×
Community Blog PostgreSQL: How to Optimize the GiST Index Scanning

PostgreSQL: How to Optimize the GiST Index Scanning

In this article, the author explains three methods to optimize the GiST index scanning to limit resource wastage in PostgreSQL.

By digoal

Background

The PostgreSQL GiST index supports distance sorting queries, some of which have three requirements:

1) Order by distance

2) Limited distance range

3) The number of records returned by limit

create extension postgis;      
      
create table t_pos(      
  id int primary key,      
  pos geometry      
);    
  
insert into t_pos       
select * from (      
  select id,      
  ST_SetSRID(      
              ST_Point( round((random()*(135.085831-73.406586)+73.406586)::numeric,6),          
                        round((random()*(53.880950-3.408477)+3.408477)::numeric,6)          
              ),      
             4326      
            )  as pos      
from generate_series(1,1000000000) t(id)       
) t      
order by st_geohash(pos,15);      
      
create index idx_t_pos_1 on t_pos using gist(pos);      
  
select *,   
st_distancespheroid(pos, st_setsrid(st_makepoint(120,50),4326), 'SPHEROID["WGS84",6378137,298.257223563]') as dist   
from t_pos   
where   
st_distancespheroid(pos, st_setsrid(st_makepoint(120,50),4326), 'SPHEROID["WGS84",6378137,298.257223563]') < 5000   
order by pos <-> st_setsrid(st_makepoint(120,50),4326)   
limit 100;   

Or,

create table t_age(id int, age int);  
insert into t_age select generate_series(1,10000000), random()*120;  
create index idx_t_age_1 on t_age using gist (age);  
  
select * from t_age   
where   
(age <-> 25) <1   
order by age <-> 25   
limit 100000; 

When you use the GiST index in both the SQL statements, what will happen if there are less than 100 records of distance less than 5000, or less than 100 thousand records of age gap smaller than 1?

The answer is:

The entire index will be scanned.

This is because the database does not know if there are any records that meet the where condition.

Since the records are returned by distance from near to far, when a record no longer meets the where condition (the distance is equal to or greater than 5000, or the age gap is equal to or greater than 1), there is no need to scan the rest.

So, this is the point where you can optimize the database kernel.

Optimization Methods

1) Subquery Optimization

A waste scan may occur in the subquery, such as limit 1000. Only 1 record meets the condition, and then scanning the rest of 999 records is a waste.

select * from (
select * from t_age
order by age <-> 25
limit 1000
) t
where
(age <-> 25) <1 ;

Compare with the following:

postgres=# explain (analyze,verbose,timing,costs,buffers) select * from t_age where (age <-> 25) <1 order by age <-> 25 limit 100000;  
                                                                     QUERY PLAN                                                                       
----------------------------------------------------------------------------------------------------------------------------------------------------  
 Limit  (cost=0.36..3572.19 rows=100000 width=12) (actual time=0.169..10757.930 rows=83553 loops=1)  
   Output: id, age, ((age <-> 25))  
   Buffers: shared hit=9525191  
   ->  Index Scan using idx_t_age_1 on public.t_age  (cost=0.36..119061.20 rows=3333333 width=12) (actual time=0.167..10750.016 rows=83553 loops=1)  
         Output: id, age, (age <-> 25)  
         Order By: (t_age.age <-> 25)  
         Filter: ((t_age.age <-> 25) < 1)  
         Rows Removed by Filter: 9916447  
         Buffers: shared hit=9525191  
 Planning Time: 0.153 ms  
 Execution Time: 10762.824 ms  
(11 rows)  
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from (  
select * from t_age   
order by age <-> 25   
limit 100000  
) t  
where   
(age <-> 25) <1 ;  
                                                                       QUERY PLAN                                                                          
---------------------------------------------------------------------------------------------------------------------------------------------------------  
 Subquery Scan on t  (cost=0.36..1354.35 rows=33333 width=8) (actual time=0.146..144.873 rows=83553 loops=1)  
   Output: t.id, t.age  
   Filter: ((t.age <-> 25) < 1)  
   Rows Removed by Filter: 16447  
   Buffers: shared hit=95549  
   ->  Limit  (cost=0.36..901.97 rows=100000 width=12) (actual time=0.144..133.330 rows=100000 loops=1)  
         Output: t_age.id, t_age.age, ((t_age.age <-> 25))  
         Buffers: shared hit=95549  
         ->  Index Scan using idx_t_age_1 on public.t_age  (cost=0.36..90161.39 rows=10000000 width=12) (actual time=0.143..124.430 rows=100000 loops=1)  
               Output: t_age.id, t_age.age, (t_age.age <-> 25)  
               Order By: (t_age.age <-> 25)  
               Buffers: shared hit=95549  
 Planning Time: 0.108 ms  
 Execution Time: 148.951 ms  
(14 rows)  

2) Customize PL Function for Optimization to Avoid Waste

create or replace function ff(int, int, int) returns setof t_age as $$  
declare  
  v t_age;  
  i int := 0;  
begin  
  set enable_seqscan=off;  
  set enable_indexscan=on;  
  set enable_bitmapscan=off;  
  for v in   
    select * from t_age order by age <-> $1   
  loop   
    if (v.age <-> $1) >= $2 or (i) >= $3 then return; end if;  
    i := i+1;   
    return next v;  
  end loop;  
  return;  
end;  
$$ language plpgsql strict;  
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from ff(25,1,1000000);  
                                                    QUERY PLAN                                                       
-------------------------------------------------------------------------------------------------------------------  
 Function Scan on public.ff  (cost=0.22..0.41 rows=1000 width=8) (actual time=159.203..164.049 rows=83553 loops=1)  
   Output: id, age  
   Function Call: ff(25, 1, 1000000)  
   Buffers: shared hit=79848  
 Planning Time: 0.042 ms  
 Execution Time: 168.601 ms  
(6 rows)  

3) Kernel Optimization

Support filter recognition in sorting the scan to avoid scanning all records when not meeting the limit number.

0 0 0
Share on

digoal

278 posts | 24 followers

You may also like

Comments

digoal

278 posts | 24 followers

Related Products