Hash indexes are widely used in modern dataintensive applications to support efficient query performance. While persistent memory (PM) provides larger capacity and byteaddressability, we identify that PM-based hash indexes suffer from scalability bottlenecks under high concurrency. This stems from NUMA access imposing significant costs, exacerbating the inherently request-agnostic I/O issues in concurrent read/write operations. To this end, we present ALPHA, a highly scalable hash index designed for persistent memory. The key idea with ALPHA lies in exploiting the natural hash-based data access features of hash indexes to reduce I/O irrelevant to data requests. We propose a NUMA-friendly framework to reduce the negative impacts of remote access through the cross-thread delegation mechanism. To further improve throughput, we use two techniques to address read and write issues of the PM-oriented hash index. First, we propose a novel two-layer hash structure to minimize PM access by filtering unnecessary key retrievals during data reads. Second, we devise a partition-based finegrained data access mechanism that enables lock-free concurrent writes without excessive PM write overheads. Our comprehensive experimental evaluation shows that ALPHA outperforms state-of-the-art PM-based hash indexes by up to$22.17 \times$in throughput while reducing tail latency by up to 97.1 %.

The row-oriented store model is the cornerstone component of modern online transaction processing (OLTP) database systems. In response to the massive increase in data within database systems, compression techniques are employed to enhance storage efficiency. Regrettably, current compression methods suffer from either the amplification issue due to coarse compression granularity or inefficient decompression operations, thus usually decreasing the speed of query processing. To this end, we present DPTC, a cost-effective and decompression-transparent approach designed to compress data pages, the basic storage unit of OLTP database systems. Specifically, (1) DPTC applies a row-wise decompression-oriented structure to track the first occurrence of redundant data in compressed data, which effectively supports the decompression of individual records from pages, thereby avoiding unwarranted decompression in record access. Moreover, (2) DPTC employs an in-page dynamic packing strategy, which determines the compression units based on the impact of each data reduction operation on the compression gains and eliminates gains-inefficient data reductions. Furthermore, (3) DPTC utilizes a SIMD-based mechanism that leverages the characteristics of operations within the decompression process to improve the decompression speed. Our evaluation results confirm that DPTC is efficient in terms of decompression speed and compression ratio. Within an OLTP database system, DPTC yields throughput improvements of up to 4.28 x in TPC-C and reduces latency by up to 33.3% for data point queries in a row-oriented storage engine.