How AI Is Built

#030 Vector Search at Scale, Why One Size Doesn't Fit All

Nov 7, 2024

Join Charles Xie, founder and CEO of Zilliz and pioneer behind the Milvus vector database, as he unpacks the complexities of scaling vector search systems. He discusses why vector search slows down at scale and introduces a multi-tier storage strategy that optimizes performance. Charles reveals innovative solutions like real-time search buffers and GPU acceleration to handle massive queries efficiently. He also dives into the future of search technology, including self-learning indices and hybrid search methods that promise to elevate data retrieval.

36:26

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Episode guests

Charles Xie

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Quick takeaways

The podcast discusses the importance of a multi-tier storage strategy, balancing speed and cost to optimize vector database performance.

Charles Xie emphasizes the significance of real-time search solutions and customizable trade-offs between cost, latency, and search relevance in scalable systems.

Deep dives

Challenges in Search Systems

Search systems face significant challenges due to the sheer volume of data often exceeding the capacity of a single node. Handling numerous queries per second, the complexities of building indices and maintaining performance while searching fresh data presents further difficulties. Trade-offs between cost, latency, data freshness, and scalability are essential considerations for developers. Solutions like Milviz allow for these trade-offs by providing options to manage data storage effectively based on specific application needs.

Intro

2min

Scaling Vector Databases

17min

Optimizing Colbert Calculations for Large Scale Performance

2min

Advancements in Vector Search Technology

6min

Building Efficient Vector Database Systems: Balancing Performance and Cost

4min

Navigating the Balance between Relevancy and Latency in Vector Search Systems

5min

Ever wondered why your vector search becomes painfully slow after scaling past a million vectors? You're not alone - even tech giants struggle with this.

Charles Xie, founder of Zilliz (company behind Milvus), shares how they solved vector database scaling challenges at 100B+ vector scale:

Key Insights:

Multi-tier storage strategy:
- GPU memory (1% of data, fastest)
- RAM (10% of data)
- Local SSD
- Object storage (slowest but cheapest)
Real-time search solution:
- New data goes to buffer (searchable immediately)
- Index builds in background when buffer fills
- Combines buffer & main index results
Performance optimization:
- GPU acceleration for 10k-50k queries/second
- Customizable trade-offs between:
  - Cost
  - Latency
  - Search relevance
Future developments:
- Self-learning indices
- Hybrid search methods (dense + sparse)
- Graph embedding support
- Colbert integration

Perfect for teams hitting scaling walls with their current vector search implementation or planning for future growth.

Worth watching if you're building production search systems or need to optimize costs vs performance.

Charles Xie:

Nicolay Gerold:

00:00 Introduction to Search System Challenges 00:26 Introducing Milvus: The Open Source Vector Database 00:58 Interview with Charles: Founder of Zilliz 02:20 Scalability and Performance in Vector Databases 03:35 Challenges in Distributed Systems 05:46 Data Consistency and Real-Time Search 12:12 Hierarchical Storage and GPU Acceleration 18:34 Emerging Technologies in Vector Search 23:21 Self-Learning Indexes and Future Innovations 28:44 Key Takeaways and Conclusion

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How AI Is Built

#030 Vector Search at Scale, Why One Size Doesn't Fit All

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Challenges in Search Systems

Scalability and Performance Solutions

Advancements in Indexing and Data Storage

Remember Everything You Learn from Podcasts