文章目录

• 概述
• 官网
• 什么是ngram
• 什么是edge ngram
• ngram和index-time搜索推荐原理
• 例子

概述

继续跟中华石杉老师学习ES，第23篇

课程地址： https://www.roncoo.com/view/55

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官网

NGram Tokenizer：
https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-ngram-tokenizer.html

NGram Token Filter:
https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-ngram-tokenfilter.html

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Edge NGram Tokenizer:
https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-edgengram-tokenizer.html

Edge NGram Token Filter：
https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-edgengram-tokenfilter.html

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什么是ngram

什么是ngram

假设有个单词quick，5种长度下的ngram

ngram length=1，会被拆成 q u i c k ngram length=2，会被拆成 qu ui ic ck ngram length=3，会被拆成 qui uic ick ngram length=4，会被拆成 quic uick ngram length=5，会被拆成 quick

其中任意一个被拆分的部分 就被称为ngram 。

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什么是edge ngram

quick，anchor首字母后进行ngram

q qu qui quic quick

上述拆分方式就被称为edge ngram

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使用edge ngram将每个单词都进行进一步的分词切分，用切分后的ngram来实现前缀搜索推荐功能

举个例子 两个doc
doc1 hello world
doc2 hello we

使用edge ngram拆分

h
he
hel
hell
hello -------> 可以匹配 doc1,doc2

w -------> 可以匹配 doc1,doc2
wo
wor
worl
world
e ---------> 可以匹配 doc2

使用hello w去搜索

hello --> hello，doc1
w --> w，doc1

doc1中hello和w，而且position也匹配，所以，ok，doc1返回，hello world

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ngram和index-time搜索推荐原理

搜索的时候，不用再根据一个前缀，然后扫描整个倒排索引了，而是简单的拿前缀去倒排索引中匹配即可，如果匹配上了，那么就好了，就和match query全文检索一样

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例子

PUT /my_index {"settings": {"analysis": {"filter": {"autocomplete_filter": { "type": "edge_ngram","min_gram": 1,"max_gram": 20}},"analyzer": {"autocomplete": {"type": "custom","tokenizer": "standard","filter": ["lowercase","autocomplete_filter" ]}}}} }

helloworld
设置

min ngram = 1 max ngram = 3

使用edge_ngram ，则会被拆分为一下 ,

h he hel

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知识点： autocomplete

https://www.elastic.co/guide/en/elasticsearch/reference/current/search-analyzer.html

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GET /my_index/_analyze {"analyzer": "autocomplete","text": "helll world" }

设置mapping , 查询的时候还是使用standard

PUT /my_index/_mapping/my_type {"properties": {"title": {"type": "text","analyzer": "autocomplete","search_analyzer": "standard"}} }

造数据

PUT /my_index/my_type/1 {"content":"hello Jack" }PUT /my_index/my_type/2 {"content":"hello John" }PUT /my_index/my_type/3 {"content":"hello Jose" }

查询

GET /my_index/my_type/_search {"query": {"match": {"content": "hello J"}} }

返回：

{"took": 7,"timed_out": false,"_shards": {"total": 5,"successful": 5,"skipped": 0,"failed": 0},"hits": {"total": 3,"max_score": 0.2876821,"hits": [{"_index": "my_index","_type": "my_type","_id": "2","_score": 0.2876821,"_source": {"content": "hello John"}},{"_index": "my_index","_type": "my_type","_id": "1","_score": 0.2876821,"_source": {"content": "hello Jack"}},{"_index": "my_index","_type": "my_type","_id": "3","_score": 0.2876821,"_source": {"content": "hello Jose"}}]} }

• 如果用match，只有hello的也会出来，全文检索，只是分数比较低
• 推荐使用match_phrase，要求每个term都有，而且position刚好靠着1位，符合我们的期望的

总结

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