标签：ima custom 分词器 join 结果 代码 入参 处理 lag
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)
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”
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
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，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分词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。
myself_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"
]
}
}
}
}
myself_mapping.json
{
"test_type": {
"properties": {
"full_name": {
"type": "text",
"analyzer": "full_prefix_analyzer"
},
"age": {
"type": "integer"
}
}
}
}
工程目录：
测试项目代码：
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); } }
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
得到的结果内容为：
{
"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
}
]
}
看到以上结果，则表明大功告成了！
标签：ima custom 分词器 join 结果 代码 入参 处理 lag
原文地址：http://www.cnblogs.com/liang1101/p/7642603.html
