Batch Ingestion: Data Loading at Scale

Loading 100 vectors is easy. Loading 10 Million vectors is a distributed systems challenge. If you try to upload them one by one, your overhead from HTTP requests and database commits will make the process take weeks.

In this lesson, we learn the High-Throughput Ingestion patterns used by production data engineers.

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1. The Power of Batching

Instead of one request per vector, you group your vectors into Batches.

• Why?: It reduces network round-trips and allows the database to perform bulk IO operations.
• The Ideal Size: Usually between 100 and 1,000 vectors per batch. Too small is inefficient; too large causes memory errors or request timeouts.

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2. Parallel Processing

If you have a 16-core CPU, you shouldn't be running ingestion on a single thread.

The Pattern:

1. Producer: Reads raw data and generates embeddings.
2. Queue: Holds batches of (Vector + Metadata).
3. Consumers: Multiple threads/workers that push batches to the Vector DB.

graph LR
    D[Raw Data] --> P[Embedder Pool]
    P --> Q[Batch Queue]
    Q --> W1[Worker A]
    Q --> W2[Worker B]
    Q --> W3[Worker C]
    W1 --> V[(Vector DB)]
    W2 --> V
    W3 --> V

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3. Implementation: Efficient Batching (Python)

Using the pinecone-client as an example of an upsert loop:

import pinecone
from pinecone.core.client.models import Vector

def chunks(lst, n):
    for i in range(0, len(lst), n):
        yield lst[i:i + n]

# 1. Prepare your data list
my_vectors = [("id1", [0.1, 0.2...], {"meta": "data"}), ...]

# 2. Upsert in chunks
index = pinecone.Index("my-index")
for batch in chunks(my_vectors, 100):
    index.upsert(vectors=batch)
    print(f"Processed batch...")

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4. Handling Ingestion Errors

In large datasets, failures are guaranteed.

1. Network blips: Use exponential backoff (Module 9.4).
2. Malformed Data: Validate your metadata schemas before embedding to avoid wasting money on broken records.
3. Idempotency: Ensure that re-running an ingestion script doesn't create duplicate vectors. (Always use unique ids based on the content hash).

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5. Summary and Key Takeaways

1. Batching: Group vectors into chunks of 100-500 for optimal throughput.
2. Parallelize: Use multiple workers to maximize your CPU and Network bandwidth.
3. Monitor Latency: Watch your "Upsert Latency." If it spikes, your batches might be too large.
4. Content-Addressable IDs: Use UUIDs or hashes to ensure stability during retries.

In the next lesson, we’ll switch focus to the query side: Query Latency Optimization.

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Congratulations on completing Module 14 Lesson 2! You are now move data at scale.