业务需求（使用背景）：

1. 实现搜索引擎前缀搜索功能(中文,拼音前缀查询及简拼前缀查询功能)
2. 实现摘要全文检索功能，及标题加权处理功能(按照标题权值高内容权值相对低的权值分配规则，按照索引的相关性进行排序，列出前20条相关性最高的文章)

一、搜索引擎前缀搜索功能：

中文搜索：
1、搜索“刘”，匹配到“刘德华”、“刘斌”、“刘德志”
2、搜索“刘德”，匹配到“刘德华”、“刘德志”
3、搜索“德华”，匹配到“刘德华”
小结：搜索的文字需要匹配到集合中所有名字的子集。
全拼搜索：
1、搜索“li”，匹配到“刘德华”、“刘斌”、“刘德志”
2、搜索“liud”，匹配到“刘德华”、“刘德”
3、搜索“liudeh”，匹配到“刘德华”
小结：搜索的文字转换成拼音后，需要匹配到集合中所有名字转成拼音后的子集

简拼搜索：
1、搜索“w”，匹配到“我是中国人”，“我爱我的祖国”
2、搜索“wszg”，匹配到“我是中国人”
小结：搜索的文字取拼音首字母进行组合，需要匹配到组合字符串中前缀匹配的子集

解决方案：

方案一：将“like”搜索的字段的中、英简拼、英全拼 分别用索引的三个字段来进行存储并且不进行分词，最简单直接(倒排索引存储它们本身数据)，检索索引数据的时候进行 通配符查询（like查询），从这三个字段中分别进行搜索，查询匹配的记录然后返回。（优势：存储格式简单，倒排索引存储的数据量最少。缺点：like索引数据的时候开销比较大 prefix 查询比 term 查询开销大得多）

方案二：将中、中简拼、中全拼 用一个字段衍生出三个字段(multi-field)来存储三种数据，并且分词器filter采用edge_ngram类型对分词的数据进行，然后处理存储到倒排索引中,当检索索引数据时，检索所有字段的数据。（优势：格式紧凑，检索索引数据的时候采用term 全匹配规则，也无需对入参进行分词，查询效率高。缺点：采用以空间换时间的策略，但是对索引来说可以接受。采用衍生字段来存储，增加了存储及检索的复杂度，对于三个字段搜索会将相关度相加，容易混淆查询相关度结果）

方案三：将索引数据存储在一个不需分词的字段中(keyword), 生成倒排索引时进行三种类型倒排索引的生成，倒排索引生成的时候采用edge_ngram 对倒排进一步拆分，以满足业务场景需求，检索时不对入参进行分词。（优势：索引数据存储简单，，检索索引数据的时只需对一个字段 采用term 全匹配查询规则，查询效率极高。缺点：采用以空间换时间的策略——比方案二要少，对索引数据来说可以接受。）

ES 针对这一业务场景解决方案还有很多种，先列出比较典型的这三种方案，选择方案三来进行处理。

准备工作：

• pinyin分词插件安装及参数解读
• ElasticSearch edge_ngram 使用
• ElasticSearch multi-field 使用
• ElasticSearch 多种查询特性熟悉

代码：

baidu_settings.json:

{   "refresh_interval":"2s",   "number_of_replicas":1,   "number_of_shards":2,   "analysis":{     "filter":{       "autocomplete_filter":{         "type":"edge_ngram",         "min_gram":1,         "max_gram":15       },       "pinyin_first_letter_and_full_pinyin_filter" : {         "type" : "pinyin",         "keep_first_letter" : true,         "keep_full_pinyin" : false,         "keep_joined_full_pinyin": true,         "keep_none_chinese" : false,         "keep_original" : false,         "limit_first_letter_length" : 16,         "lowercase" : true,         "trim_whitespace" : true,         "keep_none_chinese_in_first_letter" : true       },       "full_pinyin_filter" : {         "type" : "pinyin",         "keep_first_letter" : true,         "keep_full_pinyin" : false,         "keep_joined_full_pinyin": true,         "keep_none_chinese" : false,         "keep_original" : true,         "limit_first_letter_length" : 16,         "lowercase" : true,         "trim_whitespace" : true,         "keep_none_chinese_in_first_letter" : true       }     },     "analyzer":{       "full_prefix_analyzer":{         "type":"custom",         "char_filter": [           "html_strip"         ],         "tokenizer":"keyword",         "filter":[           "lowercase",           "full_pinyin_filter",           "autocomplete_filter"         ]       },       "chinese_analyzer":{         "type":"custom",         "char_filter": [           "html_strip"         ],         "tokenizer":"keyword",         "filter":[           "lowercase",           "autocomplete_filter"         ]       },       "pinyin_analyzer":{         "type":"custom",         "char_filter": [           "html_strip"         ],         "tokenizer":"keyword",         "filter":[           "pinyin_first_letter_and_full_pinyin_filter",           "autocomplete_filter"         ]       }     }   } }

baidu_mapping.json

{   "baidu_type": {     "properties": {       "full_name": {         "type":  "text",         "analyzer": "full_prefix_analyzer"       },       "age": {         "type":  "integer"       }     }   } } 

public class PrefixTest {      @Test     public void testCreateIndex() throws Exception{         TransportClient client = ESConnect.getInstance().getTransportClient();         //定义索引         BaseIndex.createWithSetting(client,"baidu_index","esjson/baidu_settings.json");         //定义类型及字段详细设计         BaseIndex.createMapping(client,"baidu_index","baidu_type","esjson/baidu_mapping.json");     }     @Test     public void testBulkInsert() throws Exception{         TransportClient client = ESConnect.getInstance().getTransportClient();         List<Object> list = new ArrayList<>();         list.add(new BulkInsert(12l,"我们都有一个家名字叫中国",12));         list.add(new BulkInsert(13l,"兄弟姐妹都很多景色也不错 ",13));         list.add(new BulkInsert(14l,"家里盘着两条龙是长江与黄河",14));         list.add(new BulkInsert(15l,"还有珠穆朗玛峰儿是最高山坡",15));         list.add(new BulkInsert(16l,"我们都有一个家名字叫中国",16));         list.add(new BulkInsert(17l,"兄弟姐妹都很多景色也不错",17));         list.add(new BulkInsert(18l,"看那一条长城万里在云中穿梭",18));         boolean flag = BulkOperation.batchInsert(client,"baidu_index","baidu_type",list);         System.out.println(flag);     } }

不要意思，代码封装了，java生成索引网上查方式即可：重点不在java代码怎么实现。而是上面的思想。

接下来查看下定义的分词器效果：

http://192.168.20.114:9200/baidu_index/_analyze?text=刘德华AT2016&analyzer=full_prefix_analyzer

{     "tokens": [         {             "token": "刘",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "刘德",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "刘德华",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "刘德华a",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "刘德华at",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "刘德华at2",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "刘德华at20",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "刘德华at201",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "刘德华at2016",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "l",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "li",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "liu",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "liud",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "liude",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "liudeh",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "liudehu",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "liudehua",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "l",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "ld",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "ldh",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "ldha",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "ldhat",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "ldhat2",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "ldhat20",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "ldhat201",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         },         {             "token": "ldhat2016",             "start_offset": 0,             "end_offset": 9,             "type": "word",             "position": 0         }     ] }

大功告成。

参考：

http://blog.csdn.net/napoay/article/details/53907921
https://elasticsearch.cn/question/407
http://blog.csdn.net/xifeijian/article/details/51095762
http://www.cnblogs.com/xing901022/p/5910139.html
http://www.cnblogs.com/clonen/p/6674492.html

全文检索后续有时间再进行整理。