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  • Hacker News
  • 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!
    by RobinL
  • DataFusion is really cool, it's kind of like the LLVM of the OLAP world.
    by theLiminator
  • 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?

    by ratmice
  • 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.

    by adsharma
  • 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

    by cpdomina
  • 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!

    by yadgire7
  • Datafusion is undoubtedly one of the best open source projects of all time, it's so incredibly powerful and well designed.

    The extensibility is insane, you can create your own query language that compiles to logical plans.

    by nylonstrung
  • > "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!

    by chrisweekly

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