HaSiS: A Hardware-assisted Single-index Store for Hybrid Transactional and Analytical Processing

Driven by the exploding demands for real-time data analytics, hybrid transactional and analytical processing (HTAP) has become a topic of great interest in academia and the database industry. To address the well-known conflict between optimal storage formats for online transactional processing (OLTP) and online analytical processing (OLAP), the conventional practice employs a mixture of at least two distinct index data structures (e.g., B + -tree and column-store) and dynamically migrates data across different index domains. Unfortunately, such a multi-index design is notably subject to non-trivial trade-offs among OLTP performance, OLAP performance, and OLAP data freshness. In contrast to prior work that centered around exploring the multi-index design space, this work advocates a single-index design for a paradigm shift towards much more effectively serving HTAP workloads. This is made possible by computational storage drives (CSDs) with built-in transparent compression that are emerging on the commercial market. The key is to exploit the fact that compression-capable CSDs enable data management software to purposefully employ sparsely filled storage data blocks without sacrificing physical storage capacity. Leveraging this unique feature, we have developed an HTAP-oriented B + -tree design that can effectively serve HTAP workloads and in the meantime can achieve almost instant OLAP data freshness. We have developed and open-sourced a fully functional prototype. Our results show that compared to the state-of-the-art solutions, such a CSD-assisted single-index design can ensure data freshness and deliver high performance for HTAP workloads.