> "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!

Hello, I am new to hacker news and finding it really resourceful. I found this article interesting (having learnt KG and Map Reduce (spark) as part of my masters' course), appreciate the effort to post this.

I am here to seek guidance from the community. I want to refresh my memory on knowledge graphs and algorithms for Big Data Mining and Processing.

I believe KG can solve problems on Agent attacks (LLM agency) in real-time - so want to build knowledge around the topic.

Interested to join any interest/ discussion groups if any. Thanks!

Love this Sem. Will provide a great alternative to the SQL based connected components algorithm we ship in Splink. Looking forward to testing how much faster it is. Thanks for your work on it!

cool! you might be interested in graphchi (2012), also designed to do large scale graph operations on a single machine

https://github.com/GraphChi/graphchi-cpp#performance

Related, we recently release the polars version of GFQL, the only oss cypher property graph query engine for CPU+GPU, and even better, no database nor outside process needed. We started doing LDBC benchmarks vs neo4j, memgraph, kuzu, etc, and are already starting to outperform them both on latency for small OLTP graph searches and $, speed for big graph OLAP ones, especially in GPU mode.

The cool in the original post was directly inspired by our work here, with our advocacy to the author of keeping their previous Spark work for initial data lake data extraction, and the actual graph work to be redone in our columnar in-memory optimized style for magnitudes of speedup , cost savings

Pip install, benchmarks : https://pygraphistry.readthedocs.io/en/latest/gfql/benchmark...

The idea of graph algorithms on Apache arrow at scale originated here. 100+ graph algorithms running on columnar memory.

https://github.com/Ladybug-Memory/icebug

Out of core with datafusion is the main innovation here in graphframes-rs. But it has only 2 algorithms so far.

Icebug and LadybugDB can be tightly integrated to efficiently move tables encoded as compressed sparse row (CSR) into arrow memory.

Jupyter notebooks available.

It would be nice if OP noted what caused the change in their 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?

It's hard to take the article seriously when it has quotes like this: "The hardest part. 2B edges twitter graph is already huge (its edges are 30 GB in CSV !!!)."

Who cares how big the graph is in CSV? That's not the representation you operate over in big data.

All of this would have easily fit in memory on any reasonable modern system.

3.4 gb dataset on 10gb ram

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