Distributed graph analytics frameworks must offer low and balanced communication and computation, low preprocessing overhead, low memory footprint, and scalability. 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 iterations faster by using publish-subscribe information flow along directed edges, fetch-once communication, singlepass computation, and in-neighbor storage. Our analytical 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 partitioning time.