1、ngram和index-time搜索推荐原理
什么是ngram
quick，5种长度下的ngram
ngram length=1，q u i c k
ngram length=2，qu ui ic ck
ngram length=3，qui uic ick
ngram length=4，quic uick
ngram length=5，quick
什么是edge ngram
quick，anchor首字母后进行ngram
q
qu
qui
quic
quick
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
hello world
hello we
h
he
hel
hell
hello doc1,doc2
w doc1,doc2
wo
wor
worl
world
e doc2
helloworld
min ngram = 1
max ngram = 3
h
he
hel
hello w
hello --> hello，doc1
w --> w，doc1
doc1，hello和w，而且position也匹配，所以，ok，doc1返回，hello world
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; 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; match，全文检索
2、实验一下ngram
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"
]
}
}
}
}
}
GET /my_index/_analyze
{
"analyzer": "autocomplete",
"text": "quick brown"
}
PUT /my_index/_mapping/my_type
{
"properties": {
"title": {
"type": "string",
"analyzer": "autocomplete",
"search_analyzer": "standard"
}
}
}
hello world
h
he
hel
hell
hello
w
wo
wor
worl
world
hello w
h
he
hel
hell
hello
w
hello w --> hello --> w
GET /my_index/my_type/_search
{
"query": {
"match_phrase": {
"title": "hello w"
}
}
}
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
