AniFilter

Abstract

Approximate Membership Query (AMQ) data structures are widely used in databases, storage systems, and other domains. Recent advances in Non-Volatile Memory (NVM) technologies made possible byte-addressable, high performance persistent memories. This paper presents an optimized persistent AMQ for NVM, called AniFilter (AF). Based on Cuckoo Filter (CF), AF improves insertion throughput on NVM with Spillable Buckets and Lookahead Eviction, and lookup throughput with Bucket Primacy. To analyze the effect of our optimizations, we design a probabilistic model to estimate the performance of CF and AF. For failure atomicity, AF writes minimum amount of logging to maintain its consistency. We evaluated AF and four other AMQs - CF, Morton Filter (MF), Rank-and-Select Quotient Filter (RSQF), and Bloom Filter (BF) - on NVM. We use Intel Optane DC Persistent Memory and Quartz emulation for the evaluation. AF and CF are generally much faster than the other filters for sequential runs. However, in high load factors CF's insertion throughput becomes prohibitively low due to the eviction overhead. With our optimizations, AF's insertion throughput is fastest even in high load factors. In parallel evaluation, AF's performance is substantially higher than CF for both insertion and lookup. Our optimizations reduce the bandwidth consumption, making AF's parallel performance much faster than CF's on bandwidth-limited NVM. For parallel insertion AF is up to 10.7X faster than CF (2.6X faster on average) and for parallel lookup AF is up to 1.2X faster (1.1X faster on average).