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
1CREATE 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 |
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