Elastic introduced DiskBBQ, a new disk‑friendly vector search algorithm that uses Better Binary Quantization to compress vectors and cluster them into compact partitions, enabling selective disk reads and reducing RAM requirements.
Benchmark tests show DiskBBQ can sustain query latencies of roughly 15 milliseconds while operating with as little as 100 MB of total memory, a performance level that traditional HNSW indexing cannot achieve without larger memory footprints. The algorithm removes the memory bottleneck, allowing Elasticsearch to scale to massive datasets limited only by CPU and disk resources.
DiskBBQ is available in technical preview on Elasticsearch Serverless. The launch follows Elastic’s recent financial performance, with Q4 FY24 revenue of $335 million and Q1 FY25 revenue of $347 million, and reflects the company’s focus on expanding its AI platform and competitive edge in the generative‑AI and vector‑search markets.
Elastic’s CEO Ash Kulkarni highlighted strong demand for its solutions and the importance of innovation velocity, noting that DiskBBQ enhances the platform’s cost‑effectiveness for large‑scale AI applications.
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