Scaling Ingestion with Distributed Systems: High-Throughput Graphs

Extracting a graph from one document is easy. Extracting a graph from 10 million documents—each requiring multiple LLM calls and NLP passes—is a massive compute challenge. If you run your ingestion pipeline as a single script, it will take weeks to complete. You need Parallelism.

In this final lesson of Module 6, we will look at how to scale our ingestion "Factory." We will explore Message Queues (RabbitMQ/Kafka), Distributed Workers (Celery), and the Write-Locking challenges of updating a single graph from 100 different machines at the same time.

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1. The Distributed Ingestion Architecture

In a distributed system, we decouple the "Document Fetcher" from the "Graph Extractor."

1. Fetcher Service: Crawls S3, SharePoint, or APIs and puts raw URLs into a Queue (e.g., RabbitMQ).
2. Worker Cluster: 50 individual machines pull from the queue. Each one processes 1 document (Parsing -> NLP -> LLM).
3. Sync Service: Collects the resulting Cypher scripts and commits them to the Database.

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2. Managing the "Writing Bottleneck"

Unlike a vector database (where adding an entry is independent), a graph database is highly interconnected. If Worker A is creating Node: Sudeep and Worker B is simultaneously trying to add an edge to Node: Sudeep, you may get a Deadlock.

Bottleneck Solutions:

• Batching: Don't write every fact individually. Collect 1,000 facts and commit them in a single transaction.
• Entity Pre-Indexing: Ensure all "Entities" exist (MERGE) before you start adding "Relationships."
• Leader/Follower Writes: One primary node handles the actual graph writes while others handle the compute-heavy LLM extraction.

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3. Rate Limiting the LLM

When scaling to 100 workers, you will hit your LLM API limits (e.g., OpenAI or Anthropic quotas) in seconds.

• The Solution: Implement a Rate Limiter at the queue level. If your quota is 10,000 tokens per minute, your queue should only release enough messages to match that speed.

graph LR
    S3[Document Storage] -->|Path| Q[RabbitMQ Queue]
    Q --> W1[Worker A]
    Q --> W2[Worker B]
    Q --> W3[Worker C]
    W1 -->|JSON| DB[Graph Database Cluster]
    W2 -->|JSON| DB
    W3 -->|JSON| DB
    
    style DB fill:#4285F4,color:#fff
    style Q fill:#f4b400,color:#fff

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4. Handling Partial Failures

In a 10-million document run, 1,000 documents will fail. The network will drop. The LLM will return garbage.

• Dead Letter Queues (DLQ): If a worker fails to process a document, the document isn't lost; it goes into a "Review" queue.
• Idempotency: Your ingestion must be Idempotent. If you run the same document twice, it should NOT create duplicate nodes or edges. (Always use MERGE).

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5. Implementation: A Distributed Skeleton with Python (Celery)

Here is how you would define a "Task" that can be run on 1,000 machines simultaneously.

# tasks.py (Celery Worker)
from celery import Celery

app = Celery('ingest_tasks', broker='pyamqp://guest@localhost//')

@app.task
def process_document(doc_id, content):
    # 1. NLP / LLM extraction
    extracted_graph = call_llm_extractor(content)
    
    # 2. Write to Graph DB
    # Note: Use a connection pool to handle multiple workers
    write_to_neo4j(extracted_graph)
    
    return f"Success for {doc_id}"

# To Scale:
# run 'celery -A tasks worker --concurrency=20' on 5 servers.

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6. Summary and Exercises

Scaling is about managing Flow and Contention.

• Message Queues allow for asynchronous processing.
• Parallel Workers conquer large datasets.
• Batch Transactions minimize database locking.
• Rate Limiting respects API quotas.
• DLQs ensure no document is left behind.

Exercises

1. Quota Math: If your LLM allows 1 million tokens/minute, and your average document is 1,000 tokens, how many workers can you run at full speed?
2. Locking Scenario: What happens if two workers try to change the "Last Active" property of the same node at the exact same millisecond? How does a database handle this?
3. Idempotency Test: Run a script that adds (A)-[:WORKS_AT]->(B) twice. In your database, do you see 1 edge or 2? If you see 2, how do you fix your script?

Congratulations! You have completed Module 6: Data Ingestion and Graph Construction. You now have a working "Graph Factory" that can scale to enterprise levels.

In Module 7: Graph Storage and Infrastructure, we will look at where to store this massive network to ensure it's fast enough for real-time AI.