LFGraph: Simple and Fast Distributed Graph Analytics∗

Distributed graph analytics frameworks must offer low and balanced communication and computation, low pre-processing overhead, low memory footprint, and scala-bility. We present LFGraph, a fast, scalable, distributed, in-memory graph analytics engine intended primarily for directed graphs. LFGraph is the first system to satisfy all of the above requirements. It does so by relying on cheap hash-based graph partitioning, while making iter-ations faster by using publish-subscribe information flow along directed edges, fetch-once communication, single-pass computation, and in-neighbor storage. Our analyt-ical and experimental results show that when applied to real-life graphs, LFGraph is faster than the best graph analytics frameworks by factors of 1x–5x when ignoring partitioning time and by 1x–560x when including parti-tioning time.