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Embeddings & Similarity Search • Real-time Updates (Incremental Indexing)Medium⏱️ ~2 min

What is Real-Time Incremental Indexing?

Definition
Real-time incremental indexing is the process of updating a vector index as new items arrive, rather than rebuilding the entire index from scratch.
WHY BATCH REBUILDS FAIL
A batch rebuild approach means regenerating the complete index whenever content changes. For a 100M vector index, this takes 2-8 hours. If new products are added hourly, batch rebuilds create an impossible backlog. Users searching for a product uploaded 5 minutes ago would find nothing.
The fundamental tension: indexes built for fast search (like IVF or HNSW) are optimized for static data. Their internal structures (cluster centroids, graph edges) assume the data distribution is fixed. Adding vectors incrementally can degrade these structures.
FRESHNESS REQUIREMENTS BY USE CASE
E-commerce listings: New products must be searchable within minutes. A seller uploading inventory expects immediate visibility. Delay = lost sales.
News/content: Breaking news needs to appear in search within seconds to minutes. A 6-hour rebuild cycle makes the system useless for current events.
Social media: Posts should be searchable nearly instantly. Users expect to find content they just saw in their feed.
Catalog updates: Weekly refresh is acceptable for stable catalogs like movie libraries. Here, batch rebuilds work fine.
THE INCREMENTAL CHALLENGE
Vector indexes are not designed for modification. HNSW builds a graph where each node connects to neighbors. Adding a new node requires finding its neighbors, which requires searching the index. If you add thousands of nodes per second, search performance degrades.
💡 Key Insight: Incremental indexing trades index optimality for freshness. The index gradually becomes suboptimal until a periodic rebuild restores quality.

💡 Key Takeaways

✓Batch rebuilds (2-8 hours for 100M vectors) create unacceptable freshness delays for real-time use cases

✓Vector indexes like HNSW and IVF are optimized for static data—adding vectors incrementally degrades their structure

✓Freshness requirements vary: e-commerce needs minutes, news needs seconds, catalogs can tolerate weekly rebuilds

📌 Interview Tips

1Interview Tip: Start by explaining WHY incremental indexing is needed—batch rebuilds are too slow for dynamic content.

2Interview Tip: Give concrete freshness requirements for different use cases to show you understand the business context.

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