The recent ISO SQL:2023 standard adopts SQL/PGQ (Property Graph Queries), facilitating graph-like querying within relational databases. This advancement, however, underscores a significant gap in how to effectively optimize SQL/PGQ queries within relational database systems. To address this gap, we extend the foundational SPJ (Select-Project-Join) queries to SPJM queries, which include an additional matching operator for representing graph pattern matching in SQL/PGQ. Although SPJM queries can be converted to SPJ queries and optimized using existing relational query optimizers, our analysis shows that such a graph-agnostic method fails to benefit from graph-specific optimization techniques found in the literature. To address this issue, we develop a converged relational-graph optimization framework called RelGo for optimizing SPJM queries, leveraging joint efforts from both relational and graph query optimizations. Using DuckDB as the underlying relational execution engine, our experiments show that RelGo can generate efficient execution plans for SPJM queries. On well-established benchmarks, these plans exhibit an average speedup of 21.90x compared to those produced by the graph-agnostic optimizer.

Distributed processing of large-scale graph data has many practical applications and has been widely studied. In recent years, a lot of distributed graph processing frameworks and algorithms have been proposed. While many efforts have been devoted to analyzing these, with most analyzing them based on programming models, less research focuses on understanding their challenges in distributed environments. Applying graph tasks to distributed environments is not easy, often facing numerous challenges through our analysis, including parallelism, load balancing, communication overhead, and bandwidth. In this article, we provide an extensive overview of the current state-of-the-art in this field by outlining the challenges and solutions of distributed graph algorithms. We first conduct a systematic analysis of the inherent challenges in distributed graph processing, followed by presenting an overview of existing general solutions. Subsequently, we survey the challenges highlighted in recent distributed graph processing papers and the strategies adopted to address them. Finally, we discuss the current research trends and identify potential future opportunities.

Interactive GPM (iGPM) is becoming increasingly important for data scientists to explore graphs in real life, where a series of graph pattern matching (GPM) queries are created and submitted in an interactive manner based on the insights provided by the prior queries. To solve the iGPM problem, three key considerations must be taken into account – performance, usability and scalability – namely if results can be returned in a timely manner, if queries can be written in a declarative way without the need of imperative fine-tune, and if it can work on large graphs. In this paper, we propose the GLogS system that allows users to interactively submit queries using a declarative language. The system will compile and automatically compute optimal execution plans for the queries, and execute them on an existing distributed dataflow engine. In the evaluation, we compare GLogS with the alternatives systems Neo4j and TigerGraph. GLogS outperforms Neo4j by $51\times$ on a single machine due to better execution plans. Additionally, GLogS can scale to handle large graphs with distributed capability. While compared to TigerGraph, GLogS is superior in usability, featuring an optimizer that can automatically compute optimal execution plans, eliminating the need of manual query tuning as required in TigerGraph.

Butterfly (a cyclic graph motif) counting is a fundamental task with many applications in graph analysis, which aims at computing the number of butterflies in a large graph. With the rapid growth of graph data, it is more and more challenging to do butterfly counting due to the super-linear computation complexity and large memory consumption. In this paper, we study I/O-efficient algorithms for doing butterfly counting on hierarchical memory. Existing algorithms of the kind cannot guarantee I/O optimality. Observing that in order to count butterflies, it suffices to ``witness” a subgraph instead of the whole structure, a new class of algorithm called semi-witnessing algorithm is proposed. We prove that a semi-witnessing algorithm is not restricted by the lower bound $\mathrm{\Omega }(\frac{|E{|}^2}{MB})$ of a witnessing algorithm, and give a new bound of $\mathrm{\Omega }(min(\frac{|E{|}^2}{MB},\frac{|E||V|}{\sqrt{M}B}))$. We further develop the IOBufs algorithm that manages to approach the I/O lower bound, and thus claim its optimality. Finally, we make effort to parallelize IOBufs to further improve the performance and scalability. We show in the experiment that IOBufs significantly outperforms the state-of-the-art algorithms EMRC and BFC-EM. In addition, it can scale to graphs of billions of edges even when configuring a small memory (e.g. 10% of the graph size).

Subgraph enumeration is a fundamental problem in graph analytics, which aims to find all instances of a given query graph on a large data graph. In this paper, we propose a system called HUGE to efficiently process subgraph enumeration at scale in the distributed context. HUGE features 1) an optimiser to compute the generic optimal execution plan without the constraints of existing works; 2) a hybrid communication layer that supports both pushing and pulling communication; 3) a novel two-stage execution mode with a lock-free and zero-copy cache design, 4) a BFS/DFS-adaptive scheduler to bound memory consumption, and 5) two-layer intra- and inter-machine load balancing. HUGE is generic such that all existing distributed subgraph enumeration algorithms can be plugged into the system to enjoy automatic speed up and bounded-memory execution.

GAIA is a distributed system designed specifically to make it easy for a variety of users to interactively analyze big graph data on large clusters at low latency. It adopts a high-level language called Gremlin for graph traversal, and provides automatic parallel execution. In particular, we advocate a powerful new abstraction called Scope that caters to the specific needs in this new computation model to scale graph queries with complex dependencies and runtime dynamics, while at the same time maintaining the simple and concise programming model. GAIA has been deployed in production clusters at Alibaba to support a variety of business-critical scenarios. Extensive evaluations using both benchmarks and real-world applications have validated the effectiveness of the proposed techniques, which enables GAIA to execute complex Gremlin traversal with orders-of-magnitude better performance than existing high-performance engines, and at much larger scales than recent state-of-the-art Gremlin-enabled systems such as JanusGraph.

Recently there emerge many distributed algorithms that aim at solving subgraph matching at scale. Existing algorithm-level comparisons failed to provide a systematic view of distributed subgraph matching mainly due to the intertwining of strategy and optimization. In this paper, we identify four strategies and three general-purpose optimizations from representative state-of-the-art algorithms. We implement the four strategies with the optimizations based on the common Timely dataflow system for systematic strategy-level comparison. Our implementation covers all representative algorithms. We conduct extensive experiments for both unlabelled matching and labelled matching to analyze the performance of distributed subgraph matching under various settings, which is finally summarized as a practical guide.

Graph pattern matching is one of the most fundamental problems in graph database and is associated with a wide spectrum of applications. Due to its computational intensiveness, researchers have primarily devoted their efforts to improving the performance of the algorithm while constraining the graphs to have singular labels on vertices (edges) or no label. Whereas in practice graphs are typically associated with rich properties, thus the main focus in the industry is instead on powerful query languages that can express a sufficient number of pattern matching scenarios. We demo PatMat in this work to glue together the academic efforts on performance and the industrial efforts on expressiveness. To do so, we leverage the state-of-the-art join-based algorithms in the distributed contexts and Cypher query language - the most widely-adopted declarative language for graph pattern matching. The experiments demonstrate how we are capable of turning complex Cypher semantics into a distributed solution with high performance.