Yuanyuan Tian

The Proceedings of the ACM on Management of Data (PACMMOD) is concerned with the principles, algorithms, techniques, systems, and applications of database management systems, data management technology, and science and engineering of data. It includes articles reporting cutting-edge data management, data engineering, and data science research. The 2027 SIGMOD conference cycle begins with this issue of PACMMOD --- the fourth issue of Volume 4 of PACMMOD. We thank our predecessors, Carsten Binning and Sudeepa Roy, for their work on the previous issues, and their guidance as we began the process for SIGMOD 2027.

GPUs have shown significant potential for accelerating database analytical queries, but leveraging them for graph analytics queries involving weighted shortest-path computations remains challenging. This stems from the need to efficiently support diverse query patterns that require multi-source, multi-target computations, tracking all shortest paths, ranking paths based on cost, as well as ease of integration with columnar data processing systems. We present GASP, a GPU-accelerated framework for weighted shortest-path graph analytics queries designed to operate within columnar and tensor-based data processing systems. GASP uses a supernode-based reduction of multi-source shortest-path problems to single-source ones, a multi-column predecessors table for tracking shortest paths, and efficient post-processing with GPU-parallel backtracking and sorting. Experimental results show that GASP, running on an H100 GPU, achieves speedups of orders of magnitude over CPU-only relational and graph database systems for shortest-path computations in LDBC SNB BI queries.

In the last two decades, we have been witnessing high demand for graph-based technologies in industry. On the research side, several recent advances have been made about large-scale graph processing, graph analytical systems and graph databases. The landscape of graph query languages is currently evolving with the definition of new standards, and the need for domain-specific languages to express graph algorithmic and analytical primitives will continue and increase in the next future. In this SIGMOD panel, we will discuss the impact of the above changes on the future of graph analytics. Is there a demand for more expressive languages and libraries for analyzing relationships in a graph? Are new hybrid OLTP/OLAP architectures required with improved performance and scalability? What are the graph analytical workloads and benchmarks that users expect on real-world graph applications? What will be the impact of graph ML on graph analytical systems? How to adapt these systems to the dynamic changes that are ubiquitous for graph-shaped data? These and other questions will be addressed in the panel.

Database systems often rely on historical query traces to perform workload-based performance tuning. However, real production workloads are time-evolving, making historical queries ineffective for optimizing future workloads. To address this challenge, we propose SIBYL, an end-to-end machine learning-based framework that accurately forecasts a sequence of future queries, with the entire query statements, in various prediction windows. Drawing insights from real-workloads, we propose template-based featurization techniques and develop a stacked-LSTM with an encoder-decoder architecture for accurate forecasting of query workloads. We also develop techniques to improve forecasting accuracy over large prediction windows and achieve high scalability over large workloads with high variability in arrival rates of queries. Finally, we propose techniques to handle workload drifts. Our evaluation on four real workloads demonstrates that SIBYL can forecast workloads with an 87.3% median F1 score, and can result in 1.7× and 1.3× performance improvement when applied to materialized view selection and index selection applications, respectively.

Large scale analytics engines have become a core dependency for modern data-driven enterprises to derive business insights and drive actions. These engines support a large number of analytic jobs processing huge volumes of data on a daily basis, and workloads are often inundated with overlapping computations across multiple jobs. Reusing common computation is crucial for efficient cluster resource utilization and reducing job execution time. Detecting common computation is the first and key step for reducing this computational redundancy. However, detecting equivalence on large-scale analytics engines requires efficient and scalable solutions that are fully automated. In addition, to maximize computation reuse, equivalence needs to be detected at the semantic level instead of just the syntactic level (i.e., the ability to detect semantic equivalence of seemingly different-looking queries). Unfortunately, existing solutions fall short of satisfying these requirements. In this paper, we take a major step towards filling this gap by proposing GEqO, a portable and lightweight machine-learning-based framework for efficiently identifying semantically equivalent computations at scale. GEqO introduces two machine-learning-based filters that quickly prune out nonequivalent subexpressions and employs a semi-supervised learning feedback loop to iteratively improve its model with an intelligent sampling mechanism. Further, with its novel database-agnostic featurization method, GEqO can transfer the learning from one workload and database to another. Our extensive empirical evaluation shows that, on TPC-DS-like queries, GEqO yields significant performance gains-up to 200x faster than automated verifiers-and finds up to 2x more equivalences than optimizer and signature-based equivalence detection approaches.

We present results of microarchitectural analysis for LDBC SNB BI queries on a relational database engine. We find underutilization of multicore CPUs, inefficient instruction execution, data access overheads at the on-chip cache hierarchy, data TLB overheads, and overall low (but short-term high) memory bandwidth utilization. Using huge pages increased query performance by up to 65% and workload performance by 23%.