1.找到合适的版本
| IK version | ES version |
|---|---|
| 6.1.1 | 6.1.1 |
| 5.6.4 | 5.6.4 |
| 5.5.3 | 5.5.3 |
| 5.4.3 | 5.4.3 |
| 5.3.3 | 5.3.3 |
| 5.2.2 | 5.2.2 |
| 5.1.2 | 5.1.2 |
| 1.10.6 | 2.4.6 |
| 1.9.5 | 2.3.5 |
| 1.8.1 | 2.2.1 |
| 1.7.0 | 2.1.1 |
| 1.5.0 | 2.0.0 |
| 1.2.6 | 1.0.0 |
| 1.2.5 | 0.90.x |
| 1.1.3 | 0.20.x |
| 1.0.0 | 0.16.2 -> 0.19.0 |
2.下载对应的zip包
在本地elasticsearch根目录下的plugins下新建ik文件夹，将zip包中内容解压到该文件夹下。
现在重启es就可以了
3. 安装kibana进场测试
创建index，type
PUT /yf-springboot-es-ik1 POST /yf-springboot-es-ik1/springboot-test/_mapping { "springboot-test": { "properties": { "keyword": { "type": "text", "analyzer": "ik_max_word", "search_analyzer": "ik_max_word" } } } }
使用浏览器进行测试：
http://localhost:9200/yf-springboot-es-ik1/springboot-test/14/_termvectors?fields=keyword
ip + 端口 + index + type + id + _termvectors + ?fields = 搜索字段
POST /yf-springboot-es-ik1/springboot-test/14 {"keyword":"中国驻洛杉矶领事馆遭亚裔男子枪击 嫌犯已自首"}
{ "_index": "yf-springboot-es-ik1", "_type": "springboot-test", "_id": "14", "_version": 4, "found": true, "took": 0, "term_vectors": { "keyword": { "field_statistics": { "sum_doc_freq": 14, "doc_count": 1, "sum_ttf": 14 }, "terms": { "中国": { "term_freq": 1, "tokens": [ { "position": 0, "start_offset": 0, "end_offset": 2 } ] }, "亚裔": { "term_freq": 1, "tokens": [ { "position": 7, "start_offset": 10, "end_offset": 12 } ] }, "嫌犯": { "term_freq": 1, "tokens": [ { "position": 11, "start_offset": 17, "end_offset": 19 } ] }, "子枪": { "term_freq": 1, "tokens": [ { "position": 9, "start_offset": 13, "end_offset": 15 } ] }, "已": { "term_freq": 1, "tokens": [ { "position": 12, "start_offset": 19, "end_offset": 20 } ] }, "枪击": { "term_freq": 1, "tokens": [ { "position": 10, "start_offset": 14, "end_offset": 16 } ] }, "洛杉矶": { "term_freq": 1, "tokens": [ { "position": 2, "start_offset": 3, "end_offset": 6 } ] }, "男子": { "term_freq": 1, "tokens": [ { "position": 8, "start_offset": 12, "end_offset": 14 } ] }, "自首": { "term_freq": 1, "tokens": [ { "position": 13, "start_offset": 20, "end_offset": 22 } ] }, "遭": { "term_freq": 1, "tokens": [ { "position": 6, "start_offset": 9, "end_offset": 10 } ] }, "领事": { "term_freq": 1, "tokens": [ { "position": 4, "start_offset": 6, "end_offset": 8 } ] }, "领事馆": { "term_freq": 1, "tokens": [ { "position": 3, "start_offset": 6, "end_offset": 9 } ] }, "馆": { "term_freq": 1, "tokens": [ { "position": 5, "start_offset": 8, "end_offset": 9 } ] }, "驻": { "term_freq": 1, "tokens": [ { "position": 1, "start_offset": 2, "end_offset": 3 } ] } } } } }
使用kibana添加数据并且查询
POST /yf-springboot-es-ik1/springboot-test/11 {"keyword":"美国留给伊拉克的是个烂摊子吗"} POST /yf-springboot-es-ik1/springboot-test/12 {"keyword":"公安部：各地校车将享最高路权"} POST /yf-springboot-es-ik1/springboot-test/13 {"keyword":"中韩渔警冲突调查：韩警平均每天扣1艘中国渔船"} POST /yf-springboot-es-ik1/springboot-test/14 {"keyword":"中国驻洛杉矶领事馆遭亚裔男子枪击 嫌犯已自首"} POST /yf-springboot-es-ik1/springboot-test/_search { "query" : { "match" : { "keyword" : "中国" }}, "highlight" : { "pre_tags" : ["<tag1>", "<tag2>"], "post_tags" : ["</tag1>", "</tag2>"], "fields" : { "keyword" : {} } } }
查询结果：
{ "took" : 0, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 2, "max_score" : 0.5480699, "hits" : [ { "_index" : "yf-springboot-es-ik1", "_type" : "springboot-test", "_id" : "13", "_score" : 0.5480699, "_source" : { "keyword" : "中韩渔警冲突调查：韩警平均每天扣1艘中国渔船" }, "highlight" : { "keyword" : [ "中韩渔警冲突调查：韩警平均每天扣1艘<tag1>中国</tag1>渔船" ] } }, { "_index" : "yf-springboot-es-ik1", "_type" : "springboot-test", "_id" : "14", "_score" : 0.2876821, "_source" : { "keyword" : "中国驻洛杉矶领事馆遭亚裔男子枪击 嫌犯已自首" }, "highlight" : { "keyword" : [ "<tag1>中国</tag1>驻洛杉矶领事馆遭亚裔男子枪击 嫌犯已自首" ] } } ] } }
4.ik_max_word 和 ik_smart 什么区别?
ik_max_word: 会将文本做最细粒度的拆分，比如会将“中华人民共和国国歌”拆分为“中华人民共和国,中华人民,中华,华人,人民共和国,人民,人,民,共和国,共和,和,国国,国歌”，会穷尽各种可能的组合；
ik_smart: 会做最粗粒度的拆分，比如会将“中华人民共和国国歌”拆分为“中华人民共和国,国歌”