Do we need specialized graph databases? Benchmarking real-time social networking applications

Do we need specialized graph databases? Benchmarking real-time social networking applications Pacaci et al., GRADES'17 Today's paper comes from the GRADES workshop co-located with SIGMOD. The authors take an established graph data management system benchmark suite (LDBC) and run it across a variety of graph and relational stores. The findings make for very interesting reading, … Continue reading Do we need specialized graph databases? Benchmarking real-time social networking applications

Mosaic: processing a trillion-edge graph on a single machine

Mosaic: Processing a trillion-edge graph on a single machine Maass et al., EuroSys'17 Unless your graph is bigger than Facebook's, you can process it on a single machine. With the inception of the internet, large-scale graphs comprising web graphs or social networks have become common. For example, Facebook recently reported their largest social graph comprises … Continue reading Mosaic: processing a trillion-edge graph on a single machine

Dependency-driven analytics: a compass for uncharted data oceans

Dependency-driven analytics: a compass for uncharted data oceans Mavlyutov et al. CIDR 2017 Like yesterday's paper, today's paper considers what to do when you simply have too much data to be able to process it all. Forget data lakes, we're in data ocean territory now. This is a problem Microsoft faced with their large clusters … Continue reading Dependency-driven analytics: a compass for uncharted data oceans

Arabesque: A System for Distributed Graph Mining

Arabesque: A System For Distributed Graph Mining - Teixeira et al. 2015 We've studied graph computation systems before in The Morning Paper: systems such as Pregel, Giraph and GraphLab that provide vertex-centric programming models ('think like a vertex') on top of a Bulk Synchronous Parallel compute model. We've also seen some of the limitations of … Continue reading Arabesque: A System for Distributed Graph Mining