● Covered by 1 source · 1 reportMedium impact1 positive
Apache DataFusion Used for Billion-Scale Graph Analytics with Limited RAM
Key points
- Apache DataFusion processed billion-scale graphs using 5-10GB RAM.
- Algorithms were designed for bulk scans and disk offloading.
- PageRank computed on 1 billion edges with 5GB RAM.
- Weakly Connected Components on 2 billion edges with 10GB RAM.
- Challenges traditional view that Spark is needed for such scale.
DataFusion for Large-Scale Graph Processing
An engineer successfully implemented graph map-reduce algorithms using Apache DataFusion, demonstrating its capability to process billion-scale graphs with limited memory. The approach involved offloading computations to disk and designing algorithms to rely on bulk scans rather than random access, leveraging DataFusion's handling of spillover, sort-merge joins, aggregations, planning, and execution.
Performance on Graph Analytics Tasks
The implementation computed PageRank on a directed graph with one billion edges (graph500-26 from the Graphalytics dataset) using 5 GB of memory. Additionally, it identified all weakly connected components in a graph with two billion edges (twitter_mpi from the same dataset) using 10 GB of memory. These results contrast with typical graph algorithms that often require the entire graph to fit into memory, a limitation for tools like NetworkX or Igraph at this scale.
Methodology and Algorithms
For PageRank, a classical Pregel-like bulk-synchronous parallel algorithm was expressed using joins and aggregates within DataFusion, similar to Spark's GraphFrames. For Weakly Connected Components, the implementation was based on the 'In-database connected component analysis' paper by Bögeholz et al., an algorithm previously implemented by the engineer for Spark's GraphFrames.
Implications for Graph Analytics
The findings suggest that Apache DataFusion can be a viable alternative to distributed frameworks like Apache Spark and GraphFrames for billion-scale graph analytics, potentially enabling such computations on single machines or laptops. This shifts the perspective on the hardware and software requirements for processing very large graphs, indicating that efficient algorithm design combined with DataFusion's capabilities can achieve significant scale with modest resources.
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Primary sources
GitHub SemyonSinchenko/graphframes-rs GitHub adityatelange/hugo-PaperMod arXiv 1802.09478Reporting from
An engineer demonstrated using Apache DataFusion to perform graph analytics on billion-scale datasets with minimal RAM, challenging the assumption that such tasks require distributed frameworks like Apache Spark. This approach offloads computations to disk and relies on bulk scans, enabling operations like PageRank on a billion-edge graph with 5GB RAM and Weakly Connected Components on a two-billion-edge graph with 10GB RAM. The findings suggest that DataFusion can handle large-scale graph problems on single machines, potentially reducing infrastructure complexity for certain use cases.