A Comparative Study of Secondary Indexing Techniques in LSM-based NoSQL Databases

NoSQL databases are increasingly used in big data applications, because they achieve fast write throughput and fast lookups on the primary key. Many of these applications also require queries on non-primary attributes. For that reason, several NoSQL databases have added support for secondary indexes. However, these works are fragmented, as each system generally supports one type of secondary index, and may be using different names or no name at all to refer to such indexes. As there is no single system that supports all types of secondary indexes, no experimental head-to head comparison or performance analysis of the various secondary indexing techniques in terms of throughput and space exists. In this paper, we present a taxonomy of NoSQL secondary indexes, broadly split into two classes: Embedded Indexes (i.e. lightweight filters embedded inside the primary table) and Stand-Alone Indexes (i.e. separate data structures). To ensure the fairness of our comparative study, we built a system, LevelDB++, on top of Google's popular open-source LevelDB key-value store. There, we implemented two Embedded Indexes and three state-of-the-art Stand-Alone indexes, which cover most of the popular NoSQL databases. Our comprehensive experimental study and theoretical evaluation show that none of these indexing techniques dominate the others: the embedded indexes offer superior write throughput and are more space efficient, whereas the stand-alone secondary indexes achieve faster query response times. Thus, the optimal choice of secondary index depends on the application workload. This paper provides an empirical guideline for choosing secondary indexes.