John Brown
Member
Elastic has launched its new anchor text keyword vector storage format , DiskBBQ, as part of its Elasticsearch 9.2 release. The DiskBBQ format is a disk-friendly alternative to the traditional in-memory vector index approach, designed to offer high performance search at lower memory cost.
What DiskBBQ Brings to the Table
- DiskBBQ utilizes a novel vector index technique that uses clustering and compression (via Better Binary Quantization - BBQ) to store and search embedding vectors mainly on disk rather than requiring the entire index in RAM.
- Unlike the commonly used Hierarchical Navigable Small Worlds (HNSW) algorithm which places the full vector graph in memory (leading to high RAM footprints at scale), DiskBBQ partitions vectors into clusters via Hierarchical K-Means and then only loads the relevant clusters at query time.
- Benchmarking by Elastic shows that in memory-constrained environments, DiskBBQ delivers far more graceful performance degradation compared to HNSW. For example, when operating with minimal RAM (~100 MB), DiskBBQ achieved latencies around 15 ms while HNSW failed to run.
- The approach supports massive vector databases by shifting cost burden away from RAM to disk/CPU, enabling large-scale unstructured search, embedding retrieval and AI-driven search workflows.
Why This Matters for Search & AI Applications
- Cost Efficiency: High-performance vector search has traditionally required large in-memory graphs; DiskBBQ significantly cuts RAM requirements, lowering infrastructure costs.
- Scalability: As embedding-based search grows (eg, semantic search, recommendations, agentic AI), platforms need to handle billions of vectors without exploding memory budgets. DiskBBQ offers a scalable path.
- Latency Predictability: Because the clustering and selective loading approach limits how many vectors need to be scored, latency becomes more stable in constrained environments—a key benefit for real-time applications.
- Enabling Use-cases in Resource-Constrained Environments: For deployments at edge, with memory limits or cost constraints, this format expands the opportunity for full-vector retrieval systems.
- Competitive Differentiation: By offering a disk-based vector format, Elastic differentiates its vector search stack and positions Elasticsearch as a more versatile platform for enterprise search, not just log or observability workloads.
Considerations & Implementation Insights
- Recall vs. Cost Trade-off: While ideal for many use-cases, DiskBBQ currently trades a bit of recall (for example, up to ~95%) in exchange for efficiency; very high-recall (>99 %) applications may still favor HNSW.
- Indexing Strategy: Organizations should evaluate indexing performance and memory footprint: DiskBBQ shows up to 10x faster indexing in some scenarios compared to HNSW while using much less memory.
- Hardware & Storage Considerations: While shifting load from RAM to disk and CPU, it remains important to optimize storage medium (eg, high performance SSDs) and caching strategies to make the cluster-loading efficient.
- Migration Path: Teams using HNSW may need to re-index or migrate to take full advantage of DiskBBQ. so planning for transition and verifying recall/latency in real-world datasets is recommended.
- Choosing the Right Format: Organizations should assess their workload - if memory is abundant and very high recall is needed, HNSW may remain preferable; if cost, scale or memory limits are key constraints, DiskBBQ is compelling.
Read related news - https://ittech-news.com/forethought-unveils-browser-agents-ai-automating-web-actions/