PostgreSQLUDF實現tsvector(全文檢索),array(陣列)多值欄位與scalar(單值欄位)型別的整合索引(類分割槽索引)-單值與多值型別複合查詢效能提速100倍+案例(含，單值+多值列合成)
標籤
PostgreSQL , 單值列 , 多值列 , GIN倒排索引 , 多值列變異 , 分割槽索引 , 分割槽表 , 變異索引
背景
在應用程式中，特別是使用PostgreSQL多值列（陣列、全文檢索、JSON）型別的業務中，除了多值列有查詢需求，單值列也有查詢需求。
那麼當兩種查詢並行存在時，資料庫會根據COST選擇單列或多列組合索引。但是效能並不能達到極致，只是簡化使用者的索引設計而已。
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個值，c1有10個值（取值範圍1000以內）。使用者可能按gid查詢，也可能按c1查詢，還可能按兩個欄位組合查詢。
當按兩個欄位組合查詢時，現有達到方法並不高效。包括btree_gin在內。
如何提速？
demo
1、建立一個產生隨機數的函式
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、建立測試表，包含一個單值列，和一個多值列
create table tbl(gid int, c1 int[]);
3、寫入1000萬資料
insert into tbl select random()*99, gen_randarr(999, 10) from generate_series(1,10000000);
傳統加速方法1
建立 單值列+多值列 的複合索引
create extension btree_gin;
set maintenance_work_mem =`8GB`;
create index idx_tbl_1 on tbl using gin (gid, c1);
複合查詢效能如何？
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)
如果你沒有繼續優化的動力，你會發現這個效能好像也蠻OK的。索引條件也用了多個。
實際上這個索引是內部將兩個索引合併後做的BITMAP SCAN。
《PostgreSQL bitmapAnd, bitmapOr, bitmap index scan, bitmap heap scan》
傳統加速方法2
實際上就是將 單值， 多值列 索引分開建。
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);
實際效果比GIN的複合索引差一些
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倍效能提升加速方法
本文提到的方法，適合於單值列與多值列混合查詢的場景，實際上我們用到了PostgreSQL的UDF和表示式索引的功能。
UDF將單值列和多值列合併，變成一個新的多值列。
表示式索引，針對這個UDF來構建。
目的是將內部的BITMAP合併這個部分化解掉，僅使用一顆倒排樹，並且這顆倒排樹包含了 單值列和多值列的值。
1、建立一個UDF，將本例的gid與c1的值合併。
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、建立表示式索引
set maintenance_work_mem =`8GB`;
create index idx_tbl_2 on tbl using gin (gen_newarr(gid, c1));
3、查詢時，使用表示式查詢，所以需要更改SQL語句
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)
查詢如下
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}
......
效能顯著提升。
單值列+全文檢索 複合查詢加速
原理類似，將單值列與全文檢索列合併
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`
......
效能提升也非常明顯。
小結
1、分割槽表，每個分割槽定義對應的索引。但是當單值型別本身包含的值非常多時，分割槽就要很多很多，可能也不是很好。
2、分割槽索引，目前PG還不支援對單個表建立多顆樹的複合索引（一顆樹以單值列構建，VALUE指向另一個顆樹。另一顆數以多值列構建的GIN倒排樹。）
3、也就是本例提到的方法，使用UDF，將單值列與多值列合併，類似變異，變成另一個多值列，在另一個多值列中包含這個單值列的屬性，從而達到分割槽表或分割槽索引同樣的效果。
效能提升非常明顯。
當多值列本身就攜帶單值列屬性時，我們就沒有必要建立 單值列+多值列 的複合索引，僅僅構建多值列索引即可。
當多值列中沒有攜帶單值列屬性時，那麼我們如果有 單值列+多值列 的複合查詢條件，可以採用UDF，將 單值列+多值列 合併成一個新的多值列，對多值列構建GIN倒排索引，提速非常明顯。本例提速超過100倍
參考
《PostgreSQL bitmapAnd, bitmapOr, bitmap index scan, bitmap heap scan》
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