复习一下常用的操作
1.查看集群健康状况GET /_cat/health?v&pretty2.查看my_index的mapping和setting的相关信息GET /my_index?pretty3.查看所有的indexGET /_cat/indices?v&pretty4.删除 my_index_newDELETE /my_index_new?pretty&pretty
先测试ik分词器的基本功能
GET _analyze?pretty{ "analyzer": "ik_smart", "text": "中华人民共和国国歌"} 结果：
{ "tokens": [ { "token": "中华人民共和国", "start_offset": 0, "end_offset": 7, "type": "CN_WORD", "position": 0 }, { "token": "国歌", "start_offset": 7, "end_offset": 9, "type": "CN_WORD", "position": 1 } ]}
可以看出：通过ik_smart明显很智能的将 "中华人民共和国国歌"进行了正确的分词。
另外一个例子：
GET _analyze?pretty{ "analyzer": "ik_smart", "text": "王者荣耀是最好玩的游戏"} 结果：
{ "tokens": [ { "token": "王者荣耀", "start_offset": 0, "end_offset": 4, "type": "CN_WORD", "position": 0 }, { "token": "最", "start_offset": 5, "end_offset": 6, "type": "CN_CHAR", "position": 1 }, { "token": "好玩", "start_offset": 6, "end_offset": 8, "type": "CN_WORD", "position": 2 }, { "token": "游戏", "start_offset": 9, "end_offset": 11, "type": "CN_WORD", "position": 3 } ]}
如果结果跟我的不一样，那就对了，中文ik分词词库里面将“王者荣耀”是分开的，但是我们又不愿意将其分开，根据github上面的指示可以配置
IKAnalyzer.cfg.xml 目录在：elasticsearch-5.4.0/plugins/ik/config
IK Analyzer 扩展配置 custom/mydict.dic;custom/single_word_low_freq.dic custom/ext_stopword.dic http://tagtic-slave01:82/HotWords.php http://tagtic-slave01:82/StopWords.php
可以看到HotWords.php
配置完了之后就可以看到刚才的结果了
顺便测试一下ik_max_word
GET /index/_analyze?pretty{ "analyzer": "ik_max_word", "text": "中华人民共和国国歌"} 结果看看就行了
{ "tokens": [ { "token": "中华人民共和国", "start_offset": 0, "end_offset": 7, "type": "CN_WORD", "position": 0 }, { "token": "中华人民", "start_offset": 0, "end_offset": 4, "type": "CN_WORD", "position": 1 }, { "token": "中华", "start_offset": 0, "end_offset": 2, "type": "CN_WORD", "position": 2 }, { "token": "华人", "start_offset": 1, "end_offset": 3, "type": "CN_WORD", "position": 3 }, { "token": "人民共和国", "start_offset": 2, "end_offset": 7, "type": "CN_WORD", "position": 4 }, { "token": "人民", "start_offset": 2, "end_offset": 4, "type": "CN_WORD", "position": 5 }, { "token": "共和国", "start_offset": 4, "end_offset": 7, "type": "CN_WORD", "position": 6 }, { "token": "共和", "start_offset": 4, "end_offset": 6, "type": "CN_WORD", "position": 7 }, { "token": "国", "start_offset": 6, "end_offset": 7, "type": "CN_CHAR", "position": 8 }, { "token": "国歌", "start_offset": 7, "end_offset": 9, "type": "CN_WORD", "position": 9 } ]}
再看看github上面的一个例子
POST /index/fulltext/_mapping{ "fulltext": { "_all": { "analyzer": "ik_smart" }, "properties": { "content": { "type": "text" } } }}
存一些值
POST /index/fulltext/1{ "content": "美国留给伊拉克的是个烂摊子吗"}POST /index/fulltext/2{ "content": "公安部：各地校车将享最高路权"}POST /index/fulltext/3{ "content": "中韩渔警冲突调查：韩警平均每天扣1艘中国渔船"}POST /index/fulltext/4{ "content": "中国驻洛杉矶领事馆遭亚裔男子枪击 嫌犯已自首"}
取值
POST /index/fulltext/_search{ "query": { "match": { "content": "中国" } }}
结果
{ "took": 3, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 3, "max_score": 1.0869478, "hits": [ { "_index": "index", "_type": "fulltext", "_id": "4", "_score": 1.0869478, "_source": { "content": "中国驻洛杉矶领事馆遭亚裔男子枪击 嫌犯已自首" } }, { "_index": "index", "_type": "fulltext", "_id": "3", "_score": 0.61094594, "_source": { "content": "中韩渔警冲突调查：韩警平均每天扣1艘中国渔船" } }, { "_index": "index", "_type": "fulltext", "_id": "1", "_score": 0.27179778, "_source": { "content": "美国留给伊拉克的是个烂摊子吗" } } ] }}
es会按照分词进行索引，然后根据你的查询条件按照分数的高低给出结果
官网有一个例子，可以学习学习：https://github.com/medcl/elasticsearch-analysis-ik
看另一个有趣的例子
PUT /index1{ "settings": { "refresh_interval": "5s", "number_of_shards" : 1, "number_of_replicas" : 0 }, "mappings": { "_default_":{ "_all": { "enabled": false } }, "resource": { "dynamic": false, "properties": { "title": { "type": "text", "fields": { "cn": { "type": "text", "analyzer": "ik_smart" }, "en": { "type": "text", "analyzer": "english" } } } } } }}
field的作用有二：
1.比如一个string类型可以映射成text类型来进行全文检索，keyword类型作为排序和聚合;2 相当于起了个别名，使用不同的分类器
批量插入值
POST /_bulk{ "create": { "_index": "index1", "_type": "resource", "_id": 1 } }{ "title": "周星驰最新电影" }{ "create": { "_index": "index1", "_type": "resource", "_id": 2 } }{ "title": "周星驰最好看的新电影" }{ "create": { "_index": "index1", "_type": "resource", "_id": 3 } }{ "title": "周星驰最新电影，最好，新电影" }{ "create": { "_index": "index1", "_type": "resource", "_id": 4 } }{ "title": "最最最最好的新新新新电影" }{ "create": { "_index": "index1", "_type": "resource", "_id": 5 } }{ "title": "I'm not happy about the foxes" }
取值
POST /index1/resource/_search{ "query": { "multi_match": { "type": "most_fields", "query": "fox", "fields": "title" } }}
结果
{ "took": 1, "timed_out": false, "_shards": { "total": 1, "successful": 1, "failed": 0 }, "hits": { "total": 0, "max_score": null, "hits": [] }}
原因，使用title里面查询fox,而title使用的是标准分词器，被索引的是foxes，所以不会有结果，下面这种情况就会有结果了
POST /index1/resource/_search{ "query": { "multi_match": { "type": "most_fields", "query": "fox", "fields": "title.en" } }}
结果就不列出来了，因为title.en使用的是english分词器
对比一下下面的输出，体会一下field的使用
GET /index1/resource/_search{ "query": { "match": { "title.cn": "the最好游戏" } }}POST /index1/resource/_search{ "query": { "multi_match": { "type": "most_fields", "query": "the最新游戏", "fields": [ "title", "title.cn", "title.en" ] } }}POST /index1/resource/_search{ "query": { "multi_match": { "type": "most_fields", "query": "the最新", "fields": "title.cn" } }}
根据结果体会体会用法
下面使用“王者荣耀做测试”，这里可以看到前面配置的HotWords.php是一把双刃剑，将“王者荣耀”放在里面之后，“王者荣耀”这个词就是一个整体，不会被切分成“王者”和“荣耀”，但是就是要搜索王者怎么办呢，这里就体现出fields的强大了，具体看下面
先存入数据
POST /_bulk{ "create": { "_index": "index1", "_type": "resource", "_id": 6 } }{ "title": "王者荣耀最好玩的游戏" }{ "create": { "_index": "index1", "_type": "resource", "_id": 7 } }{ "title": "王者荣耀最好玩的新游戏" }{ "create": { "_index": "index1", "_type": "resource", "_id": 8 } }{ "title": "王者荣耀最新游戏，最好玩，新游戏" }{ "create": { "_index": "index1", "_type": "resource", "_id": 9 } }{ "title": "最最最最好的新新新新游戏" }{ "create": { "_index": "index1", "_type": "resource", "_id": 10 } }{ "title": "I'm not happy about the foxes" }
查询
POST /index1/resource/_search{ "query": { "multi_match": { "type": "most_fields", "query": "王者荣耀", "fields": "title.cn" } }}#下面会没有结果返回POST /index1/resource/_search{ "query": { "multi_match": { "type": "most_fields", "query": "王者", "fields": "title.cn" } }}POST /index1/resource/_search{ "query": { "multi_match": { "type": "most_fields", "query": "王者", "fields": "title" } }}
对比结果就可以一目了然了，结果略！
所以一开始业务的需求要相当了解，才能有好的映射（mapping）被设计，搜索的时候也会省事不少
参考：
https://github.com/medcl/elasticsearch-analysis-ik
http://keenwon.com/1404.html
https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-standard-analyzer.html#_example_output