Algorithms on billion-scale graph using 10GB RAM: I love DataFusion
Algorithms on billion-scale graph using 10GB RAM: I love DataFusion!July 5, 2026 · 7 min · Sem SinchenkoTLDR;I implemented a graph map-reduce using Apache DataFusion. Where possible, I offloaded everything to disk, and designed the algorithms to rely on bulk scans rather than random access. DataFusion handles spillover, sort-merge joins, aggregations, planning and execution, so my code is very lightweight.
- ▪Algorithms on billion-scale graph using 10GB RAM: I love DataFusion!July 5, 2026 · 7 min · Sem SinchenkoTLDR;I implemented a graph map-reduce using Apache DataFusion.
- ▪Where possible, I offloaded everything to disk, and designed the algorithms to rely on bulk scans rather than random access.
- ▪DataFusion handles spillover, sort-merge joins, aggregations, planning and execution, so my code is very lightweight.
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Record
| Original publisher | Sem Sinchenko |
| Canonical URL | https://semyonsinchenko.github.io/ssinchenko/post/datafusion-graphs-cc-2/ |
| Publication time | Fri, 31 Jul 2026 15:53:37 +0000 |
| Retrieval time | 2026-07-31T16:23:06.271Z |
| Last seen | 2026-07-31T16:23:06.271Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | OpgbCi6VqafL · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Algorithms on billion-scale graph using 10GB RAM: I love DataFusion!July 5, 2026 · 7 min · Sem SinchenkoTLDR;I implemented a graph map-reduce using Apache DataFusion. Where possible, I offloaded everything to disk, and designed the algorithms to rely on bulk scans rather than random access. DataFusion handles spillover, sort-merge joins, aggregations, planning and execution, so my code is very lightweight. I tested it in strict mode by running it via systemd-run with a hard memory limit. It works. Of course, I have encountered some issues: for example, I frequently experience deadlocks from FairSpillPool in extreme scenarios, and I have not yet found a way to make SMJ use pre-sorting of the data on disk. But it works.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Sem Sinchenko.
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