Vector DB Persistence and Scalability
Learn about strategies for ensuring data durability, handling large datasets, and scaling vector databases for production workloads.
Data That Stays: Persistence
In this lesson, we'll explore two crucial concepts for any production-ready vector database: Persistence and Scalability.
Imagine building a powerful search engine. You wouldn't want to lose all your indexed data every time the system restarts, right? That's where persistence comes in!
What is Persistence?
Persistence means your data survives even if the application or server shuts down. It's saved to a durable storage like a disk, not just kept in temporary memory.
- Why it matters: Prevents data loss.
- Without it: All your carefully generated vector embeddings would vanish on restart.
- Goal: Ensure data durability and reliability.
All lessons in this course
- Vector DB Storage Architectures
- Proximity Search Algorithms (HNSW, IVFFlat)
- Vector DB Persistence and Scalability
- Quantization and Compression of Vectors