| From: | Peter Geoghegan <pg(at)bowt(dot)ie> |
|---|---|
| To: | Chapman Flack <chap(at)anastigmatix(dot)net> |
| Cc: | Andrey Borodin <x4mmm(at)yandex-team(dot)ru>, Stefan Keller <sfkeller(at)gmail(dot)com>, Oleg Ivanov <o(dot)ivanov(at)postgrespro(dot)ru>, Oleg Bartunov <obartunov(at)gmail(dot)com>, Nikolay Samokhvalov <samokhvalov(at)gmail(dot)com>, Pgsql Hackers <pgsql-hackers(at)postgresql(dot)org> |
| Subject: | Re: ML-based indexing ("The Case for Learned Index Structures", a paper from Google) |
| Date: | 2021-04-20 19:45:19 |
| Message-ID: | CAH2-Wz=L6OsZDjFd7gvgZz7PZCAbhL3H9h6Wk7dqUBAUHjf0vg@mail.gmail.com |
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| Lists: | pgsql-hackers |
On Tue, Apr 20, 2021 at 12:35 PM Chapman Flack <chap(at)anastigmatix(dot)net> wrote:
> How would showing that to be true for data structure X be different from
> making a case for data structure X?
You don't have to understand the theoretical basis of B-Tree indexes
to see that they work well. In fact, it took at least a decade for
somebody to formalize how the space utilization works with B-Trees
containing random data. Of course theory matters, but the fact is that
B-Trees had been widely used for commercial and scientific
applications that whole time.
Maybe I'll be wrong about learned indexes - who knows? But the burden
of proof is not mine. I prefer to spend my time on things that I am
reasonably confident will work out well ahead of time.
--
Peter Geoghegan
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