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Graph database management systems (GDBMSs) are highly optimized to performfast traversals, i.e., joins of vertices with their neighbours, by indexing theneighbourhoods of vertices in adjacency lists. However, existing GDBMSs havesystem-specific and fixed adjacency list structures, which makes each systemefficient on only a fixed set of workloads. We describe a new tunable indexingsubsystem for GDBMSs, we call A+ indexes, with materialized view support. Thesubsystem consists of two types of indexes: (i) vertex-partitioned indexes thatpartition 1-hop materialized views into adjacency lists on either the source ordestination vertex IDs; and (ii) edge-partitioned indexes that partition 2-hopviews into adjacency lists on one of the edge IDs. As in existing GDBMSs, asystem by default requires one forward and one backward vertex-partitionedindex, which we call the primary A+ index. Users can tune the primary index orsecondary indexes by adding nested partitioning and sorting criteria. Oursecondary indexes are space-efficient and use a technique we call offset lists.Our indexing subsystem allows a wider range of applications to benefit fromGDBMSs' fast join capabilities. We demonstrate the tunability and spaceefficiency of A+ indexes through extensive experiments on three workloads.

A+ Indexes: Tunable and Space-Efficient Adjacency Lists in Graph Database Management Systems