FishStore: Faster ingestion with subset hashing

Dong Xie, Badrish Chandramouli, Yinan Li, Donald Kossmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


The last decade has witnessed a huge increase in data being ingested into the cloud, in forms such as JSON, CSV, and binary formats. Traditionally, data is either ingested into storage in raw form, indexed ad-hoc using range indices, or cooked into analytics-friendly columnar formats. None of these solutions is able to handle modern requirements on storage: making the data available immediately for ad-hoc and streaming queries while ingesting at extremely high throughputs. This paper builds on recent advances in parsing and indexing techniques to propose FishStore, a concurrent latch-free storage layer for data with flexible schema, based on multi-chain hash indexing of dynamically registered predicated subsets of data. We find predicated subset hashing to be a powerful primitive that supports a broad range of queries on ingested data and admits a high-performance concurrent implementation. Our detailed evaluation on real datasets and queries shows that FishStore can handle a wide range of workloads and can ingest and retrieve data at an order of magnitude lower cost than state-of-the-art alternatives.

Original languageEnglish (US)
Title of host publicationSIGMOD 2019 - Proceedings of the 2019 International Conference on Management of Data
PublisherAssociation for Computing Machinery
Number of pages18
ISBN (Electronic)9781450356435
StatePublished - Jun 25 2019
Event2019 International Conference on Management of Data, SIGMOD 2019 - Amsterdam, Netherlands
Duration: Jun 30 2019Jul 5 2019

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078


Conference2019 International Conference on Management of Data, SIGMOD 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems


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