MILC

Abstract

Inverted list compression is a topic that has been studied for 50 years due to its fundamental importance in numerous applications including information retrieval, databases, and graph analytics. Typically, an inverted list compression algorithm is evaluated on its space overhead and query processing time. Earlier list compression designs mainly focused on minimizing the space overhead to reduce expensive disk I/O time in disk-oriented systems. But the recent trend is shifted towards reducing query processing time because the underlying systems tend to be memory-resident. Although there are many highly optimized compression approaches in main memory, there is still a considerable performance gap between query processing over compressed lists and uncompressed lists, which motivates this work. In this work, we set out to bridge this performance gap for the first time by proposing a new compression scheme, namely, MILC (memory inverted list compression). MILC relies on a series of techniques including offset-oriented fixed-bit encoding, dynamic partitioning, in-block compression, cache-aware optimization, and SIMD acceleration. We conduct experiments on three real-world datasets in information retrieval, databases, and graph analytics to demonstrate the high performance and low space overhead of MILC. We compare MILC with 12 recent compression algorithms and experimentally show that MILC improves the query performance by up to 13.2× and reduces the space overhead by up to 4.7×.