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From: Haibo Yan <tristan.yim@gmail.com>
Date: Mon, 6 Apr 2026 16:32:02 -0700
Message-ID: 
 <CABXr29HUT2m7YPpB4gOZHphYmwpwuEWFZ_p6oeyouhWw8Zc1Tw@mail.gmail.com>
Subject: Re: Implement missing join selectivity estimation for range types
To: jian he <jian.universality@gmail.com>
Cc: vignesh C <vignesh21@gmail.com>,
 Schoemans Maxime <maxime.schoemans@ulb.be>,
	Tom Lane <tgl@sss.pgh.pa.us>, Damir Belyalov <dam.bel07@gmail.com>,
	PostgreSQL Hackers <pgsql-hackers@lists.postgresql.org>,
 SAKR Mahmoud <mahmoud.sakr@ulb.be>,
	Diogo Repas <diogo.repas@gmail.com>, LUO Zhicheng <zhicheng.luo@ulb.be>,
	Tomas Vondra <tomas.vondra@enterprisedb.com>,
 Andrey Lepikhov <a.lepikhov@postgrespro.ru>
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On Mon, Apr 6, 2026 at 3:12=E2=80=AFPM jian he <jian.universality@gmail.com=
> wrote:

> I cannot figure out why it aborts.
>
> as Tom mentioned in upthread about the test cases.
> similar to src/test/regress/sql/stats_ext.sql check_estimated_rows
> function.
> we can test it by something:
>
> create or replace function check_estimated_rows(text) returns table (ok
> bool)
> language plpgsql as
> $$
> declare
>     ln text;
>     tmp text[];
>     first_row bool :=3D true;
> begin
>     for ln in
>         execute format('explain analyze %s', $1)
>     loop
>         if first_row then
>             first_row :=3D false;
>             tmp :=3D regexp_match(ln, 'rows=3D(\d*) .* rows=3D(\d*)');
>             return query select 0.2 < tmp[1]::float8 / tmp[2]::float8
> and tmp[1]::float8 / tmp[2]::float8 < 5;
>         end if;
>     end loop;
> end;
> $$;
>
> select * from check_estimated_rows($$select * from test_range_join_1,
> test_range_join_2 where ir1 && ir2$$);
> select * from check_estimated_rows($$select * from test_range_join_1,
> test_range_join_2 where ir1 << ir2$$);
> select * from check_estimated_rows($$select * from test_range_join_1,
> test_range_join_2 where ir1 >> ir2$$);
>
> Do you need 3 tables to do the test? because we need to actually run
> the query then compare the estimated row
> and actually returned rows.
> If you really execute the query with 3 table joins, it will take a lot of
> time.
> So two tables join with where quql should be fine?
>
> /* Fast-forwards i and j to start of iteration */
> + for (i =3D 0; range_cmp_bound_values(typcache, &hist1[i], &hist2[0]) < =
0;
> i++);
> + for (j =3D 0; range_cmp_bound_values(typcache, &hist2[j], &hist1[0]) < =
0;
> j++);
> +
> + /* Do the estimation on overlapping regions */
> + while (i < nhist1 && j < nhist2)
> + {
> + double cur_sel1,
> + cur_sel2;
> + RangeBound cur_sync;
> +
> + if (range_cmp_bound_values(typcache, &hist1[i], &hist2[j]) < 0)
> + cur_sync =3D hist1[i++];
> + else if (range_cmp_bound_values(typcache, &hist1[i], &hist2[j]) > 0)
> + cur_sync =3D hist2[j++];
> + else
> + {
> + /* If equal, skip one */
> + cur_sync =3D hist1[i];
> +
>
> this part range_cmp_bound_values "if else if" part computed twice, you
> can just do
> `
> int cmp;
> cmp =3D range_cmp_bound_values(typcache, &hist1[i], &hist2[j]);
> if cmp <0  then
> else if cmp > 0 then
> else then
> `
>
> also. I think you can put the following into  main while loop.
> + for (i =3D 0; range_cmp_bound_values(typcache, &hist1[i], &hist2[0]) < =
0;
> i++);
> + for (j =3D 0; range_cmp_bound_values(typcache, &hist2[j], &hist1[0]) < =
0;
> j++);
>
> split range and multirange into 2 patches might be a good idea.
> seems: same function (calc_hist_join_selectivity) with same function
> signature in src/backend/utils/adt/multirangetypes_selfuncs.c
> and src/backend/utils/adt/rangetypes_selfuncs.c,
> previously mail complaints not resolved.
>
>
>
> Hi,all
I'd like to revive the discussion on improving selectivity estimation for
range/range joins.
Attached is the v5 patch which teaches rangejoinsel to use range bound
histograms for estimating the <<, >>, and && operators. Currently, the
planner often falls back to a hardcoded default (like 0.005), which can
lead to poor join ordering in complex queries.
In this version, I have intentionally excluded &< and &>.
a &< b essentially maps to upper(a) <=3D upper(b).
a &> b essentially maps to lower(a) >=3D lower(b).
Since these operators include equality (<=3D / >=3D) rather than strict
inequality (< / >), their estimation is slightly more nuanced. I believe
focusing on the strict inequality and overlap operators first allows us to
deliver a clean, converged, and significantly beneficial improvement. We
can discuss the best approach for the remaining operators once this
foundation is in place.
Test Results
My local tests show that the planner now correctly identifies cases with
zero or full selectivity, which were previously misestimated.
----------------------------------------------------------------------
CREATE TABLE t1 (id int, r int4range);
CREATE TABLE t2 (id int, r int4range);
INSERT INTO t1 SELECT g, int4range(g * 2 - 1, g * 2) FROM
generate_series(1, 1000) g;
INSERT INTO t2 SELECT g, int4range(10000 + g * 200, 10000 + g * 200 + 100)
FROM generate_series(1, 1000) g;
ANALYZE t1, t2;
----------------------------------------------------------------------

Real Selectivity:
----------------------------------------------------------------------
SELECT
    avg((t1.r << t2.r)::int) AS p_ll, -- Expected: 1.0
    avg((t1.r >> t2.r)::int) AS p_rr, -- Expected: 0.0
    avg((t1.r && t2.r)::int) AS p_ov  -- Expected: 0.0
FROM t1, t2;
-- Result: p_ll =3D 1.0, p_rr =3D 0.0, p_ov =3D 0.0
----------------------------------------------------------------------

Planner Improvements:
With the patch, the EXPLAIN output reflects these probabilities accurately:
For << (High Selectivity):
The planner correctly estimates rows=3D1000000 (1000 * 1000 * 1.0).
----------------------------------------------------------------------
Nested Loop (cost=3D29.50..15080.75 rows=3D1000000 width=3D61)
  Join Filter: (t1.r << t2.r)
----------------------------------------------------------------------
For && and >> (Near-Zero Selectivity):
The planner now correctly predicts rows=3D1 instead of using the default
multiplier.
----------------------------------------------------------------------
Nested Loop (cost=3D0.00..15036.50 rows=3D1 width=3D36)
  Join Filter: (t1.r && t2.r)
This improved estimation allows the optimizer to make much better decisions
regarding join order and nesting when range columns are involved.
I look forward to your feedback.
Regards,
Haibo

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<div dir=3D"ltr"><div dir=3D"ltr">On Mon, Apr 6, 2026 at 3:12=E2=80=AFPM ji=
an he &lt;<a href=3D"mailto:jian.universality@gmail.com">jian.universality@=
gmail.com</a>&gt; wrote:</div><div class=3D"gmail_quote gmail_quote_contain=
er"><blockquote class=3D"gmail_quote" style=3D"margin:0px 0px 0px 0.8ex;bor=
der-left:1px solid rgb(204,204,204);padding-left:1ex">I cannot figure out w=
hy it aborts.<br>
<br>
as Tom mentioned in upthread about the test cases.<br>
similar to src/test/regress/sql/stats_ext.sql check_estimated_rows function=
.<br>
we can test it by something:<br>
<br>
create or replace function check_estimated_rows(text) returns table (ok boo=
l)<br>
language plpgsql as<br>
$$<br>
declare<br>
=C2=A0 =C2=A0 ln text;<br>
=C2=A0 =C2=A0 tmp text[];<br>
=C2=A0 =C2=A0 first_row bool :=3D true;<br>
begin<br>
=C2=A0 =C2=A0 for ln in<br>
=C2=A0 =C2=A0 =C2=A0 =C2=A0 execute format(&#39;explain analyze %s&#39;, $1=
)<br>
=C2=A0 =C2=A0 loop<br>
=C2=A0 =C2=A0 =C2=A0 =C2=A0 if first_row then<br>
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 first_row :=3D false;<br>
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 tmp :=3D regexp_match(ln, &#39;ro=
ws=3D(\d*) .* rows=3D(\d*)&#39;);<br>
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 return query select 0.2 &lt; tmp[=
1]::float8 / tmp[2]::float8<br>
and tmp[1]::float8 / tmp[2]::float8 &lt; 5;<br>
=C2=A0 =C2=A0 =C2=A0 =C2=A0 end if;<br>
=C2=A0 =C2=A0 end loop;<br>
end;<br>
$$;<br>
<br>
select * from check_estimated_rows($$select * from test_range_join_1,<br>
test_range_join_2 where ir1 &amp;&amp; ir2$$);<br>
select * from check_estimated_rows($$select * from test_range_join_1,<br>
test_range_join_2 where ir1 &lt;&lt; ir2$$);<br>
select * from check_estimated_rows($$select * from test_range_join_1,<br>
test_range_join_2 where ir1 &gt;&gt; ir2$$);<br>
<br>
Do you need 3 tables to do the test? because we need to actually run<br>
the query then compare the estimated row<br>
and actually returned rows.<br>
If you really execute the query with 3 table joins, it will take a lot of t=
ime.<br>
So two tables join with where quql should be fine?<br>
<br>
/* Fast-forwards i and j to start of iteration */<br>
+ for (i =3D 0; range_cmp_bound_values(typcache, &amp;hist1[i], &amp;hist2[=
0]) &lt; 0; i++);<br>
+ for (j =3D 0; range_cmp_bound_values(typcache, &amp;hist2[j], &amp;hist1[=
0]) &lt; 0; j++);<br>
+<br>
+ /* Do the estimation on overlapping regions */<br>
+ while (i &lt; nhist1 &amp;&amp; j &lt; nhist2)<br>
+ {<br>
+ double cur_sel1,<br>
+ cur_sel2;<br>
+ RangeBound cur_sync;<br>
+<br>
+ if (range_cmp_bound_values(typcache, &amp;hist1[i], &amp;hist2[j]) &lt; 0=
)<br>
+ cur_sync =3D hist1[i++];<br>
+ else if (range_cmp_bound_values(typcache, &amp;hist1[i], &amp;hist2[j]) &=
gt; 0)<br>
+ cur_sync =3D hist2[j++];<br>
+ else<br>
+ {<br>
+ /* If equal, skip one */<br>
+ cur_sync =3D hist1[i];<br>
+<br>
<br>
this part range_cmp_bound_values &quot;if else if&quot; part computed twice=
, you<br>
can just do<br>
`<br>
int cmp;<br>
cmp =3D range_cmp_bound_values(typcache, &amp;hist1[i], &amp;hist2[j]);<br>
if cmp &lt;0=C2=A0 then<br>
else if cmp &gt; 0 then<br>
else then<br>
`<br>
<br>
also. I think you can put the following into=C2=A0 main while loop.<br>
+ for (i =3D 0; range_cmp_bound_values(typcache, &amp;hist1[i], &amp;hist2[=
0]) &lt; 0; i++);<br>
+ for (j =3D 0; range_cmp_bound_values(typcache, &amp;hist2[j], &amp;hist1[=
0]) &lt; 0; j++);<br>
<br>
split range and multirange into 2 patches might be a good idea.<br>
seems: same function (calc_hist_join_selectivity) with same function<br>
signature in src/backend/utils/adt/multirangetypes_selfuncs.c<br>
and src/backend/utils/adt/rangetypes_selfuncs.c,<br>
previously mail complaints not resolved.<br>
<br>
<br><br></blockquote>Hi,all<br>I&#39;d like to revive the discussion on imp=
roving selectivity estimation for range/range joins.<br>Attached is the v5 =
patch which teaches rangejoinsel to use range bound histograms for estimati=
ng the &lt;&lt;, &gt;&gt;, and &amp;&amp; operators. Currently, the planner=
 often falls back to a hardcoded default (like 0.005), which can lead to po=
or join ordering in complex queries.<br>In this version, I have intentional=
ly excluded &amp;&lt; and &amp;&gt;.<br>a &amp;&lt; b essentially maps to u=
pper(a) &lt;=3D upper(b).<br>a &amp;&gt; b essentially maps to lower(a) &gt=
;=3D lower(b).<br>Since these operators include equality (&lt;=3D / &gt;=3D=
) rather than strict inequality (&lt; / &gt;), their estimation is slightly=
 more nuanced. I believe focusing on the strict inequality and overlap oper=
ators first allows us to deliver a clean, converged, and significantly bene=
ficial improvement. We can discuss the best approach for the remaining oper=
ators once this foundation is in place.<br>Test Results<br>My local tests s=
how that the planner now correctly identifies cases with zero or full selec=
tivity, which were previously misestimated. <br>---------------------------=
-------------------------------------------<br>CREATE TABLE t1 (id int, r i=
nt4range);<br>CREATE TABLE t2 (id int, r int4range);<br>INSERT INTO t1 SELE=
CT g, int4range(g * 2 - 1, g * 2) FROM generate_series(1, 1000) g;<br>INSER=
T INTO t2 SELECT g, int4range(10000 + g * 200, 10000 + g * 200 + 100) FROM =
generate_series(1, 1000) g;<br>ANALYZE t1, t2;<br>-------------------------=
---------------------------------------------<br><br>Real Selectivity:<br>-=
---------------------------------------------------------------------<br>SE=
LECT <br>=C2=A0 =C2=A0 avg((t1.r &lt;&lt; t2.r)::int) AS p_ll, -- Expected:=
 1.0<br>=C2=A0 =C2=A0 avg((t1.r &gt;&gt; t2.r)::int) AS p_rr, -- Expected: =
0.0<br>=C2=A0 =C2=A0 avg((t1.r &amp;&amp; t2.r)::int) AS p_ov =C2=A0-- Expe=
cted: 0.0<br>FROM t1, t2;<br>-- Result: p_ll =3D 1.0, p_rr =3D 0.0, p_ov =
=3D 0.0<br>----------------------------------------------------------------=
------<br><br>Planner Improvements:<br>With the patch, the EXPLAIN output r=
eflects these probabilities accurately:<br>For &lt;&lt; (High Selectivity):=
<br>The planner correctly estimates rows=3D1000000 (1000 * 1000 * 1.0).<br>=
----------------------------------------------------------------------<br>N=
ested Loop (cost=3D29.50..15080.75 rows=3D1000000 width=3D61)<br>=C2=A0 Joi=
n Filter: (t1.r &lt;&lt; t2.r)<br>-----------------------------------------=
-----------------------------<br>For &amp;&amp; and &gt;&gt; (Near-Zero Sel=
ectivity):<br>The planner now correctly predicts rows=3D1 instead of using =
the default multiplier.<br>------------------------------------------------=
----------------------<br>Nested Loop (cost=3D0.00..15036.50 rows=3D1 width=
=3D36)<br>=C2=A0 Join Filter: (t1.r &amp;&amp; t2.r)<br>This improved estim=
ation allows the optimizer to make much better decisions regarding join ord=
er and nesting when range columns are involved.<br>I look forward to your f=
eedback.<br>Regards,<br><div>Haibo=C2=A0</div></div></div>

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