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English(EN) The query win at 100M vectors on 4x less RAM holds up. The 42-hour build gap is tilted, and the extension is alpha and needs PostgreSQL 18. # postgres # ai # da

PostgreSQL 查询方法在内存占用更少的情况下展示了 1 亿向量的性能

已为 PostgreSQL 开发了一种新的查询方法,该方法通过使用显著更少的 RAM 处理 1 亿个向量,展示了令人印象深刻的性能。然而,这一进展伴随着显著的构建时间和对特定即将发布的 PostgreSQL 版本的需求,表明它目前处于 alpha 阶段。 AI

影响 这一发展可以提高依赖大规模向量数据库的 AI 应用程序的效率,用于诸如相似性搜索和推荐系统之类的任务。

排序理由 该项目描述了一种数据库系统的新技术能力,属于研究范畴。[lever_c_demoted from research: ic=1 ai=0.7]

在 Mastodon — sigmoid.social 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

PostgreSQL 查询方法在内存占用更少的情况下展示了 1 亿向量的性能

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该项目描述了一种数据库系统的新技术能力,属于研究范畴。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. Mastodon — sigmoid.social TIER_1 English(EN) · [email protected] ·

    100M 向量查询在 4 倍更少内存上获胜的说法成立。42 小时构建差距倾斜,扩展为 Alpha 版且需要 PostgreSQL 18。# postgres # ai # da

    The query win at 100M vectors on 4x less RAM holds up. The 42-hour build gap is tilted, and the extension is alpha and needs PostgreSQL 18. # postgres # ai # database # machinelearning # software # coding # development # engineering # inclusive # community pg_vectorsearch vs pgve…