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- W3018708672 abstract "We introduce Rosetta, a probabilistic range filter designed specifically for LSM-tree based key-value stores. The core intuition is that we can sacrifice filter probe time because it is not visible in end-to-end key-value store performance, which in turn allows us to significantly reduce the filter false positive rate for every level of the tree. Rosetta indexes all binary prefixes of a key using a hierarchically arranged set of Bloom filters. It then converts each range query into multiple probes, one for each non-overlapping binary prefix. Rosetta has the ability to track workload patterns and adopt a beneficial tuning for each individual LSM-tree run by adjusting the number of Bloom filters it uses and how memory is spread among them to optimize the FPR/CPU cost balance. We show how to integrate Rosetta in a full system, RocksDB, and we demonstrate that it brings as much as a 40x improvement compared to default RocksDB and 2-5x improvement compared to state-of-the-art range filters in a variety of workloads and across different levels of the memory hierarchy (memory, SSD, hard disk). We also show that, unlike state-of-the-art filters, Rosetta brings a net benefit in RocksDB's overall performance, i.e., it improves range queries without losing any performance for point queries." @default.
- W3018708672 created "2020-05-01" @default.
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- W3018708672 date "2020-05-31" @default.
- W3018708672 modified "2023-09-26" @default.
- W3018708672 title "Rosetta: A Robust Space-Time Optimized Range Filter for Key-Value Stores" @default.
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- W3018708672 doi "https://doi.org/10.1145/3318464.3389731" @default.
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