Real-time analysis of social networks leveraging the flink framework

Abstract

In this paper, we present a solution to the DEBS 2016 Grand Challenge that leverages Apache Flink, an open source platform for distributed stream and batch processing. We design the system architecture focusing on the exploitation of parallelism and memory efficiency so to enable an effective processing of high volume data streams on a distributed infrastructure. Our solution to the first query relies on a distributed and fine-grain approach for updating the post scores and determining partial ranks, which are then merged into a single final rank. Furthermore, changes in the final rank are identified so to update the output only if needed. The second query efficiently represents in-memory the evolving social graph and uses a customized Bron-Kerbosch algorithm to identify the largest communities active on a topic. We leverage on an in-memory caching system to keep the largest connected components which have been previously identified by the algorithm, thus saving computational time. The experimental results show that, on a portion of the dataset large half that provided for the Grand Challenge, our system can process up to 400 tuples/s with an average latency of 2.5 ms for the first query, and up to 370 tuples/s with an average latency of 2.7 ms for the second query.