The log structured merge (LSM) tree has been widely adopted by database systems owing to its superior write performance. However, LSM-tree based databases face vulnerabilities when processing analytical queries due to the read amplification caused by its architecture and the limited use of storage devices with high bandwidth. To flexibly handle transactional and analytical workloads, we proposed and implemented OurRocks taking full advantage of NVMe SSD and GPU devices, which improves scan performance. Although the NVMe SSD serves multi GB/s I/O rates, it is necessary to solve the data transfer overhead which limits the benefits of the GPU processing. The primary idea is to offload the scan operation to the GPU with filtering predicate pushdown and resolve the bottleneck from the data transfer between devices with direct memory access (DMA). OurRocks benefits from all the features of write-optimized database systems, in addition to accelerating the analytic queries using the aforementioned idea. Experimental results indicate that OurRocks effectively leverages resources of the NVMe SSD and GPU and significantly improves the execution of queries in the YCSB and TPC-H benchmarks, compared to the conventional write-optimized database. Our research demonstrates that the proposed approach can speed up the handling of the data-intensive workloads.

In this paper, we propose MV-FTL, a multi-version flash transition layer (FTL) that provides page-level multi-version management. By extending a unique characteristic of solid-state drives (SSDs), the out-of-place (OoP) update to multi-version management, MV-FTL can both guarantee atomic page updates from each transaction and provide concurrency without requiring redundant log data writes as well. For evaluation, we first modified SQLite, a lightweight database management system (DBMS), to cooperate with MV-FTL. Owing to the architectural simplicity of SQLite, we clearly show that MV-FTL improves both the performance and the concurrency aspects of the system. In addition, to prove the effectiveness in a full-fledged enterprise-level DBMS, we modified MyRocks, a MySQL variant by Facebook, to use our new Patch Compaction algorithm, which deeply relies on MV-FTL. The TPC-C and LinkBench benchmark tests demonstrated that MV-FTL reduces the overall amount of writes, implying that MV-FTL can be effective in such DBMSs.

In this paper, we propose a multiversion index utilizing key features of SSDs (solid state drives). SSDs have many advantages, e.g., fast read/write performance, high energy efficiency, and non-volatility. Thus, SSDs have been considered for several years as a promising alternative to HDDs (hard disk drives). Many studies have made progress in optimizing and modifying HDD-based database management system (DBMS) to suit SSDs. In the case of multiversion databases, which manage not only keys and but also versions, research optimizing SSD query processing has been ignored in comparison with single versioned databases. Generally, the multiversion databases manage an evolving data which is processed in a cyber physical system or an accounting system. Therefore, the data is large and the index structure requires frequent rearrangement of its structure to maximize efficiency, which is called structure modification operation. The multiversion index based on HDDs utilizes random writes to conduct the structure modification operation. This feature can introduce crucial performance problems on SSDs, because the speed of random writes on SSDs is much slower than the speed of sequential writes. We propose a Bulk Split multiversion tree (BSMVBT) index that utilizes sequential pattern I/O and out-of-place updates of SSDs. Experimental results showed that it is 10% – 30% faster than the compared version.