July 31, 2026
Big Graph Energy
Algorithms on billion-scale graph using 10GB RAM: I love DataFusion
This coder says a laptop can handle giant web maps—and the comments are losing it
TLDR: A developer says they processed gigantic link networks on a laptop-class setup with only 5GB to 10GB of memory, challenging the idea that you need huge server systems. Commenters split between amazement, tough questions about what changed, and classic nerd hype over whether this tool is secretly a big deal.
A developer just dropped a very online flex: they say a regular laptop can crunch massive social-style link maps with surprisingly little memory, doing jobs that usually sound like they belong in a giant data center. The big boast is simple enough for non-specialists: one test handled a graph with 1 billion links using 5GB of memory, and another found clusters in a 2 billion-link dataset using 10GB. Translation: absurdly large network analysis on hardware that sounds almost normal. And yes, the author admits it is not all sunshine—there are crashes, deadlocks, and some stubborn performance quirks—but the core message landed hard: it works.
The comments, though, are where the real fireworks are. One camp was instantly impressed, with people calling DataFusion “the LLVM of the OLAP world,” which is peak nerd praise for “this could become foundational.” Another commenter got distracted in the best way possible, saying the visualization looked like a neural network, basically turning the whole post into accidental tech art. But not everyone was ready to clap on cue. One of the sharpest reactions came from a user asking what exactly changed since the author’s earlier skepticism: did the software improve, or did the author just have a breakthrough? That question added a nice little receipts, please energy to the thread. And then there was the practical crowd asking the least glamorous but most revealing question of all: can it go beyond one machine? In other words, the community vibe was a perfect mix of hype, side-eye, and “okay, but show your work.”
Key Points
- •The article reports a graph map-reduce implementation in Apache DataFusion that spills data to disk and uses bulk scans to operate under strict RAM limits.
- •PageRank was run on the Graphalytics graph500-26 dataset with 32,804,978 nodes and 1,051,922,853 edges using a 5 GB memory limit.
- •Weakly connected components were computed on the Graphalytics twitter_mpi dataset with 52,579,682 nodes and 1,963,263,821 edges using a 10 GB memory limit.
- •The author says tests were enforced with systemd-run hard memory caps and notes issues including FairSpillPool deadlocks and lack of reuse of pre-sorted disk data for sort-merge joins.
- •For PageRank, the article reports roughly 30 minutes for 15 iterations and states the results matched ground truth within 0.0001 tolerance.