Graph search is highly needed in applications over graphs. Specifically, graph search seeks a sub-graph(s) meeting the specific purposes, such as the shortest path between two nodes, the minimal spanning tree, the salesman traveling path, and the like. We also observe that these graphs are always exceedingly large and keep growing at a fast rate. Relational Database (RDB) provides a promising infra-structure to support graph search. After more than 40 years of development, RDB is mature and stable enough, and plays a key role in information systems. This abstract takes the shortest path discovery to study efficient relational approaches to graph search queries. We first abstract three enhanced relational operators, based on which we introduce an FEM framework to bridge the gap between relational operations and graph operations. We show new features introduced by recent SQL standards, such as window function and merge statement, can improve the performance of the FEM framework. Second, we propose an edge weight aware graph partitioning schema and design a bi-directional restrictive BFS (breadth-first-search) over partitioned tables, which improves the scalability and performance without extra indexing overheads. The final extensive experimental results illustrate our relational approach with optimization strategies can achieve high scalability and performance.
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