我有一个小的PostgreSQL数据库(~~ 3,000行)。
我正在尝试在其中一个文本字段(“正文”)上设置全文搜索。
问题是任何查询都非常慢(35秒以上!!!)。
我认为问题来自于DB选择顺序扫描模式......
这是我的疑问:
SELECT
ts_rank_cd(to_tsvector('italian', body), query),
ts_headline('italian', body, to_tsquery('torino')),
title,
location,
id_author
FROM
fulltextsearch.documents, to_tsquery('torino') as query
WHERE
(body_tsvector @@ query)
OFFSET
0
这是EXPLAIN ANALYZE:
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------
Limit (cost=0.00..1129.81 rows=19 width=468) (actual time=74.059..13630.114 rows=863 loops=1)
-> Nested Loop (cost=0.00..1129.81 rows=19 width=468) (actual time=74.056..13629.342 rows=863 loops=1)
Join Filter: (documents.body_tsvector @@ query.query)
-> Function Scan on to_tsquery query (cost=0.00..0.01 rows=1 width=32) (actual time=4.606..4.608 rows=1 loops=1)
-> Seq Scan on documents (cost=0.00..1082.09 rows=3809 width=591) (actual time=0.045..48.072 rows=3809 loops=1)
Total runtime: 13630.720 ms
这是我的表:
mydb=# \d+ fulltextsearch.documents;
Table "fulltextsearch.documents"
Column | Type | Modifiers | Storage | Description
---------------+-------------------+-----------------------------------------------------------------------+----------+-------------
id | integer | not null default nextval('fulltextsearch.documents_id_seq'::regclass) | plain |
id_author | integer | | plain |
body | character varying | | extended |
title | character varying | | extended |
location | character varying | | extended |
date_creation | date | | plain |
body_tsvector | tsvector | | extended |
Indexes:
"fulltextsearch_documents_tsvector_idx" gin (to_tsvector('italian'::regconfig, COALESCE(body, ''::character varying)::text))
"id_idx" btree (id)
Triggers:
body_tsvectorupdate BEFORE INSERT OR UPDATE ON fulltextsearch.documents FOR EACH ROW EXECUTE PROCEDURE tsvector_update_trigger('body_tsvector', 'pg_catalog.italian', 'body')
Has OIDs: no
我确定我错过了一些明显的东西......
任何线索?
=== 更新 ==================================== ===================================
感谢您的建议,我想出了这个(更好的)查询:
SELECT
ts_rank(body_tsvector, query),
ts_headline('italian', body, query),
title,
location
FROM
fulltextsearch.documents, to_tsquery('italian', 'torino') as query
WHERE
to_tsvector('italian', coalesce(body,'')) @@ query
这是相当好的,但总是很慢(13秒以上......)。
我注意到,在查询“ts_headline()”行时,查询是闪电般快速的。
这是EXPLAIN ANALYZE,它最终使用了索引,但对我没什么帮助......:
EXPLAIN ANALYZE SELECT
clock_timestamp() - statement_timestamp() as elapsed_time,
ts_rank(body_tsvector, query),
ts_headline('italian', body, query),
title,
location
FROM
fulltextsearch.documents, to_tsquery('italian', 'torino') as query
WHERE
to_tsvector('italian', coalesce(body,'')) @@ query
Nested Loop (cost=16.15..85.04 rows=19 width=605) (actual time=102.290..13392.161 rows=863 loops=1)
-> Function Scan on query (cost=0.00..0.01 rows=1 width=32) (actual time=0.008..0.009 rows=1 loops=1)
-> Bitmap Heap Scan on documents (cost=16.15..84.65 rows=19 width=573) (actual time=0.381..4.236 rows=863 loops=1)
Recheck Cond: (to_tsvector('italian'::regconfig, (COALESCE(body, ''::character varying))::text) @@ query.query)
-> Bitmap Index Scan on fulltextsearch_documents_tsvector_idx (cost=0.00..16.15 rows=19 width=0) (actual time=0.312..0.312 rows=863 loops=1)
Index Cond: (to_tsvector('italian'::regconfig, (COALESCE(body, ''::character varying))::text) @@ query.query)
Total runtime: 13392.717 ms
答案 0 :(得分:4)
你错过了两件(相当明显的)事情:
1您已在'italian'
中设置to_tsvector()
,但未在to_tsquery()
COALESCE(body, ...)
保持一致。
2您已将{{1}}编入索引,但这不是您要搜索的内容。
规划师并不神奇 - 你只能使用索引,如果那是你正在搜索的内容。
答案 1 :(得分:0)
最后,借助你的答案和评论,以及一些谷歌搜索,我通过在完整结果集的一个子集上运行ts_headline()(我认为是一个非常繁重的函数)来解决(结果页面I感兴趣的是:
SELECT
id,
ts_headline('italian', body, to_tsquery('italian', 'torino')) as headline,
rank,
title,
location
FROM (
SELECT
id,
body,
title,
location,
ts_rank(body_tsvector, query) as rank
FROM
fulltextsearch.documents, to_tsquery('italian', 'torino') as query
WHERE
to_tsvector('italian', coalesce(body,'')) @@ query
LIMIT 10
OFFSET 0
) as s
答案 2 :(得分:0)
我通过预先计算ts_rank_cd并将其存储在语料库中的流行术语(高出现次数)的表中来解决了这个问题。搜索查看此表以获取查询字词的排序文档排名。如果不存在(对于不太流行的术语),它将默认创建ts_rank_cd。
请看一下这篇文章。