Re: Problems with estimating OR conditions, IS NULL on LEFT JOINs

From: Alena Rybakina <lena(dot)ribackina(at)yandex(dot)ru>
To: Tomas Vondra <tomas(dot)vondra(at)enterprisedb(dot)com>
Cc: PostgreSQL Hackers <pgsql-hackers(at)lists(dot)postgresql(dot)org>, Andrey Lepikhov <a(dot)lepikhov(at)postgrespro(dot)ru>, Tom Lane <tgl(at)sss(dot)pgh(dot)pa(dot)us>
Subject: Re: Problems with estimating OR conditions, IS NULL on LEFT JOINs
Date: 2023-07-08 08:29:52
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> Well, one option would be to modify all selectivity functions to do
> something like the patch does for nulltestsel(). That seems a bit
> cumbersome because why should those places care about maybe running on
> the outer side of a join, or what? For code in extensions this would be
> particularly problematic, I think.
Agree. I would say that we can try it if nothing else works out.
> So what I was thinking about doing this in a way that'd make this
> automatic, without having to modify the selectivity functions.
> Option (3) is very simple - examine_variable would simply adjust the
> statistics by tweaking the null_frac field, when looking at variables on
> the outer side of the join. But it has issues when estimating multiple
> conditions.
> Imagine t1 has 1M rows, and we want to estimate
> WHERE ((t2.a=1) AND (t2.b=1))
> but only 50% of the t1 rows has a match in t2. Assume each of the t2
> conditions matches 100% rows in the table. With the correction, this
> means 50% selectivity for each condition. And if we combine them the
> usual way, it's 0.5 * 0.5 = 0.25.
> But we know all the rows in the "matching" part match the condition, so
> the correct selectivity should be 0.5.
> In a way, this is just another case of estimation issues due to the
> assumption of independence.
> FWIW, I used "AND" in the example for simplicity, but that'd probably be
> pushed to the baserel level. There'd need to be OR to keep it at the
> join level, but the overall issue is the same, I think.
> Also, this entirely ignores extended statistics - I have no idea how we
> might tweak those in (3).

I understood the idea - it is very similar to what is implemented in the
current patch.

But I don't understand how to do it in the examine_variable function, to
be honest.

> But (4) was suggesting we could improve this essentially by treating the
> join as two distinct sets of rows
> - the inner join result
> - rows without match on the outer side
> For the inner part, we would do estimates as now (using the regular
> per-column statistics). If we knew the conditions match 100% rows, we'd
> still get 100% when the conditions are combined.
> For the second part of the join we know the outer side is just NULLs in
> all columns, and that'd make the estimation much simpler for most
> clauses. We'd just need to have "fake" statistics with null_frac=1.0 and
> that's it.
> And then we'd just combine these two selectivities. If we know the inner
> side is 50% and all rows match the conditions, and no rows in the other
> 50% match, the selectivity is 50%.
> inner_part * inner_sel + outer_part * outer_sel = 0.5 * 1.0 + 0.0 = 0.5
> Now, we still have issues with independence assumption in each of these
> parts separately. But that's OK, I think.
> I think (4) could be implemented by doing the current estimation for the
> inner part, and by tweaking examine_variable in the "outer" part in a
> way similar to (3). Except that it just sets null_frac=1.0 everywhere.
> For (4) we don't need to tweak those at all,
> because for inner part we can just apply them as is, and for outer part
> it's irrelevant because everything is NULL.
I like this idea the most) I'll try to start with this and implement the
> I hope this makes more sense. If not, let me know and I'll try to
> explain it better.

Thank you for your explanation)

I will unsubscribe soon based on the results or if I have any questions.

Alena Rybakina
Postgres Professional

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