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Endigest AI Core Summary
Databricks redesigned its Vector Search from scratch to support billion-scale workloads by decoupling storage from compute.
•Storage Optimized endpoints store vector indexes in cloud object storage and load them into memory only at query time, reducing serving costs by up to 7x compared to fully in-memory approaches.
•IVF (Inverted File Index) was chosen over HNSW because IVF partitions vectors into independently fetchable clusters, enabling partial loads from object storage without keeping the full graph in memory.
•Distributed K-means clustering is implemented as native PySpark jobs using JAX for hardware-accelerated matrix operations, scaling linearly by adding Spark executors.
•Ingestion runs on ephemeral serverless Spark clusters completely isolated from the query path, preventing write-heavy workloads from degrading query latency.
•The query layer uses a Rust engine with a dual-runtime architecture separating async I/O and CPU-bound vector computation into distinct thread po
This summary was automatically generated by AI based on the original article and may not be fully accurate.
Databricks
Cloudflare
Google Cloud