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 distinct indexes and dynamically migrates data across different index domains. Unfortunately, such a multi-index design is notably subject to non-trivial trade-offs among OLTP/OLAP performance and data freshness. This work advocates a single-index design to serve 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 sparse data management without sacrificing physical storage capacity. Leveraging this, we have developed an HTAP-oriented B + -tree design that can effectively serve HTAP workloads and meanwhile achieve almost instant data freshness. We also comprehensively analyze the inherent effects of storage fill factor and propose a dynamic adapter design to achieve optimal storage/performance efficiency for varying workloads. We have developed and open-sourced a fully functional prototype. Evaluation results show that our design can ensure data freshness and deliver high performance.
Huang et al. (2026) studied this question.