Graph processing has become an important part of multiple areas of computer science, such as machine learning, computational sciences, medical applications, social network analysis, and many others. Numerous graphs such as web or social networks may contain up to trillions of edges. Often, these graphs are also dynamic (their structure changes over time) and have domain-specific rich data associated with vertices and edges. Graph database systems such as Neo4j enable storing, processing, and analyzing such large, evolving, and rich datasets. Due to the sheer size of such datasets, combined with the irregular nature of graph processing, these systems face unique design challenges. To facilitate the understanding of this emerging domain, we present the first survey and taxonomy of graph database systems. We focus on identifying and analyzing fundamental categories of these systems (e.g., triple stores, tuple stores, native graph database systems, or object-oriented systems), the associated graph models (e.g., RDF or Labeled Property Graph), data organization techniques (e.g., storing graph data in indexing structures or dividing data into records), and different aspects of data distribution and query execution (e.g., support for sharding and ACID). 45 graph database systems are presented and compared, including Neo4j, OrientDB, or Virtuoso. We outline graph database queries and relationships with associated domains (NoSQL stores, graph streaming, and dynamic graph algorithms). Finally, we describe research and engineering challenges to outline the future of graph databases.

Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries.

Enterprises are creating domain-specific knowledge graphs by curating and integrating their business data from multiple sources. Ontologies provide a semantic abstraction for such knowledge graphs to describe their data in terms of the entities involved and their relationships. There has been a lot of effort to build systems that enable efficient querying over knowledge graphs, represented as property graphs. However the problem of schema optimization in the property graph setting has been largely ignored. In this work, we show that graph schema design has significant impact on query performance, and propose two algorithms to generate an optimized property graph schema from the domain ontology. To the best of our knowledge, we are the first to present an ontology-driven approach for property graph schema optimization. The rich semantic relationships in an ontology contain a variety of opportunities to reduce edge traversals and consequently improve the graph query performance. Our experimental study with two real-world knowledge graphs shows that our algorithms produce high-quality schemas, achieving up to 2 orders of magnitude speed-up compared to alternative schema designs.

Property Graph Schema Optimization for Domain-Specific Knowledge Graphs

We introduce LSQB, a new large-scale subgraph query benchmark. LSQB tests the performance of database management systems on an important class of subgraph queries overlooked by existing benchmarks. Matching a labelled structural graph pattern, referred to as subgraph matching, is the focus of LSQB. In relational terms, the benchmark tests DBMSs' join performance as a choke-point since subgraph matching is equivalent to multi-way joins between base Vertex and base Edge tables on ID attributes. The benchmark focuses on read-heavy workloads by relying on global queries which have been ignored by prior benchmarks. Global queries, also referred to as unseeded queries, are a type of queries that are only constrained by labels on the query vertices and edges. LSQB contains a total of nine queries and leverages the LDBC social network data generator for scalability. The benchmark gained both academic and industrial interest and is used internally by 5+ different vendors.

https://dl.acm.org/doi/pdf/10.1145/3461837.3464516

LSQB: a large-scale subgraph query benchmark

Enterprises are creating domain-specific knowledge graphs by curating and integrating their business data from multiple sources. The data in these knowledge graphs can be described using ontologies, which provide a semantic abstraction to define the content in terms of the entities and the relationships of the domain. The rich semantic relationships in an ontology contain a variety of opportunities to reduce edge traversals and consequently improve the graph query performance. Although there has been a lot of effort to build systems that enable efficient querying over knowledge graphs, the problem of schema optimization for query performance has been largely ignored in the graph setting. In this work, we show that graph schema design has significant impact on query performance, and then propose optimization algorithms that exploit the opportunities from the domain ontology to generate efficient property graph schemas. To the best of our knowledge, we are the first to present an ontology-driven approach for property graph schema optimization. We conduct empirical evaluations with two real-world knowledge graphs from medical and financial domains. The results show that the schemas produced by the optimization algorithms achieve up to 2 orders of magnitude speed-up compared to the baseline approach.

Triangles are an important pattern in large-scale graph analysis for their practical use in many real-life applications. However, with the expansion of networks, maintaining a balanced computational load is challenging especially for problems like triangle computations because of skewed vertices. On the other hand, there is a huge amount of data in database management systems (DBMSs) that can be modeled and analyzed as graphs. With these motivations in mind, we developed PandaSQL, a novel approach using SQL queries to enumerate all the triangles in a given graph based on Randomized Triangle Enumeration Algorithm. Our approach is elegant, abstract, and short compared to traditional languages like C++ or Python. Moreover, our partitioning queries ensures perfect load balancing. Thus, the triangle enumeration is independent, local, and parallel.

PandaSQL: Parallel Randomized Triangle Enumeration with SQL Queries

To meet the challenge of analyzing rapidly growing graph and network data created by modern applications, a large number of graph databases have emerged, such as Neo4j and JanusGraph. They mainly target low-latency graph queries, such as finding the neighbors of a vertex with certain properties, and retrieving the shortest path between two vertices. Although many of the graph databases handle the graph-only queries very well, they fall short for real life applications involving graph analysis. This is because graph queries are not all that one does in an analytics workload of a real life application. They are often only a part of an integrated heterogeneous analytics pipeline, which may include SQL, machine learning, graph, and other analytics. This means graph queries need to be synergistic with other analytics. Unfortunately, most existing graph databases are standalone and cannot easily integrate with other analytics systems. In addition, many graph data (data about relationships between objects or people) are already prevalent in existing non-graph databases, although they are not explicitly stored as graphs. None of existing graph databases can retrofit graph queries onto these existing data without transferring or transforming data. In this paper, we propose an in-DBMS graph query approach, IBM Db2 Graph, to support synergistic and retrofittable graph queries inside the IBM Db2 relational database. It is implemented as a layer inside Db2, and thus can support integrated graph and SQL analytics efficiently. Db2 Graph employs a novel graph overlay approach to expose a graph view of the relational data. This approach flexibly retrofits graph queries to existing graph data stored in relational tables, without expensive data transfer or transformation. In addition, it enables efficient execution of graph queries with the help of Db2 relational engine, through sophisticated compile-time and runtime optimization strategies. Our experimental study, as well as our experience with real customers using Db2 Graph, showed that Db2 Graph can provide very competitive and sometimes even better performance on graph-only queries, compared to existing graph databases. Moreover, it optimizes the overall performance of complex analytics workloads.

Huijuan Peng

Papers

IBM Db2 Graph: Supporting Synergistic and Retrofittable Graph Queries Inside IBM Db2