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Investing.com -- Elastic NV (NYSE:ESTC) stock rose 3.5% Thursday after the company announced DiskBBQ, a new disk-friendly vector search algorithm in Elasticsearch that promises more efficient vector search at scale compared to traditional industry-standard techniques.
The Search AI Company said its new algorithm eliminates the need to keep entire vector indexes in memory, delivers predictable performance, and reduces costs. DiskBBQ, now available in Elasticsearch 9.2, uses Better Binary Quantization to compress vectors efficiently and cluster them into compact partitions for selective disk reads.
This approach addresses limitations of the commonly used Hierarchical Navigable Small Worlds (HNSW) technique, which requires all vectors to reside in memory - a potentially costly requirement at large scale. By reducing RAM usage and avoiding spikes in data retrieval time, DiskBBQ aims to improve system performance for data ingestion and organization.
"As AI applications scale, traditional vector storage formats force them to choose between slow indexing or significant infrastructure costs required to overcome memory limitations," said Ajay Nair, general manager, Platform at Elastic .
In benchmark testing, DiskBBQ demonstrated sustained query latencies of approximately 15 milliseconds while operating in as little as 100 MB of total memory, where traditional HNSW indexing could not run. The company noted that DiskBBQ’s performance scaled smoothly as available memory increased, without the sharp latency issues typical of in-memory approaches.
The new algorithm is currently available in technical preview in Elasticsearch Serverless.
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