Text analytics directly on compression (TADOC) is a promising technology designed for handling big data analytics. However, a substantial amount of DRAM is required for high performance, which limits its usage in many important scenarios where the capacity of DRAM is limited, such as memory-constrained systems. Non-volatile memory (NVM) is a novel storage technology that combines the advantage of reading per-formance and byte addressability of DRAM with the durability of traditional storage devices like SSD and HDD. Unfortunately, no research demonstrates how to use NVM to reduce DRAM utilization in compressed data analytics. In this paper, we propose N-TADOC, which substitutes DRAM with NVM while maintaining TADOC's analytics performance and space savings. Utilizing an NVM block device to reduce DRAM utilization presents two challenges, including poor data locality in traversing datasets and auxiliary data structure reconstruction on NVM. We develop novel designs to solve these challenges, including a pruning method with NVM pool management, bottom-up upper bound estimation, correspondent data structures, and persistence strategy at different levels of cost. Experimental results show that on four real-world datasets, N-TADOC achieves 2.04× performance speedup compared to the processing directly on the uncompressed data and 70.7% DRAM space saving compared to the original TADOC.

Evolving graphs consisting of slices are large and constantly changing. For example, in Alipay, the graph generates hundreds of millions of new transaction records every day. Analyzing the graph within a temporary window is time-consuming due to the heavy merging of slices. Fortunately, we have discovered that most queries exhibit consistent patterns and possess monotonic properties. As a result, transitional results can be computed within slice generation for reuse. Accordingly, we develop MergeGraph enabling window-based monotonic graph analytics with reusable transitional results for pattern-consistent queries. MergeGraph has three advantages over previous works. First, it is the first system specifically tailored for window-based monotonic graph analytics with pattern-consistent queries. Second, it effectively utilizes transitional results from different slices concurrently. Third, MergeGraph boasts a high degree of expressiveness, supporting a broad spectrum of monotonic graph queries. Experimental results demonstrate that MergeGraph delivers significant performance benefits. In evaluating four typical graph applications, MergeGraph achieves an average speedup of 11.30× compared to state-of-the-art methods.

Huge amounts of data have been generated on edge devices every day, which requires efficient data analytics and management. However, due to the limited computing capacity of these edge devices, query processing at the edge faces tremendous pressure. Fortunately, in recent years, hardware vendors have integrated heterogeneous coprocessors, such as GPUs, into the edge device, which can provide much more computing power. Furthermore, the CPU-GPU integrated edge device has shown significant benefits in a variety of situations. Therefore, the exploration of query processing on such CPU-GPU integrated edge devices becomes an urgent need. In this article, we develop a fine-grained query processing engine, called FineQuery, which can perform efficient query processing on CPU-GPU integrated edge devices. Particularly, FineQuery can take advantage of both architectural features of edge devices and query characteristics by performing fine-grained workload scheduling between the CPU and the GPU. Experiments show that on TPC-H workloads, FineQuery reduces 42.81% latency and improves 2.39× bandwidth utilization on average compared to the implementation of using only GPU or CPU. Furthermore, query processing at the edge can bring significant performance-per-cost benefits and energy efficiency. On average, FineQuery at the edge brings 21× performance-per-cost ratio and 4× energy efficiency compared with processing the data on a discrete GPU platform.