When calculating the distance between date values, make sure to subtract
them in the right order, i.e. (larger - smaller).
The distance is used to determine which values to merge, and is expected
to be a positive value. The code unfortunately did the subtraction in
the opposite order, i.e. (smaller - larger), thus producing negative
values and merging values the most distant values first.
The resulting index is correct (i.e. produces correct results), but may
be significantly less efficient. This affects all minmax-multi indexes
on date columns.
Backpatch to 14, where minmax-multi indexes were introduced.
Reported-by: Ashutosh Bapat
Reviewed-by: Ashutosh Bapat, Dean Rasheed
Backpatch-through: 14
Discussion: https://postgr.es/m/eef0ea8c-4aaa-8d0d-027f-58b1f35dd170@enterprisedb.com
When calculating distances for timestamp values for BRIN minmax-multi
indexes, we need to be careful about overflows for extreme values. If
the value overflows into a negative value, the index may be inefficient.
The new regression test checks this for the timestamp type by adding a
table with enough values to force range compaction/merging. The values
are close to min/max, which means a risk of overflow.
Fixed by converting the int64 values to double first, before calculating
the distance. This prevents the overflow. We may lose some precision, of
course, but that's good enough. In the worst case we build a slightly
less efficient index, but for large distances this won't matter.
This only affects minmax-multi indexes on timestamp columns, with ranges
containing values sufficiently distant to cause an overflow. That seems
like a fairly rare case in practice.
Backpatch to 14, where minmax-multi indexes were introduced.
Reported-by: Ashutosh Bapat
Reviewed-by: Ashutosh Bapat, Dean Rasheed
Backpatch-through: 14
Discussion: https://postgr.es/m/eef0ea8c-4aaa-8d0d-027f-58b1f35dd170@enterprisedb.com
When calculating distance in brin_minmax_multi_distance_inet(), the
netmask was applied incorrectly. This results in (seemingly) incorrect
ordering of values, triggering an assert.
For builds without asserts this is mostly harmless - we may merge other
ranges, possibly resulting in slightly less efficient index. But it's
still correct and the greedy algorithm doesn't guarantee optimality
anyway.
Backpatch to 14, where minmax-multi indexes were introduced.
Reported by Dmitry Dolgov, investigation and fix by me.
Reported-by: Dmitry Dolgov
Backpatch-through: 14
Discussion: https://postgr.es/m/17774-c6f3e36dd4471e67@postgresql.org
When calculating distance between float4/float8 values, we need to be a
bit more careful about NaN values in order not to trigger assert. We
consider NaN values to be equal (distace 0.0) and in infinite distance
from all other values.
On builds without asserts, this issue is mostly harmless - the ranges
may be merged in less efficient order, but the index is still correct.
Per report from Andreas Seltenreich. Backpatch to 14, where this new
BRIN opclass was introduced.
Reported-by: Andreas Seltenreich
Discussion: https://postgr.es/m/87r1bw9ukm.fsf@credativ.de
Adds BRIN opclasses similar to the existing minmax, except that instead
of summarizing the page range into a single [min,max] range, the summary
consists of multiple ranges and/or points, allowing gaps. This allows
more efficient handling of data with poor correlation to physical
location within the table and/or outlier values, for which the regular
minmax opclassed tend to work poorly.
It's possible to specify the number of values kept for each page range,
either as a single point or an interval boundary.
CREATE TABLE t (a int);
CREATE INDEX ON t
USING brin (a int4_minmax_multi_ops(values_per_range=16));
When building the summary, the values are combined into intervals with
the goal to minimize the "covering" (sum of interval lengths), using a
support procedure computing distance between two values.
Bump catversion, due to various catalog changes.
Author: Tomas Vondra <tomas.vondra@postgresql.org>
Reviewed-by: Alvaro Herrera <alvherre@alvh.no-ip.org>
Reviewed-by: Alexander Korotkov <aekorotkov@gmail.com>
Reviewed-by: Sokolov Yura <y.sokolov@postgrespro.ru>
Reviewed-by: John Naylor <john.naylor@enterprisedb.com>
Discussion: https://postgr.es/m/c1138ead-7668-f0e1-0638-c3be3237e812@2ndquadrant.com
Discussion: https://postgr.es/m/5d78b774-7e9c-c94e-12cf-fef51cc89b1a%402ndquadrant.com
