TL;DR
The paper introduces Memento filter, a novel range filter that supports dynamic datasets, fast operations, and low false positive rates, making it suitable for real-time applications like B-Trees.
Contribution
Memento filter is the first range filter to combine dynamicity, efficiency, and robust false positive guarantees for any workload.
Findings
Achieves competitive false positive rates and performance.
Supports inserts, deletes, and dataset expansion.
Doubles range query throughput in B-Tree-based key-value store.
Abstract
Range filters are probabilistic data structures that answer approximate range emptiness queries. They aid in avoiding processing empty range queries and have use cases in many application domains such as key-value stores and social web analytics. However, current range filter designs do not support dynamically changing and growing datasets. Moreover, several of these designs also exhibit impractically high false positive rates under correlated workloads, which are common in practice. These impediments restrict the applicability of range filters across a wide range of use cases. We introduce Memento filter, the first range filter to offer dynamicity, fast operations, and a robust false positive rate guarantee for any workload. Memento filter partitions the key universe and clusters its keys according to this partitioning. For each cluster, it stores a fingerprint and a list of key…
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