| LC version | ES version |
|---|---|
| master | 5.3.0 -> master |
| 5.3.0.1 | 5.3.0 |
| 5.2.2.1 | 5.2.2 |
| 5.2.0.1 | 5.2.0 |
| 5.1.2.1 | 5.1.2 |
| 5.1.1.1 | 5.1.1 |
| 5.0.2.1 | 5.0.2 |
| 5.0.1.2 | 5.0.1 |
| 5.0.1.1 | 5.0.1 |
| 2.4.2.1 | 2.4.2 |
| 2.2.2.1 | 2.2.2 |
| 1.4.5.2 | 1.4.5 |
| 1.4.5.1 | 1.4.5 |
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
lc_index: 该分词器用于索引数据时指定，将中文转换为全拼和首字，同时保留中文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_index：参数 mode: full_pinyin，first_letter，chinese_charlc_search：参数 mode: smart_pinyin，single_letter
lc_full_pinyin：将中文Token转全拼(支持多音字)lc_first_letter：将中文Token转首字母(支持多音字)
以下是使用ik分词结合拼音过滤器使用例子：
- 创建一个索引，并定义分析器
PUT /index
{
"settings": {
"analysis": {
"analyzer": {
"ik_letter_smart": {
"type": "custom",
"tokenizer": "ik_max_word",
"filter": [
"lc_first_letter"
]
},
"ik_py_smart": {
"type": "custom",
"tokenizer": "ik_max_word",
"filter": [
"lc_full_pinyin"
]
}
}
}
}
}- 测试分词
curl -XGET 'http://192.168.0.109:9200/index/_analyze?analyzer=ik_py_smart&pretty&text=英雄难过美人关'
#返回词条：
{
"tokens" : [
{
"token" : "yingxiongnanguomeirenguan",
"start_offset" : 0,
"end_offset" : 7,
"type" : "CN_WORD",
"position" : 0
},
{
"token" : "yingxiongnanguo",
"start_offset" : 0,
"end_offset" : 4,
"type" : "CN_WORD",
"position" : 1
},
{
"token" : "yingxiong",
"start_offset" : 0,
"end_offset" : 2,
"type" : "CN_WORD",
"position" : 2
},
{
"token" : "nanguo",
"start_offset" : 2,
"end_offset" : 4,
"type" : "CN_WORD",
"position" : 3
},
{
"token" : "meirenguan",
"start_offset" : 4,
"end_offset" : 7,
"type" : "CN_WORD",
"position" : 4
},
{
"token" : "meiren",
"start_offset" : 4,
"end_offset" : 6,
"type" : "CN_WORD",
"position" : 5
},
{
"token" : "guan",
"start_offset" : 6,
"end_offset" : 7,
"type" : "CN_CHAR",
"position" : 6
}
]
}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.索引一些互联网品牌
curl -XPOST http://localhost:9200/index/brand/1 -d'{"name":"百度"}'
curl -XPOST http://localhost:9200/index/brand/8 -d'{"name":"百度糯米"}'
curl -XPOST http://localhost:9200/index/brand/2 -d'{"name":"阿里巴巴"}'
curl -XPOST http://localhost:9200/index/brand/3 -d'{"name":"腾讯科技"}'
curl -XPOST http://localhost:9200/index/brand/4 -d'{"name":"网易游戏"}'
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：
#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: 465360798@qq.com
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