We present the Succinct Range Filter (SuRF), a fast and compact data structure for approximate membership tests. Unlike traditional Bloom filters, SuRF supports both single-key lookups and common range queries. SuRF is based on a new data structure called the Fast Succinct Trie (FST) that matches the point and range query performance of state-of-the-art order-preserving indexes, while consuming only 10 bits per trie node. The false positive rates in SuRF for both point and range queries are tunable to satisfy different application needs. We evaluate SuRF in RocksDB as a replacement for its Bloom filters to reduce I/O by filtering requests before they access on-disk data structures. Our experiments on a 100 GB dataset show that replacing RocksDB's Bloom filters with SuRFs speeds up open-seek (without upper-bound) and closed-seek (with upper-bound) queries by up to 1.5× and 5× with a modest cost on the worst-case (all-missing) point query throughput due to slightly higher false positive rate.

中文翻译：

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简洁范围过滤器

我们提出了简洁范围过滤器 (SuRF)，这是一种用于近似隶属度测试的快速且紧凑的数据结构。与传统的 Bloom 过滤器不同，SuRF 支持单键查找和常见范围查询。SuRF 基于一种名为 Fast Succinct Trie (FST) 的新数据结构，该结构与最先进的保序索引的点和范围查询性能相匹配，同时每个 trie 节点仅消耗 10 位。SuRF 中针对点查询和范围查询的误报率是可调的，以满足不同的应用程序需求。我们评估 RocksDB 中的 SuRF 作为其 Bloom 过滤器的替代品，通过在请求访问磁盘数据结构之前过滤请求来减少 I/O。我们在 100 GB 数据集上的实验表明，替换 RocksDB'