Efficient Single Step Traversals in Main-Memory Graph-Shaped Data

Management of graph-shaped data gained a momentum to both industry and research. Traversal queries through a graph-shaped dataset are easy to express, and can be efficiently executed using graph databases. High-performance traversals through graphshaped data is claimed to be enabled by native graph storage (i.e., encoding data using graph data structures), and native graph processing (i.e., operating on data with graphdomain specific operations). A common belief is that native graph storage databases are inherently superior to non-native graph storage databases (e.g., relational databases) in terms of traversal efficiency. This claim is especially supported by graph database vendors, but not yet proven or disproven objectively. In this work, we study in context of main-memory systems how the primitives of arbitrary traversal algorithms (i.e., single step traversal queries) are affected by native graph storage, and non-native graph storage in terms of execution performance. We focus on single step traversal queries that address navigation in graph-shaped data. We compare classic graph encoding and a state-of-the-art graph database micro-index as representatives of native graph storage, and table scanning and indexing by several binary search trees as representatives of non-native graph storage. We evaluate the representatives for native and non-native graph storage on both artificial datasets, and real world graph datasets. To be aware of confounding variables, we implement a unified main-memory-only experimental query engine to avoid bias from internal behavior of some blackbox systems (e.g., main-memory systems vs. disk-based systems). Our experimental results show that high efficient traversal algorithm in main-memory systems require indexing adjacent records and incident relationships rather than the property of being a native graph storage or a non-native graph storage.