Kuaishou and Tsinghua University Win NeurIPS'21 Billion-Scale ANN Challenge with FAISS‑Optimized KST_ANN Solution

On December 6, Kuaishou and Tsinghua University’s joint team secured first place in the NeurIPS'21 Billion‑Scale Approximate Nearest Neighbor Search Challenge by leveraging a FAISS‑optimized, memory‑efficient KST_ANN algorithm that achieved over 6% higher recall on multiple billion‑scale datasets, showcasing the practical impact of large‑scale vector retrieval in AI‑driven services.

AIANNFAISS

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Kuaishou and Tsinghua University Win NeurIPS'21 Billion-Scale ANN Challenge with FAISS‑Optimized KST_ANN Solution

dbaplus Community

Feb 5, 2020 · Databases

Tencent’s Secrets to Scaling Elasticsearch for Trillion‑Level Data

Tencent shares how it leverages Elasticsearch at trillion‑scale across logging, search, and time‑series workloads, detailing the challenges of high availability, low cost, and high performance, and describing concrete kernel‑level optimizations, resource‑limiting strategies, storage tiering, rollup, cache, and merge techniques that enable robust, efficient operation.

Cost OptimizationElasticsearchLarge‑Scale Search

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Tencent’s Secrets to Scaling Elasticsearch for Trillion‑Level Data