postgresql 中的 like 查询优化方案

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当时数量量比较庞大的时候，做模糊查询效率很慢，为了优化查询效率，尝试如下方法做效率对比

一、对比情况说明：

1、数据量100w条数据

2、执行sql

二、对比结果

explain analyze SELECT
 c_patent,
 c_applyissno,
 d_applyissdate,
 d_applydate,
 c_patenttype_dimn,
 c_newlawstatus,
 c_abstract 
FROM
 public.t_knowl_patent_zlxx_temp 
WHERE
 c_applicant LIKE '%本溪满族自治县连山关镇安平安养殖场%';

1、未建索时执行计划：

"Gather (cost=1000.00..83803.53 rows=92 width=1278) (actual time=217.264..217.264 rows=0 loops=1)
 Workers Planned: 2
 Workers Launched: 2
 -> Parallel Seq Scan on t_knowl_patent_zlxx (cost=0.00..82794.33 rows=38 width=1278) (actual time=212.355..212.355 rows=0 loops=3)
  Filter: ((c_applicant)::text ~~ '%本溪满族自治县连山关镇安平安养殖场%'::text)
  Rows Removed by Filter: 333333
Planning time: 0.272 ms
Execution time: 228.116 ms"

2、btree索引

建索引语句

CREATE INDEX idx_public_t_knowl_patent_zlxx_applicant ON public.t_knowl_patent_zlxx(c_applicant varchar_pattern_ops);

执行计划

"Gather (cost=1000.00..83803.53 rows=92 width=1278) (actual time=208.253..208.253 rows=0 loops=1)
 Workers Planned: 2
 Workers Launched: 2
 -> Parallel Seq Scan on t_knowl_patent_zlxx (cost=0.00..82794.33 rows=38 width=1278) (actual time=203.573..203.573 rows=0 loops=3)
  Filter: ((c_applicant)::text ~~ '%本溪满族自治县连山关镇安平安养殖场%'::text)
  Rows Removed by Filter: 333333
Planning time: 0.116 ms
Execution time: 218.189 ms"

但是如果将查询sql稍微改动一下，把like查询中的前置%去掉是这样的

Index Scan using idx_public_t_knowl_patent_zlxx_applicant on t_knowl_patent_zlxx_temp (cost=0.55..8.57 rows=92 width=1278) (actual time=0.292..0.292 rows=0 loops=1)
 Index Cond: (((c_applicant)::text ~>=~ '本溪满族自治县连山关镇安平安养殖场'::text) AND ((c_applicant)::text ~<~ '本溪满族自治县连山关镇安平安养殖圻'::text))
 Filter: ((c_applicant)::text ~~ '本溪满族自治县连山关镇安平安养殖场%'::text)
Planning time: 0.710 ms
Execution time: 0.378 ms

3、gin索引

创建索引语句(postgresql要求在9.6版本及以上)

create extension pg_trgm;
CREATE INDEX idx_public_t_knowl_patent_zlxx_applicant ON public.t_knowl_patent_zlxx USING gin (c_applicant gin_trgm_ops);

执行计划

Bitmap Heap Scan on t_knowl_patent_zlxx (cost=244.71..600.42 rows=91 width=1268) (actual time=0.649..0.649 rows=0 loops=1)
 Recheck Cond: ((c_applicant)::text ~~ '%本溪满族自治县连山关镇安平安养殖场%'::text)
 -> Bitmap Index Scan on idx_public_t_knowl_patent_zlxx_applicant (cost=0.00..244.69 rows=91 width=0) (actual time=0.647..0.647 rows=0 loops=1)
  Index Cond: ((c_applicant)::text ~~ '%本溪满族自治县连山关镇安平安养殖场%'::text)
Planning time: 0.673 ms
Execution time: 0.740 ms

三、结论

btree索引可以让后置% "abc%"的模糊匹配走索引，gin + gp_trgm可以让前后置% "%abc%" 走索引。但是gin 索引也有弊端，以下情况可能导致无法命中：

搜索字段少于3个字符时，不会命中索引，这是gin自身机制导致。

当搜索字段过长时，比如email检索，可能也不会命中索引，造成原因暂时未知。

补充：PostgreSQL LIKE 查询效率提升实验

一、未做索引的查询效率

作为对比，先对未索引的查询做测试

EXPLAIN ANALYZE select * from gallery_map where author = '曹志耘';
             QUERY PLAN             
-----------------------------------------------------------------------------------------------------------------
 Seq Scan on gallery_map (cost=0.00..7002.32 rows=1025 width=621) (actual time=0.011..39.753 rows=1031 loops=1)
 Filter: ((author)::text = '曹志耘'::text)
 Rows Removed by Filter: 71315
 Planning time: 0.194 ms
 Execution time: 39.879 ms
(5 rows)
 
Time: 40.599 ms
EXPLAIN ANALYZE select * from gallery_map where author like '曹志耘';
             QUERY PLAN             
-----------------------------------------------------------------------------------------------------------------
 Seq Scan on gallery_map (cost=0.00..7002.32 rows=1025 width=621) (actual time=0.017..41.513 rows=1031 loops=1)
 Filter: ((author)::text ~~ '曹志耘'::text)
 Rows Removed by Filter: 71315
 Planning time: 0.188 ms
 Execution time: 41.669 ms
(5 rows)
 
Time: 42.457 ms
 
EXPLAIN ANALYZE select * from gallery_map where author like '曹志耘%';
             QUERY PLAN             
-----------------------------------------------------------------------------------------------------------------
 Seq Scan on gallery_map (cost=0.00..7002.32 rows=1028 width=621) (actual time=0.017..41.492 rows=1031 loops=1)
 Filter: ((author)::text ~~ '曹志耘%'::text)
 Rows Removed by Filter: 71315
 Planning time: 0.307 ms
 Execution time: 41.633 ms
(5 rows)
 
Time: 42.676 ms

很显然都会做全表扫描

二、创建btree索引

PostgreSQL默认索引是btree

CREATE INDEX ix_gallery_map_author ON gallery_map (author);
 
EXPLAIN ANALYZE select * from gallery_map where author = '曹志耘';  
                QUERY PLAN                
-------------------------------------------------------------------------------------------------------------------------------------
 Bitmap Heap Scan on gallery_map (cost=36.36..2715.37 rows=1025 width=621) (actual time=0.457..1.312 rows=1031 loops=1)
 Recheck Cond: ((author)::text = '曹志耘'::text)
 Heap Blocks: exact=438
 -> Bitmap Index Scan on ix_gallery_map_author (cost=0.00..36.10 rows=1025 width=0) (actual time=0.358..0.358 rows=1031 loops=1)
   Index Cond: ((author)::text = '曹志耘'::text)
 Planning time: 0.416 ms
 Execution time: 1.422 ms
(7 rows)
 
Time: 2.462 ms
 
EXPLAIN ANALYZE select * from gallery_map where author like '曹志耘';
                QUERY PLAN                
-------------------------------------------------------------------------------------------------------------------------------------
 Bitmap Heap Scan on gallery_map (cost=36.36..2715.37 rows=1025 width=621) (actual time=0.752..2.119 rows=1031 loops=1)
 Filter: ((author)::text ~~ '曹志耘'::text)
 Heap Blocks: exact=438
 -> Bitmap Index Scan on ix_gallery_map_author (cost=0.00..36.10 rows=1025 width=0) (actual time=0.560..0.560 rows=1031 loops=1)
   Index Cond: ((author)::text = '曹志耘'::text)
 Planning time: 0.270 ms
 Execution time: 2.295 ms
(7 rows)
 
Time: 3.444 ms
EXPLAIN ANALYZE select * from gallery_map where author like '曹志耘%';
             QUERY PLAN             
-----------------------------------------------------------------------------------------------------------------
 Seq Scan on gallery_map (cost=0.00..7002.32 rows=1028 width=621) (actual time=0.015..41.389 rows=1031 loops=1)
 Filter: ((author)::text ~~ '曹志耘%'::text)
 Rows Removed by Filter: 71315
 Planning time: 0.260 ms
 Execution time: 41.518 ms
(5 rows)
 
Time: 42.430 ms
EXPLAIN ANALYZE select * from gallery_map where author like '%研究室';
             QUERY PLAN             
-----------------------------------------------------------------------------------------------------------------
 Seq Scan on gallery_map (cost=0.00..7002.32 rows=2282 width=621) (actual time=0.064..52.824 rows=2152 loops=1)
 Filter: ((author)::text ~~ '%研究室'::text)
 Rows Removed by Filter: 70194
 Planning time: 0.254 ms
 Execution time: 53.064 ms
(5 rows)
 
Time: 53.954 ms

可以看到，等于、like的全匹配是用到索引的，like的模糊查询还是全表扫描

三、创建gin索引

CREATE EXTENSION pg_trgm;
 
CREATE INDEX ix_gallery_map_author ON gallery_map USING gin (author gin_trgm_ops);
EXPLAIN ANALYZE select * from gallery_map where author like '曹%'; 
                QUERY PLAN                
-------------------------------------------------------------------------------------------------------------------------------------
 Bitmap Heap Scan on gallery_map (cost=19.96..2705.69 rows=1028 width=621) (actual time=0.419..1.771 rows=1031 loops=1)
 Recheck Cond: ((author)::text ~~ '曹%'::text)
 Heap Blocks: exact=438
 -> Bitmap Index Scan on ix_gallery_map_author (cost=0.00..19.71 rows=1028 width=0) (actual time=0.312..0.312 rows=1031 loops=1)
   Index Cond: ((author)::text ~~ '曹%'::text)
 Planning time: 0.358 ms
 Execution time: 1.916 ms
(7 rows)
 
Time: 2.843 ms
EXPLAIN ANALYZE select * from gallery_map where author like '%耘%'; 
             QUERY PLAN             
-----------------------------------------------------------------------------------------------------------------
 Seq Scan on gallery_map (cost=0.00..7002.32 rows=1028 width=621) (actual time=0.015..51.641 rows=1031 loops=1)
 Filter: ((author)::text ~~ '%耘%'::text)
 Rows Removed by Filter: 71315
 Planning time: 0.268 ms
 Execution time: 51.957 ms
(5 rows)
 
Time: 52.899 ms
EXPLAIN ANALYZE select * from gallery_map where author like '%研究室%';
                QUERY PLAN                
-------------------------------------------------------------------------------------------------------------------------------------
 Bitmap Heap Scan on gallery_map (cost=31.83..4788.42 rows=2559 width=621) (actual time=0.914..4.195 rows=2402 loops=1)
 Recheck Cond: ((author)::text ~~ '%研究室%'::text)
 Heap Blocks: exact=868
 -> Bitmap Index Scan on ix_gallery_map_author (cost=0.00..31.19 rows=2559 width=0) (actual time=0.694..0.694 rows=2402 loops=1)
   Index Cond: ((author)::text ~~ '%研究室%'::text)
 Planning time: 0.306 ms
 Execution time: 4.403 ms
(7 rows)
 
Time: 5.227 ms

gin_trgm索引的效果好多了

由于pg_trgm的索引是把字符串切成多个3元组，然后使用这些3元组做匹配，所以gin_trgm索引对于少于3个字符（包括汉字）的查询，只有前缀匹配会走索引

另外，还测试了btree_gin，效果和btree一样

注意：

gin_trgm要求数据库必须使用UTF-8编码

demo_v1 # \l demo_v1
        List of databases
 Name | Owner | Encoding | Collate | Ctype | Access privileges
---------+-----------+----------+-------------+-------------+-------------------
 demo_v1 | wmpp_user | UTF8  | en_US.UTF-8 | en_US.UTF-8 |

以上为个人经验，希望能给大家一个参考，也希望大家多多支持。如有错误或未考虑完全的地方，望不吝赐教。

postgresql,like查询优化

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postgresql 中的 like 查询优化方案

一、对比情况说明：

二、对比结果

1、未建索时执行计划：

2、btree索引

3、gin索引

三、结论

一、未做索引的查询效率

二、创建btree索引

三、创建gin索引

postgresql,like查询优化

PostgreSQL存储过程循环调用方式

postgresql 存储函数调用变量的3种方法小结

评论“postgresql 中的 like 查询优化方案”

RTX 5090要首发性能要翻倍！三星展示GDDR7显存

友情链接

postgresql 中的 like 查询优化方案

一、对比情况说明：

二、对比结果

1、未建索时执行计划：

2、btree索引

3、gin索引

三、结论

一、未做索引的查询效率

二、创建btree索引

三、创建gin索引

postgresql,like查询优化

PostgreSQL存储过程循环调用方式

postgresql 存储函数调用变量的3种方法小结

评论“postgresql 中的 like 查询优化方案”

RTX 5090要首发 性能要翻倍！三星展示GDDR7显存

友情链接

RTX 5090要首发性能要翻倍！三星展示GDDR7显存