Algorithms on billion-scale graph using 10GB RAM: I love DataFusion

It would be nice if OP noted what changed between their old opinion, did datafusion gain some feature that they noted was missing in the previous article, or did something in their understanding click so they could overcome the previous issues?

> "I can compute PageRank on a directed graph with one billion edges (graph500-26 from the Graphalytics dataset) using 5 GB of memory. Alternatively, I can identify all the weakly connected components in a graph with two billion edges (twitter_mpi from the same dataset collection) using 10 GB of memory. Neither NetworkX nor Igraph can do this; most existing graph algorithms require the graph to fit into memory. Previously, I thought you needed Apache Spark and GraphFrames for billion-scale graph analytics. Now, however, I think all you need is a laptop. I have completely changed my old opinion about using Apache DataFusion for graph analytics."

Impressive!

DataFusion is really cool, it's kind of like the LLVM of the OLAP world.

Does it support out-of-core or multi-processor processing?

Really cool visualization, amazing how it resembles a neural network.