Distributed Knowledge Extraction: The Super-Ingestor

If you are building a Graph RAG system for a global company with 1 billion documents, a standard "Worker Pool" (Module 17) is not enough. You need Distributed Extraction. You need a system that can run across 10 regions, hitting 20 different LLM providers simultaneously, and merging them into a Unified Global Graph without collisions.

In this lesson, we will look at Extreme Scale Ingestion. We will learn how to implement Geo-Distributed Extraction, how to handle Entity Sharding to prevent write-locks on your graph database, and how to use Bloom Filters to quickly check if a fact has already been extracted by a different worker in a different country.

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1. Geo-Distributed Extraction

To minimize latency and handle data residency laws (GDPR), you process the data Where it lives.

• EU Workers: Process 1M PDFs using local Gemini nodes.
• US Workers: Process 5M PDFs using local Azure-OpenAI nodes.
• Sync: They both send their resulting "Triplets" to a central Conflict Resolver (Module 12) before writing to the main graph.

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2. Preventing "Write-Locks" via Sharding

In a graph database, if 100 workers try to update the same "Sudeep" node at the same microsecond, you get a Deadlock.

• The Solution: Group your jobs by Entity ID.
• All jobs related to "Sudeep" go to Worker A.
• All jobs related to "Project Titan" go to Worker B.

This is called Affinity Routing. It ensures that no two workers are ever fighting over the same area of the graph.

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3. The "Bloom Filter" Optimization

Before a worker calls an expensive LLM API to extract facts, it checks a Distributed Bloom Filter (in Redis).

• "Has any other worker already extracted facts from PDF_882?"
• This prevents you from paying for the same AI extraction twice, saving thousands of dollars in a billion-document system.

graph LR
    subgraph "Region: EU"
    E1[Worker] --> G1[Local Graph]
    end
    
    subgraph "Region: US"
    U1[Worker] --> G1
    end
    
    G1 -->|Consolidated| CG[(Central Global Graph)]
    
    style CG fill:#34A853,color:#fff
    note[Distributed workers feed a single source of truth]

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4. Implementation: Affinity Routing in Python

def get_worker_for_entity(entity_name):
    # Use consistent hashing to always map the same name to the same worker
    worker_id = hash(entity_name) % NUM_WORKERS
    return f"worker_queue_{worker_id}"

# This ensures all updates for 'Sudeep' go into ONE queue, preventing write-locks.

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5. Summary and Exercises

Super-ingestion is about Geometry and Concurrency.

• Geo-distribution handles legal constraints and network latency.
• Affinity Routing prevents the graph database from locking up during high-speed writes.
• Caching (Bloom Filters) prevents redundant, expensive AI calls.
• Consistency: A distributed system must have a "Final Resolution" layer to merge conflicting discoveries.

Exercises

1. Scale Problem: You have 1B documents. Each costs $0.01 to process via AI. How much does the initial ingestion cost? How do you use "Bloom Filters" to reduce this for future updates?
2. Affinity Design: If you route jobs by "Company Name," what happens if one company has 90% of your documents? (Hint: The 'Big Tenant' problem).
3. Visualization: Draw two workers in different countries. Draw a single "Redis" box that they both check before starting a job.

In the next lesson, we will look at real-time updates: Real-Time Graph Evolution.