PostgreSQL 中的 Edge NGram 搜索

Eug*_*nij 3 postgresql full-text-search elasticsearch pg-trgm

我需要为大量公司(超过 80,000,000 家)进行搜索时自动完成。公司名称应包含以这样的搜索查询开头的单词

+-------+----------------------------------+
| term  | results                          |
+-------+----------------------------------+ 
| gen   | general motors; general electric |
| geno  | genoptix; genomic health         |
| genom | genoma group; genomic health     |
+-------+----------------------------------+
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pg_trgm模块和GIN索引实现类似行为,但并没有解决我的问题。

例如,ElasticSearch 具有完全符合我的要求的Edge NGram Tokenizer功能。

从文档:

The edge_ngram tokenizer first breaks the text down into words 
whenever it encounters one of a list of specified characters, 
then it emits N-grams of each word 
where the start of the N-gram is anchored to the beginning of the word.

Edge N-Grams are useful for search-as-you-type queries.
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PostgreSQL 中有类似的解决方案吗?

Eug*_*nij 8

我创建了一个自定义标记器

CREATE OR REPLACE FUNCTION edge_gram_tsvector(text text) RETURNS tsvector AS
$BODY$
BEGIN
    RETURN (select array_to_tsvector((select array_agg(distinct substring(lexeme for len)) from unnest(to_tsvector(text)), generate_series(1,length(lexeme)) len)));
END;
$BODY$
IMMUTABLE
language plpgsql;
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此函数创建所有这样的边缘 ngram

postgres=# select edge_gram_tsvector('general electric');
                               edge_gram_tsvector
-----------------------------------------------------------------------------------------
 'e' 'el' 'ele' 'elec' 'elect' 'electr' 'g' 'ge' 'gen' 'gene' 'gener' 'genera' 'general'
(1 row)
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然后我GINtsquery创建一个索引

create index on company using gin(edge_gram_tsvector(name));
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搜索查询将如下所示

b2bdb_master=# select name from company where edge_gram_tsvector(name) @@ 'electric'::tsquery limit 3;
                    name
--------------------------------------------
 General electric
 Electriciantalk
 Galesburg Electric Industrial Supply
(3 rows)
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解决方案的性能相当高


explain analyse select * from company where edge_gram_tsvector(name) @@ 'electric'::tsquery;


Bitmap Heap Scan on company  (cost=175.13..27450.31 rows=20752 width=2247) (actual time=0.224..1.019 rows=343 loops=1)
  Recheck Cond: (edge_gram_tsvector((name)::text) @@ '''electric'''::tsquery)
  Heap Blocks: exact=342
  ->  Bitmap Index Scan on company_edge_gram_tsvector_idx  (cost=0.00..169.94 rows=20752 width=0) (actual time=0.138..0.138 rows=343 loops=1)
        Index Cond: (edge_gram_tsvector((name)::text) @@ '''electric'''::tsquery)
Planning Time: 0.216 ms
Execution Time: 1.100 ms
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  • 这很好。您可以添加相关性排名吗?我试图弄清楚如何将 ts_rank_cd 与此方案一起使用。 (3认同)