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Why Vector DBs are used instead of traditional databases to store vector data

Two core reasons why vector databases are fundamentally different from traditional relational databases.


✅ 1. Binary Search Doesn’t Scale to High Dimensions

Traditional Indexing (Relational DBs):

  • Uses B-trees or hash indexes on 1D scalar fields (like integers, names, timestamps).
  • Search is fast because of ordering — e.g., binary search in a sorted list is O(log n).

Problem in Vector Space:

  • A 2D+ vector doesn’t have a linear ordering.
  • In 768D space, you can’t “sort” vectors or do binary search because:
    • There’s no concept of greater/lesser in multiple directions.
    • Similarity is measured by angles or distances, not order.

Hence, binary search, B-trees, and hash indexes are useless here.


🧠 What Vector DBs Do Instead:

They use Approximate Nearest Neighbor (ANN) algorithms like:

  • HNSW (Hierarchical Navigable Small World)
  • IVF (Inverted File Index)
  • PQ (Product Quantization)

These algorithms structure the vector space in a way that lets you quickly find the most similar vectors without scanning them all — even in millions.


✅ 2. ACID Compliance Is Overkill for Vector Workloads

Relational DBs:

  • Built for data integrity, with strict ACID guarantees:
    • Atomicity: all-or-nothing transactions
    • Consistency, Isolation, Durability

Great for:

  • Banking systems
  • Inventory tracking
  • Critical business apps

Vector DBs:

  • Most use cases don’t need full ACID:
    • Embedding search
    • Semantic recommendation
    • Similar image/doc retrieval

These are mostly read-heavy, and eventual consistency or light durability is enough.

Plus, enforcing ACID would slow down high-throughput similarity search.


🔚 So to summarize your point, expanded:

✅ Relational DBs are great for structured, exact-match data with high consistency needs.
❌ They don’t scale to vector math — because there’s no sorting, no scalar index, and no need for strict ACID guarantees.

Meanwhile:

✅ Vector DBs are optimized for fast, similarity-based retrieval, often at web-scale, where approximate is good enough and speed is king.

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