The case against specialized graph analytics engines
Jing‐Li Fan, Adalbert Gerald, S. Deepak Raj, Jignesh M. Patel · 2015
Graph analytic processing has started to become a nearly ubiquitous component in the enterprise data analytics ecosys-tem. In response to this growing need, various specialized graph processing engines have been created in recent years. Sadly, the use of relational database management systems (RDBMSs) for graph processing is largely ignored in most enterprise settings. This oversight is surprising since in most enterprise settings, RDBMSs are already present and used for a variety of other analytic tasks. This situation then begs the question of whether the use of RDBMS for graph pro-cessing is fundamentally lacking in some respect compared to the specialized graph processing engines. In this paper, we aim to address this question both from the programmer productivity perspective and from the performance perspec-tive. We present Grail – a syntactic layer for querying graph in a vertex-centric way in an RDBMS, which can be com-piled to translate graph queries to SQL. In a single node setting, we also compare Grail to GraphLab and Giraph, and examine the performance implications of using Grail, showing that the RDBMS engine is competitive to these specialized engines. Given that RDBMSs are ubiquitous in enterprise settings, and have a robust and mature technol-ogy that has been hardened over decades, and are part of existing administrative methods in place, we argue that it is time to reconsider if specialized graph engines have a role to play in most enterprises. 1.