CakeMask: Enabling Fresh-Data HTAP Databases with High Performance and Low Memory Overhead

HTAP systems increasingly need to support analytics over fresh transactional data. In row-columnar HTAP architectures, this raises a visibility-management problem: analytical queries must observe the latest updates. Addressing this, existing designs struggle with a three-way tradeoff among transactional update throughput, analytical scan efficiency, and memory overhead.

This paper presents CakeMask, a visibility-management design for fresh-data HTAP. To balance performance and memory overhead, CakeMask preserves bitmap as the compact, scan-efficient base visibility representation, while moving recent visibility changes into a delta visibility layer to avoid concurrent update overhead on the bitmap. It further combines an LSM-inspired delta structure, an optimized concurrent B+ tree with non-blocking scans, optimized bitmap–delta merging via vectorization, parallelism, and caching, and dynamic consolidation to bound long-term scan overhead. We implement CakeMask in a commercial cloud HTAP database and evaluate it on diverse workloads. The results show that CakeMask delivers scan performance comparable to in-memory version array at 1.6% of its memory usage, and outperforms existing on-disk visibility-management solutions up to 79× in write throughput. CakeMask has been deployed in production in a commercial HTAP system and has been validated on real-world workloads over three years.