A performance evaluation of open source graph databases

Robert McColl, David Ediger, Jason Poovey, Dan Campbell, David A. Bader

Research output: Contribution to conferencePaperpeer-review

61 Scopus citations


With the proliferation of large, irregular, and sparse relational datasets, new storage and analysis platforms have arisen to fill gaps in performance and capability left by conventional approaches built on traditional database technologies and query languages. Many of these platforms apply graph structures and analysis techniques to enable users to ingest, update, query, and compute on the topological structure of the network represented as sets of edges relating sets of vertices. To store and process Facebook-scale datasets, software and algorithms must be able to support data sources with billions of edges, update rates of millions of updates per second, and complex analysis kernels. These platforms must provide intuitive interfaces that enable graph experts and novice programmers to write implementations of common graph algorithms. In this paper, we conduct a qualitative study and a performance comparison of 12 open source graph databases using four fundamental graph algorithms on networks containing up to 256 million edges. Copyright is held by the owner/author(s).

Original languageEnglish (US)
Number of pages7
StatePublished - 2014
Externally publishedYes
Event2014 1st Workshop on Parallel Programming for Analytics Applications, PPAA 2014 - Orlando, FL, United States
Duration: Feb 16 2014Feb 16 2014


Other2014 1st Workshop on Parallel Programming for Analytics Applications, PPAA 2014
Country/TerritoryUnited States
CityOrlando, FL

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software


  • Graph algorithms
  • Graph databases
  • Relational databases


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