Re: ML-based indexing ("The Case for Learned Index Structures", a paper from Google)

From: Oleg Bartunov <obartunov(at)gmail(dot)com>
To: Nikolay Samokhvalov <samokhvalov(at)gmail(dot)com>, Oleg <o(dot)ivanov(at)postgrespro(dot)ru>
Cc: Pgsql Hackers <pgsql-hackers(at)postgresql(dot)org>
Subject: Re: ML-based indexing ("The Case for Learned Index Structures", a paper from Google)
Date: 2017-12-12 13:33:15
Message-ID: CAF4Au4x1VFpytRKkkeRmaxbiyk-xomNZm+V3+xcksNrCDY+p-w@mail.gmail.com
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On Mon, Dec 11, 2017 at 11:11 PM, Nikolay Samokhvalov
<samokhvalov(at)gmail(dot)com> wrote:
> Very interesting read: https://arxiv.org/abs/1712.01208
>
> HN discussion: https://news.ycombinator.com/item?id=15894896
>
> Some of the comments (from Twitter
> https://twitter.com/schrockn/status/940037656494317568): "Jeff Dean and co
> at GOOG just released a paper showing how machine-learned indexes can
> replace B-Trees, Hash Indexes, and Bloom Filters. Execute 3x faster than
> B-Trees, 10-100x less space. Executes on GPU, which are getting faster
> unlike CPU. Amazing."
>
> Can those ideas be applied to Postgres in its current state? Or it's not
> really down-to-earth?

Oleg made some analysis of the paper.

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