Scaling Graph Algorithms: From Prototypes to Production
TL;DR Graph algorithms are memory-bound, not CPU-bound: a 5 billion node, 50 billion edge graph needs 500+ GB just for adjacency, scores, and working memory - it simply does not fit on one machine Four scaling strategies cover most production cases: distributed processing (vertex-cut or edge-cut partitioning), approximate algorithms (sampling, sketching, early stopping), incremental/streaming approaches, and storage-level optimisation (columnar layout, compression, caching) The hidden costs dominate at scale: communication overhead, synchronisation barriers, debugging distributed state, and the operational burden of keeping it all running Full-graph recompute is not feasible at billions of nodes - incremental algorithms and clever approximations are the norm, not the exception Managed services like Neptune Analytics remove much of the partitioning and operations work, at the price of less control Graph algorithms work great on your laptop. PageRank on a 100,000-node graph finishes in seconds. Louvain finds communities instantly. ...