PostgreSQL UDF实现tsvector(全文检索), array(数组)多值字段与scalar(单值字段)类型的整合索引(类分区索引) - 单值与多值类型复合查询性能提速100倍+ 案例 (含，单值+多值列合成)

背景

在应用程序中，特别是使用PostgreSQL多值列（数组、全文检索、JSON）类型的业务中，除了多值列有查询需求，单值列也有查询需求。

那么当两种查询并行存在时，数据库会根据COST选择单列或多列组合索引。但是性能并不能达到极致，只是简化用户的索引设计而已。

例如

create table tbl(gid int, c1 int[]);  
  
  
insert into tbl select random()*99, gen_randarr(999, 10) from generate_series(1,10000000);  

gid有100个值，c1有10个值（取值范围1000以内）。用户可能按gid查询，也可能按c1查询，还可能按两个字段组合查询。

当按两个字段组合查询时，现有达到方法并不高效。包括btree_gin在内。

如何提速？

demo

1、创建一个产生随机数的函数

CREATE OR REPLACE FUNCTION public.gen_randarr(integer, integer)  
 RETURNS integer[]  
 LANGUAGE sql  
 STRICT  
AS $function$  
  select array(select (random()*$1)::int from generate_series(1,$2));  
$function$;  

2、创建测试表，包含一个单值列，和一个多值列

create table tbl(gid int, c1 int[]);  

3、写入1000万数据

insert into tbl select random()*99, gen_randarr(999, 10) from generate_series(1,10000000);  

传统加速方法1

建立 单值列+多值列 的复合索引

create extension btree_gin;  

set maintenance_work_mem ='8GB';  
create index idx_tbl_1 on tbl using gin (gid, c1);  

复合查询性能如何？

postgres=# explain (analyze,verbose,timing,costs,buffers) select * from tbl where gid=1 and c1 @> array[1,2,3];  
                                                     QUERY PLAN                                                        
---------------------------------------------------------------------------------------------------------------------  
 Bitmap Heap Scan on public.tbl  (cost=96.00..97.02 rows=1 width=65) (actual time=12.810..12.810 rows=0 loops=1)  
   Output: gid, c1  
   Recheck Cond: ((tbl.gid = 1) AND (tbl.c1 @> '{1,2,3}'::integer[]))  
   Buffers: shared hit=184  
   ->  Bitmap Index Scan on idx_tbl_1  (cost=0.00..96.00 rows=1 width=0) (actual time=12.807..12.807 rows=0 loops=1)  
         Index Cond: ((tbl.gid = 1) AND (tbl.c1 @> '{1,2,3}'::integer[]))  
         Buffers: shared hit=184  
 Planning time: 0.154 ms  
 Execution time: 12.838 ms  
(9 rows)  

postgres=# explain (analyze,verbose,timing,costs,buffers)  select * from tbl where gid=1 and c1 && array[1,2,3];  
                                                         QUERY PLAN                                                           
----------------------------------------------------------------------------------------------------------------------------  
 Bitmap Heap Scan on public.tbl  (cost=129.80..3433.25 rows=3297 width=65) (actual time=17.453..22.486 rows=2932 loops=1)  
   Output: gid, c1  
   Recheck Cond: ((tbl.gid = 1) AND (tbl.c1 && '{1,2,3}'::integer[]))  
   Heap Blocks: exact=2906  
   Buffers: shared hit=3089  
   ->  Bitmap Index Scan on idx_tbl_1  (cost=0.00..128.97 rows=3297 width=0) (actual time=17.121..17.121 rows=2932 loops=1)  
         Index Cond: ((tbl.gid = 1) AND (tbl.c1 && '{1,2,3}'::integer[]))  
         Buffers: shared hit=183  
 Planning time: 0.223 ms  
 Execution time: 22.761 ms  
(10 rows)  

如果你没有继续优化的动力，你会发现这个性能好像也蛮OK的。索引条件也用了多个。

实际上这个索引是内部将两个索引合并后做的BITMAP SCAN。

《PostgreSQL bitmapAnd, bitmapOr, bitmap index scan, bitmap heap scan》

传统加速方法2

实际上就是将 单值， 多值列 索引分开建。

postgres=# drop index idx_tbl_1;  
DROP INDEX  
  
postgres=# create index idx_tbl_1 on tbl (gid);  
CREATE INDEX  
  
postgres=# create index idx_tbl_2 on tbl using gin (c1);  

实际效果比GIN的复合索引差一些

explain (analyze,verbose,timing,costs,buffers) select * from tbl where gid=1 and c1 @> array[1,2,3];  
  
explain (analyze,verbose,timing,costs,buffers)  select * from tbl where gid=1 and c1 && array[1,2,3];  
  
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from tbl where gid=1 and c1 @> array[1,2,3];  
                                                      QUERY PLAN                                                         
-----------------------------------------------------------------------------------------------------------------------  
 Bitmap Heap Scan on public.tbl  (cost=72.09..83.25 rows=1 width=65) (actual time=12.848..12.848 rows=0 loops=1)  
   Output: gid, c1  
   Recheck Cond: (tbl.c1 @> '{1,2,3}'::integer[])  
   Filter: (tbl.gid = 1)  
   Rows Removed by Filter: 13  
   Heap Blocks: exact=13  
   Buffers: shared hit=131  
   ->  Bitmap Index Scan on idx_tbl_2  (cost=0.00..72.09 rows=11 width=0) (actual time=12.810..12.810 rows=13 loops=1)  
         Index Cond: (tbl.c1 @> '{1,2,3}'::integer[])  
         Buffers: shared hit=118  
 Planning time: 0.254 ms  
 Execution time: 12.874 ms  
(12 rows)  
  
postgres=# explain (analyze,verbose,timing,costs,buffers)  select * from tbl where gid=1 and c1 && array[1,2,3];  
                                                              QUERY PLAN                                                                 
---------------------------------------------------------------------------------------------------------------------------------------  
 Bitmap Heap Scan on public.tbl  (cost=3534.41..6837.86 rows=3297 width=65) (actual time=69.636..74.613 rows=2932 loops=1)  
   Output: gid, c1  
   Recheck Cond: ((tbl.gid = 1) AND (tbl.c1 && '{1,2,3}'::integer[]))  
   Heap Blocks: exact=2906  
   Buffers: shared hit=2982 read=279  
   ->  BitmapAnd  (cost=3534.41..3534.41 rows=3297 width=0) (actual time=69.002..69.002 rows=0 loops=1)  
         Buffers: shared hit=76 read=279  
         ->  Bitmap Index Scan on idx_tbl_1  (cost=0.00..1089.93 rows=106333 width=0) (actual time=13.538..13.538 rows=100704 loops=1)  
               Index Cond: (tbl.gid = 1)  
               Buffers: shared read=279  
         ->  Bitmap Index Scan on idx_tbl_2  (cost=0.00..2442.58 rows=310077 width=0) (actual time=50.878..50.878 rows=296887 loops=1)  
               Index Cond: (tbl.c1 && '{1,2,3}'::integer[])  
               Buffers: shared hit=76  
 Planning time: 0.147 ms  
 Execution time: 74.886 ms  
(15 rows)  

本文100倍性能提升加速方法

本文提到的方法，适合于单值列与多值列混合查询的场景，实际上我们用到了PostgreSQL的UDF和表达式索引的功能。

UDF将单值列和多值列合并，变成一个新的多值列。

表达式索引，针对这个UDF来构建。

目的是将内部的BITMAP合并这个部分化解掉，仅使用一颗倒排树，并且这颗倒排树包含了 单值列和多值列的值。

1、创建一个UDF，将本例的gid与c1的值合并。

create or replace function gen_newarr(int, anyarray) returns text[] as $$  
declare  
  res text[] := '{}';  
  x int;  
begin  
  foreach x in array $2 loop  
    res := array_append(res, $1||'_'||x);  
  end loop;  
  return res;  
end;  
$$ language plpgsql strict immutable;  
  
  
  
  
postgres=# select gen_newarr(123,array[1,2,3,4]);  
-[ RECORD 1 ]-------------------------  
gen_newarr | {123_1,123_2,123_3,123_4}  

2、创建表达式索引

set maintenance_work_mem ='8GB';  
create index idx_tbl_2 on tbl using gin (gen_newarr(gid, c1));  

3、查询时，使用表达式查询，所以需要更改SQL语句

postgres=# explain (analyze,verbose,timing,costs,buffers)  select * from tbl where gen_newarr(gid, c1) @> array['1_1','1_2','1_3'];  
                                                    QUERY PLAN                                                       
-------------------------------------------------------------------------------------------------------------------  
 Bitmap Heap Scan on public.tbl  (cost=12.01..13.27 rows=1 width=65) (actual time=0.146..0.146 rows=0 loops=1)  
   Output: gid, c1  
   Recheck Cond: (gen_newarr(tbl.gid, tbl.c1) @> '{1_1,1_2,1_3}'::text[])  
   Buffers: shared hit=14  
   ->  Bitmap Index Scan on idx_tbl_2  (cost=0.00..12.01 rows=1 width=0) (actual time=0.144..0.144 rows=0 loops=1)  
         Index Cond: (gen_newarr(tbl.gid, tbl.c1) @> '{1_1,1_2,1_3}'::text[])  
         Buffers: shared hit=14  
 Planning time: 0.092 ms  
 Execution time: 0.174 ms  
(9 rows)  
  
postgres=# explain (analyze,verbose,timing,costs,buffers)  select * from tbl where gen_newarr(gid, c1) && array['1_1','1_2','1_3'];  
                                                         QUERY PLAN                                                            
-----------------------------------------------------------------------------------------------------------------------------  
 Bitmap Heap Scan on public.tbl  (cost=1220.70..133422.08 rows=149251 width=65) (actual time=1.020..6.034 rows=2932 loops=1)  
   Output: gid, c1  
   Recheck Cond: (gen_newarr(tbl.gid, tbl.c1) && '{1_1,1_2,1_3}'::text[])  
   Heap Blocks: exact=2906  
   Buffers: shared hit=2919  
   ->  Bitmap Index Scan on idx_tbl_2  (cost=0.00..1183.38 rows=149251 width=0) (actual time=0.640..0.640 rows=2932 loops=1)  
         Index Cond: (gen_newarr(tbl.gid, tbl.c1) && '{1_1,1_2,1_3}'::text[])  
         Buffers: shared hit=13  
 Planning time: 0.102 ms  
 Execution time: 6.348 ms  
(10 rows)  

查询如下

select * from tbl where gen_newarr(gid, c1) && array['1_1','1_2','1_3'];  
  
 gid |                   c1                      
-----+-----------------------------------------  
   1 | {62,904,204,618,917,227,388,352,167,1}  
   1 | {825,126,174,409,340,285,231,942,3,136}  
   1 | {222,418,799,881,728,582,558,2,368,196}  
   1 | {847,197,690,1,288,468,179,521,799,196}  
   1 | {867,316,447,747,953,998,370,360,558,3}  
   1 | {249,963,669,929,534,945,388,816,1,601}  
   1 | {925,609,108,981,712,681,906,832,3,275}  
   1 | {3,354,253,947,588,598,401,89,246,968}  
   1 | {323,121,22,3,7,714,80,619,178,439}  
   1 | {866,1,185,704,932,882,496,324,264,882}  
......  

性能显著提升。

单值列+全文检索 复合查询加速

原理类似，将单值列与全文检索列合并

create table tbl123(gid int, ts tsvector);  
  
insert into tbl123 select random()*99, array_to_tsvector(gen_randarr(999, 10)::text[]) from generate_series(1,10000000);  

create index idx_tbl123_1 on tbl123 using gin ( array_to_tsvector(gen_newarr(gid, tsvector_to_array(ts))) );  

explain (analyze,verbose,timing,costs,buffers)  select * from tbl123 where array_to_tsvector(gen_newarr(gid, tsvector_to_array(ts))) @@ tsquery '1_1 & 1_2 & 1_3';  
  
  
explain (analyze,verbose,timing,costs,buffers)  select * from tbl123 where array_to_tsvector(gen_newarr(gid, tsvector_to_array(ts))) @@ tsquery '1_1 | 1_2 | 1_3';  
  
  
postgres=# explain (analyze,verbose,timing,costs,buffers)  select * from tbl123 where array_to_tsvector(gen_newarr(gid, tsvector_to_array(ts))) @@ tsquery '1_1 & 1_2 & 1_3';  
                                                               QUERY PLAN                                                                  
-----------------------------------------------------------------------------------------------------------------------------------------  
 Bitmap Heap Scan on public.tbl123  (cost=12.01..13.28 rows=1 width=77) (actual time=0.207..0.207 rows=0 loops=1)  
   Output: gid, ts  
   Recheck Cond: (array_to_tsvector(gen_newarr(tbl123.gid, tsvector_to_array(tbl123.ts))) @@ '''1_1'' & ''1_2'' & ''1_3'''::tsquery)  
   Buffers: shared hit=14  
   ->  Bitmap Index Scan on idx_tbl123_1  (cost=0.00..12.01 rows=1 width=0) (actual time=0.204..0.204 rows=0 loops=1)  
         Index Cond: (array_to_tsvector(gen_newarr(tbl123.gid, tsvector_to_array(tbl123.ts))) @@ '''1_1'' & ''1_2'' & ''1_3'''::tsquery)  
         Buffers: shared hit=14  
 Planning time: 0.080 ms  
 Execution time: 0.238 ms  
(9 rows)  
  
postgres=# explain (analyze,verbose,timing,costs,buffers)  select * from tbl123 where array_to_tsvector(gen_newarr(gid, tsvector_to_array(ts))) @@ tsquery '1_1 | 1_2 | 1_3';  
                                                               QUERY PLAN                                                                  
-----------------------------------------------------------------------------------------------------------------------------------------  
 Bitmap Heap Scan on public.tbl123  (cost=1220.70..136709.34 rows=149251 width=77) (actual time=0.971..5.988 rows=2970 loops=1)  
   Output: gid, ts  
   Recheck Cond: (array_to_tsvector(gen_newarr(tbl123.gid, tsvector_to_array(tbl123.ts))) @@ '''1_1'' | ''1_2'' | ''1_3'''::tsquery)  
   Heap Blocks: exact=2937  
   Buffers: shared hit=2950  
   ->  Bitmap Index Scan on idx_tbl123_1  (cost=0.00..1183.38 rows=149251 width=0) (actual time=0.612..0.612 rows=2970 loops=1)  
         Index Cond: (array_to_tsvector(gen_newarr(tbl123.gid, tsvector_to_array(tbl123.ts))) @@ '''1_1'' | ''1_2'' | ''1_3'''::tsquery)  
         Buffers: shared hit=13  
 Planning time: 0.029 ms  
 Execution time: 6.284 ms  
(10 rows)  
  
postgres=# select * from tbl123 where array_to_tsvector(gen_newarr(gid, tsvector_to_array(ts))) @@ tsquery '1_1 | 1_2 | 1_3';  
 gid |                            ts                               
-----+-----------------------------------------------------------  
   1 | '180' '219' '253' '262' '282' '3' '633' '657' '807' '809'  
   1 | '1' '166' '261' '670' '807' '860' '897' '922' '93' '964'  
   1 | '1' '174' '211' '319' '322' '532' '84' '849' '869' '993'  
......  

性能提升也非常明显。

小结

1、分区表，每个分区定义对应的索引。但是当单值类型本身包含的值非常多时，分区就要很多很多，可能也不是很好。

2、分区索引，目前PG还不支持对单个表创建多颗树的复合索引（一颗树以单值列构建，VALUE指向另一个颗树。另一颗数以多值列构建的GIN倒排树。）

3、也就是本例提到的方法，使用UDF，将单值列与多值列合并，类似变异，变成另一个多值列，在另一个多值列中包含这个单值列的属性，从而达到分区表或分区索引同样的效果。

性能提升非常明显。

当多值列本身就携带单值列属性时，我们就没有必要建立 单值列+多值列 的复合索引，仅仅构建多值列索引即可。

当多值列中没有携带单值列属性时，那么我们如果有 单值列+多值列 的复合查询条件，可以采用UDF，将 单值列+多值列 合并成一个新的多值列，对多值列构建GIN倒排索引，提速非常明显。本例提速超过100倍

参考

《PostgreSQL bitmapAnd, bitmapOr, bitmap index scan, bitmap heap scan》

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