Graph Processing on an “almost ” Relational Database

It would be hard to disagree with the contention that graph processing (whether it be of connections between people, shopping habits,...) allows the creation of valuable data-driven products and insights. There is less consensus on the systems that make it easy to analyze these graphs. In this paper, we argue that a relational database (actually, a close approximation to one) is well suited for many graph pro-cessing applications. We restrict our claims to the following case, which, we believe, dominates the nature of much data analyses — the data does not change during the analysis in response to external events. We present representative ex-amples from our work at LinkedIn, the world’s largest pro-fessional network. We present performance results for these examples using Q, a single-node, analytical database