LC Pinyin Analysis for Elasticsearch
Lc Pinyin版本
| LC version | ES version |
|---|---|
| master | 5.0.1 -> master |
| 5.0.1.1 | 5.0.1 |
| 2.2.2.1 | 2.2.2 |
| 1.4.5.1 | 1.4.5 |
Lc Pinyin介绍
elasticsearch-analysis-lc-pinyin是一款elasticsearch拼音分词插件，可以支持按照全拼、首字母，中文混合搜索。 例如我们在某宝搜索框中输入“jianpan” 可以搜索到关键字包含“键盘”的商品。不仅仅输入全拼，有时候我们输入首字母、拼音和首字母、中文和首字母的混合输入，比如：“键pan”、“j盘”、“jianp”、“jpan”、“jianp”、“jp” 等等，都应该匹配到键盘。通过elasticsearch-analysis-lc-pinyin这个插件就能做到类似的搜索
- 此拼音插件主要用在
短文档的搜索上，如文章的标题、作者，商品的品牌等，不建议用在长文档中
分词器
lc_index: 该分词器用于索引数据时指定lc_search: 该分词器用于拼音搜索时指定，按最小拼音分词个数拆分拼音，优先拆分全拼lc_first_letter: 该分词器用于首字母搜索，只按照单字母拆分
使用示例
1.创建一个索引index
curl -XPUT http://localhost:9200/index2.创建类型brand的mapping
curl -XPOST http://localhost:9200/index/_mapping/brand -d'
{
"brand": {
"properties": {
"name": {
"type": "text",
"analyzer": "lc_index",
"search_analyzer": "lc_search",
"term_vector": "with_positions_offsets"
}
}
}
}'3.索引一些互联网公司的名字
curl -XPOST http://localhost:9200/index/brand/1 -d'{"name":"百度"}'
curl -XPOST http://localhost:9200/index/brand/2 -d'{"name":"阿里巴巴"}'
curl -XPOST http://localhost:9200/index/brand/3 -d'{"name":"腾讯"}'
curl -XPOST http://localhost:9200/index/brand/4 -d'{"name":"网易"}'
curl -XPOST http://localhost:9200/index/brand/5 -d'{"name":"饿了么"}'
curl -XPOST http://localhost:9200/index/brand/6 -d'{"name":"百姓网"}'
curl -XPOST http://localhost:9200/index/brand/7 -d'{"name":"滴滴打车"}'
curl -XPOST http://localhost:9200/index/brand/8 -d'{"name":"百度糯米"}'
curl -XPOST http://localhost:9200/index/brand/9 -d'{"name":"大众点评"}'
curl -XPOST http://localhost:9200/index/brand/10 -d'{"name":"携程旅行网"}'4.编写高亮查询DSL
# 此示例通过`lc_search`分词器配合`match_phrase`查询实现品牌的`全拼`搜索
# 搜索全拼关键字`baidu`，请求DSL如下：
curl -XPOST http://localhost:9200/index/brand/_search -d'
{
"query": {
"match": {
"name": {
"query": "baidu",
"analyzer": "lc_search",
"type": "phrase"
}
}
},
"highlight" : {
"pre_tags" : ["<tag1>"],
"post_tags" : ["</tag1>"],
"fields" : {
"name" : {}
}
}
}'
# 匹配到`百度`、`百度糯米`两个品牌
# tip：`百度`排在`百度糯米`的前面，因为name字段长度更短
#查询结果：
{
"took": 18,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 2.5751648,
"hits": [
{
"_index": "index",
"_type": "brand",
"_id": "1",
"_score": 2.5751648,
"_source": {
"name": "百度"
},
"highlight": {
"name": [
"<tag1>百度</tag1>"
]
}
}
,
{
"_index": "index",
"_type": "brand",
"_id": "8",
"_score": 2.0601318,
"_source": {
"name": "百度糯米"
},
"highlight": {
"name": [
"<tag1>百度</tag1>糯米"
]
}
}
]
}
}
# 此示例通过`lc_search`分词器配合`match_phrase`查询实现品牌的`中文&全拼`搜索
# 搜索全拼关键字`xie程lu行wang`，请求DSL如下：
curl -XPOST http://localhost:9200/index/brand/_search -d'
{
"query": {
"match": {
"name": {
"query": "xie程lu行",
"analyzer": "lc_search",
"type": "phrase"
}
}
},
"highlight" : {
"pre_tags" : ["<tag1>"],
"post_tags" : ["</tag1>"],
"fields" : {
"name" : {}
}
}
}'
#匹配到`携程旅行网` 结果如下：
{
"took": 4,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 4.5665164,
"hits": [
{
"_index": "index",
"_type": "brand",
"_id": "10",
"_score": 4.5665164,
"_source": {
"name": "携程旅行网"
},
"highlight": {
"name": [
"<tag1>携程旅行</tag1>网"
]
}
}
]
}
}
# 此示例通过`lc_search`分词器配合`match_phrase`查询实现品牌的`首字母`搜索
# 此示例中也可以通过`lc_first_letter`分词器搜索，结果和`lc_search`一样
#
# 这两个分词器的主要区别:
# lc_first_letterl 会把所有输入的字母拆分成单字母用于首字母匹配
# lc_search 会优先把输入的字母串拆分成全拼,并找到一个最优拆分结果
#
# 搜索全拼关键字`albb`，请求DSL如下：
curl -XPOST http://localhost:9200/index/brand/_search -d'
{
"query": {
"match": {
"name": {
"query": "albb",
"analyzer": "lc_search",
"type": "phrase"
}
}
},
"highlight" : {
"pre_tags" : ["<tag1>"],
"post_tags" : ["</tag1>"],
"fields" : {
"name" : {}
}
}
}'
#匹配到`阿里巴巴`，结果如下：
{
"took": 4,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 3.9560113,
"hits": [
{
"_index": "index",
"_type": "brand",
"_id": "2",
"_score": 3.9560113,
"_source": {
"name": "阿里巴巴"
},
"highlight": {
"name": [
"<tag1>阿里巴巴</tag1>"
]
}
}
]
}
}//java api实现
//该查询会匹配`阿里巴巴`这条数据
QueryBuilder pinyinQueryBuilder = QueryBuilders.matchPhraseQuery("name", "ali巴b").analyzer("lc_search");
SearchRequestBuilder requestBuilder = client.prepareSearch("index").setTypes("brand");
requestBuilder.setQuery(pinyinQueryBuilder)
.setHighlighterPreTags("<tag1>")
.setHighlighterPostTags("</tag1>")
.addHighlightedField("name")
.execute().actionGet();
//该查询会匹配`大众点评`这条数据
QueryBuilder pinyinQueryBuilder = QueryBuilders.matchPhraseQuery("name", "dzdp").analyzer("lc_first_letter");
SearchRequestBuilder requestBuilder = client.prepareSearch("index").setTypes("brand");
requestBuilder.setQuery(pinyinQueryBuilder)
.setHighlighterPreTags("<tag1>")
.setHighlighterPostTags("</tag1>")
.addHighlightedField("name")
.execute().actionGet();
//该查询也会匹配`大众点评`这条数据
QueryBuilder pinyinQueryBuilder = QueryBuilders.matchPhraseQuery("name", "dzdp").analyzer("lc_search");
SearchRequestBuilder requestBuilder = client.prepareSearch("index").setTypes("brand");
requestBuilder.setQuery(pinyinQueryBuilder)
.setHighlighterPreTags("<tag1>")
.setHighlighterPostTags("</tag1>")
.addHighlightedField("name")
.execute().actionGet();作者: @陈楠 Email: [email protected]
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