- Format(s):
- Medium: X
- Location:
- Nantes, France
- Sponsoring Org:
- National Science Foundation
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