Billion-Scale Graph Algorithms on 10GB RAM with Apache DataFusion

The Core Insight: Graphs Are Just Tables

For decades, we’ve treated graph processing as a specialized systems problem. You needed a cluster, a bespoke framework like Pregel or Giraph, and a PhD to tune the JVM garbage collector so you didn’t run out of memory on a graph with a few hundred million edges.

Semyon Sinchenko’s recent experiment blows a hole in that assumption. The core idea is brutally simple: stop thinking about graphs as pointer-chasing data structures and start thinking about them as relational tables. An edge list is just a two-column table (src, dst). A vertex property is just a table with a primary key. Once you accept that, you can leverage the past 20 years of relational query optimization to solve graph problems.

The engine of choice here is Apache DataFusion, an embeddable query engine written in Rust that uses the Arrow columnar memory format. It doesn’t have a built-in graph library. It doesn’t need one. The entire “graph algorithm” is expressed as a recursive SQL query that DataFusion optimizes into a vectorized execution plan.

This isn’t just an academic toy. Sinchenko ran connected components—a foundational graph algorithm used for fraud detection, community discovery, and deduplication—on a 1.1 billion edge graph. The hardware? A single machine with 10GB of RAM.

Why This Breaks the Memory Barrier

The traditional approach to connected components on a massive graph requires holding the entire adjacency structure in memory. Even with compressed representations, a billion-edge graph typically demands 64GB+ of RAM, plus overhead for the iterative processing state.

DataFusion sidesteps this through three mechanisms working in concert:

1. Columnar spilling: Arrow data can be spilled to disk in its native format with zero serialization overhead. When memory pressure hits, DataFusion pushes batches to a temporary directory and streams them back in as needed.

2. Vectorized iteration: Rather than updating one vertex at a time, the algorithm operates on batches of thousands of vertices. Each iteration produces a new column of labels, and the join logic that propagates labels runs at memory-bandwidth speeds thanks to SIMD-optimized kernels.

3. Late materialization: DataFusion defers reading columns that aren’t needed for a particular operation. During label propagation, you’re only touching the label column and the edge endpoints, not pulling in vertex metadata until the final join.

The recursive CTE in DataFusion acts as the iteration controller. Each round, a hash join matches edges against the current labels, an aggregation picks the minimum label for each vertex, and the result becomes the input for the next round. The optimizer can reorder joins, push down filters, and decide when to spill intermediate results based on available memory.

The Algorithm: Label Propagation via SQL

Here’s the actual SQL pattern that replaces hundreds of lines of graph-processing code:

WITH RECURSIVE components AS (
    -- Seed: every vertex starts with its own ID as the component label
    SELECT id AS vertex, id AS component_label
    FROM vertices
    
    UNION ALL
    
    -- Propagation: join edges with current labels, take minimum
    SELECT 
        v.vertex,
        MIN(neighbor.component_label) AS component_label
    FROM components v
    JOIN edges e ON v.vertex = e.src
    JOIN components neighbor ON e.dst = neighbor.vertex
    WHERE v.component_label > neighbor.component_label
    GROUP BY v.vertex
)
SELECT vertex, MIN(component_label) AS final_component
FROM components
GROUP BY vertex;

This reads like textbook graph theory but executes like a database query. The WHERE v.component_label > neighbor.component_label clause is a clever pruning trick: it only propagates labels when the neighbor has a smaller label, which prevents infinite loops and reduces the number of rows processed in each iteration.

For a billion-edge graph, this converges in roughly 5-10 iterations. Each iteration does a full pass over the edge table, but because DataFusion streams the data and only materializes the label column, the working set stays within the 10GB budget.

Benchmarks That Defy Intuition

Sinchenko’s results on a 1.1 billion edge graph (roughly the size of the Twitter follower graph from 2010):

Forty minutes on a laptop-class machine for a graph that would traditionally require a Spark cluster. The secret isn’t in the algorithm—label propagation is well-known—but in the execution engine. DataFusion’s Rust implementation avoids the serialization tax that plagues JVM-based systems. There’s no garbage collection pause when you’re streaming 100 million rows through a hash join.

More importantly, the disk spilling is graceful. When the hash table for the join grows beyond available memory, DataFusion partitions it and spills partitions to disk, processing them one at a time. This is the same technique that makes relational databases handle 1TB+ queries on modest hardware, now applied to graph algorithms.

Getting Your Hands Dirty: A Practical Setup

You don’t need to set up a cluster or learn a new DSL. If you have a graph stored as a Parquet or CSV file, you can run this today with a single Rust binary or Python script.

Python path (via datafusion-python):

import datafusion
from datafusion import SessionContext

ctx = SessionContext()
ctx.register_parquet("edges", "s3://my-bucket/edges.parquet")
ctx.register_parquet("vertices", "s3://my-bucket/vertices.parquet")

result = ctx.sql("""
    WITH RECURSIVE components AS (
        SELECT id AS vertex, id AS component_label FROM vertices
        UNION ALL
        SELECT v.vertex, MIN(n.component_label)
        FROM components v
        JOIN edges e ON v.vertex = e.src
        JOIN components n ON e.dst = n.vertex
        WHERE v.component_label > n.component_label
        GROUP BY v.vertex
    )
    SELECT vertex, MIN(component_label) FROM components GROUP BY vertex
""").collect()

The Python bindings are thin wrappers around the Rust core, so you get the same spilling behavior. For production workloads, you’d run this inside a Rust service that embeds DataFusion directly, avoiding the Python GIL entirely.

For FDEs who need to prototype graph features quickly, this approach is a cheat code. Instead of negotiating for a Spark cluster or wrestling with Neo4j’s resource limits, you can iterate on a laptop against production-scale data. The same Parquet files that feed your data warehouse become your graph source of truth.

If you’re building internal tools that need to answer questions like “which users form dense communities?” or “are there disconnected subgraphs in our infrastructure dependency map?”, this pattern lets you embed those queries directly into a lightweight service. No external graph database required.

The Sharp Edges: Where This Fails

This approach is not a universal graph processing panacea. It excels at algorithms that can be expressed as iterative label propagation: connected components, PageRank, label propagation for community detection, and shortest paths in unweighted graphs.

It fails badly at algorithms that require random access to arbitrary subgraphs. Triangle counting, subgraph isomorphism, and pattern matching all require traversing neighborhoods in ways that don’t vectorize cleanly into SQL joins. You can force them into recursive CTEs, but the iteration count explodes and the optimizer can’t help you.

There’s also a cold-start problem. The first query against a 1TB Parquet file will be slow because nothing is cached. DataFusion doesn’t maintain indexes or materialized views unless you build that infrastructure yourself. For recurring queries, you’ll want to partition your edge table by a key that aligns with your query patterns.

Finally, this is a batch-processing pattern, not a real-time one. If you need to update components as edges stream in, you’re better off with a dedicated graph system or a custom incremental algorithm. DataFusion is an analytical engine; it shines when you’re asking big questions over static snapshots.

From Graph Theory to Field Engineering

Why should an FDE care about recursive SQL on graphs? Because enterprise customers constantly ask questions that reduce to graph algorithms without knowing the terminology.

“Which of our customers are likely part of the same organization?” is connected components on an email domain or payment instrument graph. “What’s the blast radius if this service goes down?” is reachability on an infrastructure dependency graph. “Which documents in our knowledge base are semantically duplicate?” is connected components on a cosine similarity graph.

When a customer asks for these features, the traditional answer involves a graph database migration, a new service, and a six-week timeline. The DataFusion approach lets you prototype the answer in a Jupyter notebook during a customer call, using their existing data exports.

This pattern also aligns with the growing trend of building lightweight AI-powered tools that work with existing infrastructure. The same Parquet files that feed your RAG pipeline can be queried for graph insights without adding a new storage system.

For FDEs who need to debug customer issues involving large-scale data relationships, being able to run graph algorithms locally is a superpower. You’re no longer blocked on a data engineering team to provision a Spark cluster just to check whether a specific user cluster looks anomalous. This fits squarely into the FDE workflow of rapid prototyping and debugging.

FAQ

Q: Can I use this on a graph larger than RAM?

Yes, that’s the entire point. DataFusion spills intermediate hash tables to disk when memory pressure exceeds the configured limit. A 10GB buffer can process a 1TB edge table, though runtime increases linearly with the amount of spilling.

Q: Does this work with weighted edges?

For algorithms like connected components that ignore edge weights, yes. For weighted shortest paths or PageRank with weighted edges, you’d need to modify the aggregation step. It’s possible but requires more complex SQL.

Q: How does this compare to GraphX or Giraph?

GraphX requires a Spark cluster and JVM tuning. This approach runs on a single machine. For graphs under 10 billion edges, the single-machine DataFusion approach often finishes before a Spark cluster finishes provisioning, as outlined in the case for deprecating complex infrastructure when simpler solutions suffice.

Q: What storage format should I use?

Parquet with snappy or zstd compression. The columnar format lets DataFusion skip irrelevant columns, and the compression reduces I/O. Avoid row-based formats like JSON or CSV for graphs over 100M edges.

Q: Can I run this in a serverless function?

For smaller graphs (under 50M edges), yes. The DataFusion runtime starts in under a second and can process a query entirely in memory within a Lambda timeout. For billion-scale graphs, the 40-minute runtime exceeds most serverless limits—use a container or a dedicated instance.

Q: Where can I learn more about the original experiment?

Semyon Sinchenko’s detailed writeup at semyonsinchenko.github.io walks through the full benchmark setup, including the exact DataFusion configuration flags for memory management and spilling behavior. The post includes reproducible code and performance breakdowns across different graph sizes.