Re: PoC Refactor AM analyse API

From: Andrey Borodin <x4mmm(at)yandex-team(dot)ru>
To: Смирнов Денис <sd(at)arenadata(dot)io>
Cc: pgsql-hackers(at)postgresql(dot)org
Subject: Re: PoC Refactor AM analyse API
Date: 2020-12-08 08:42:12
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Hi Denis!

> 7 дек. 2020 г., в 18:23, Смирнов Денис <sd(at)arenadata(dot)io> написал(а):
> I suggest a refactoring of analyze AM API as it is too much heap specific at the moment. The problem was inspired by Greenplum’s analyze improvement for append-optimized row and column AM with variable size compressed blocks.
> Currently we do analyze in two steps.
> 1. Sample fix size blocks with algorithm S from Knuth (BlockSampler function)
> 2. Collect tuples into reservoir with algorithm Z from Vitter.
> So this doesn’t work for AMs using variable sized physical blocks for example. They need weight random sampling (WRS) algorithms like A-Chao or logical blocks to follow S-Knuth (and have a problem with RelationGetNumberOfBlocks() estimating a physical number of blocks). Another problem with columns - they are not passed to analyze begin scan and can’t benefit from column storage at ANALYZE TABLE (COL).
> The suggestion is to replace table_scan_analyze_next_block() and table_scan_analyze_next_tuple() with a single function: table_acquire_sample_rows(). The AM implementation of table_acquire_sample_rows() can use the BlockSampler functions if it wants to, but if the AM is not block-oriented, it could do something else. This suggestion also passes VacAttrStats to table_acquire_sample_rows() for column-oriented AMs and removes PROGRESS_ANALYZE_BLOCKS_TOTAL and PROGRESS_ANALYZE_BLOCKS_DONE definitions as not all AMs can be block-oriented.

Just few random notes about the idea.
Heap pages are not of a fixed size, when measured in tuple count. And comment in the codes describes it.
* Although every row has an equal chance of ending up in the final
* sample, this sampling method is not perfect: not every possible
* sample has an equal chance of being selected. For large relations
* the number of different blocks represented by the sample tends to be
* too small. We can live with that for now. Improvements are welcome.

Current implementation provide framework with shared code. Though this framework is only suitable for block-of-tuples AMs. And have statistical downsides when count of tuples varies too much.
Maybe can we just provide a richer API? To have both: avoid copying code and provide flexibility.

Best regards, Andrey Borodin.

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