02 elasticsearch学习笔记-IK分词器？

1. 什么是IK分词器

2. 下载IK分词器

下载地址，版本要和ES的版本对应上

https://github.com/medcl/elasticsearch-analysis-ik/releases/tag/v7.3.1

注意下载zip版本

下载完毕后，放入到我们的elasticsearch/plus/ik插件目录里即可

3. 重启观察ES，可以看到IK分词器被加载了！
   

4. elasticsearch-plugin可以通过这个命令查看加载进来的插件
   

docker下载的安装的查看方法

root@haima-PC:/usr/local/docker/efk/docker_compose_efk# docker ps -a
CONTAINER ID        IMAGE                                                 COMMAND                  CREATED             STATUS                      PORTS                                                          NAMES
c43439e99ff0        docker_compose_efk_fluentd                            "/bin/entrypoint.sh …"   29 seconds ago      Up 27 seconds               5140/tcp, 0.0.0.0:24224->24224/tcp, 0.0.0.0:24224->24224/udp   docker_compose_efk_fluentd_1
00eda4c1585d        docker_compose_efk_kibana                             "/usr/local/bin/dumb…"   29 seconds ago      Up 26 seconds               0.0.0.0:5601->5601/tcp                                         docker_compose_efk_kibana_1
6e2e42f9e3ca        docker.elastic.co/elasticsearch/elasticsearch:7.3.1   "/usr/local/bin/dock…"   31 seconds ago      Up 29 seconds               0.0.0.0:9200->9200/tcp, 9300/tcp                               docker_compose_efk_elasticsearch_1
root@haima-PC:/usr/local/docker/efk/docker_compose_efk# docker exec -it docker_compose_efk_elasticsearch_1 bash ./bin/elasticsearch-plugin list
ik

详细查看我github里的readme.md

https://github.com/haimait/docker_compose_efk

5. 使用kibana测试！

查看不同的分词效果

ik_smart和ik_max_word,其中ik_smart为最少切分

GET _analyze
{
  "analyzer":"ik_smart",
  "text": "我是中国人"
}

划出了3组

{
  "tokens" : [
    {
      "token" : "我",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "CN_CHAR",
      "position" : 0
    },
    {
      "token" : "是",
      "start_offset" : 1,
      "end_offset" : 2,
      "type" : "CN_CHAR",
      "position" : 1
    },
    {
      "token" : "中国人",
      "start_offset" : 2,
      "end_offset" : 5,
      "type" : "CN_WORD",
      "position" : 2
    }
  ]
}

ik_max_word为最细粒度划分，穷尽词库的可能

GET _analyze
{
  "analyzer":"ik_max_word",
  "text": "我是中国人"
}

划出了5组

{
  "tokens" : [
    {
      "token" : "我",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "CN_CHAR",
      "position" : 0
    },
    {
      "token" : "是",
      "start_offset" : 1,
      "end_offset" : 2,
      "type" : "CN_CHAR",
      "position" : 1
    },
    {
      "token" : "中国人",
      "start_offset" : 2,
      "end_offset" : 5,
      "type" : "CN_WORD",
      "position" : 2
    },
    {
      "token" : "中国",
      "start_offset" : 2,
      "end_offset" : 4,
      "type" : "CN_WORD",
      "position" : 3
    },
    {
      "token" : "国人",
      "start_offset" : 3,
      "end_offset" : 5,
      "type" : "CN_WORD",
      "position" : 4
    }
  ]
}

测试狂神说java
发现狂神说被分词器拆分开了，这里我们不想让它拆开，就需要在分词器中添加一个自己的词典，把自己的词放到自己的词典里

6. 创建自定义词典

1. 创建 haima.dic

vim /elasticsearch/es/plugins/ik/config/haima.dic

写入 狂神说

2. 把自定义的haima.dic字典，载入到配置文件里

vim /elasticsearch/es/plugins/ik/config/IKAnalyzer.cfg.xml

3. 重启es服务

root@haima-PC:/usr/local/docker/efk/docker_compose_efk/elasticsearch/es/plugins/ik/config# docker restart docker_compose_efk_elasticsearch_1 
docker_compose_efk_elasticsearch_1

4. 再看一下分词器，狂神说 已经不会被分开了