vtnews 2020-07-29
ES提供的分词是英文分词,对中文做分词时会拆成单字而不是词语,非常不好,因此索引信息含中文时需要使用中文分词器插件。
docker pull elasticsearch:7.8.0# 在Linux根目录创建docker文件夹并进入文件夹 mkdir /docker cd /docker # 下载IK插件文件(如果提示没有wget命令则先执行:`yum install -y wget`,再执行下载命令) wget https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v7.8.0/elasticsearch-analysis-ik-7.8.0.zip # 可选项:wget下载过慢可先用浏览器将文件下载到本地再上传到Linux(如果提示没有rz命令则先执行:`yum install -y lrzsz`,再执行上传命令,选择elasticsearch-analysis-ik-7.8.0.zip文件) rz # 解压(如果提示没有unzip命令则先执行:`yum install -y unzip`,再执行下载命令) unzip elasticsearch-analysis-ik-7.8.0.zip -d elasticsearch-analysis-ik
注意:ElasticSearch镜像版本要与IK分词器一致(我使用elasticsearch:7.8.1镜像与elasticsearch-analysis-ik-7.8.0插件,构建镜像后无法使用)
vi DockerFileFROM elasticsearch:7.8.0 ADD elasticsearch-analysis-ik /usr/share/elasticsearch/plugins/elasticsearch-analysis-ik
docker build -f DockerFile -t elasticsearch-ik:7.8.0 .镜像构建成功:
[ elasticsearch-ik]# docker build -f DockerFile -t elasticsearch-ik:7.8.0 . Sending build context to Docker daemon 14.39MB Step 1/2 : FROM elasticsearch:7.8.0 ---> 121454ddad72 Step 2/2 : ADD elasticsearch-analysis-ik /usr/share/elasticsearch/plugins/elasticsearch-analysis-ik ---> Using cache ---> 2af03d5426d3 Successfully built 2af03d5426d3 Successfully tagged elasticsearch-ik:7.8.0
docker run -d -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node" --name elasticsearch_test elasticsearch-ik:7.8.0
curl localhost:9200显示如下即启动成功:
[ docker]# curl localhost:9200
{
"name" : "9f832bbeb44a",
"cluster_name" : "docker-cluster",
"cluster_uuid" : "8GAjHyQEToO6PMl8dDoemQ",
"version" : {
"number" : "7.8.0",
"build_flavor" : "default",
"build_type" : "docker",
"build_hash" : "757314695644ea9a1dc2fecd26d1a43856725e65",
"build_date" : "2020-06-14T19:35:50.234439Z",
"build_snapshot" : false,
"lucene_version" : "8.5.1",
"minimum_wire_compatibility_version" : "6.8.0",
"minimum_index_compatibility_version" : "6.0.0-beta1"
},
"tagline" : "You Know, for Search"
}这里使用的是postman
请求url:http://192.168.0.199:9200/_analyze
请求方式:post
在请求体body中请求入参格式:
{
"analyzer": "chinese",
"text": "今天是个好日子"
}参数说明:
analyzer:可填项有:chinese|ik_max_word|ik_smart,其中chinese是ES的默认分词器选项,ik_max_word(最细粒度划分)和ik_smart(最少划分)是ik中文分词器选项
text:要进行分词操作的内容
{
"analyzer": "chinese",
"text": "今天是个好日子"
}结果:
{
"tokens": [
{
"token": "今",
"start_offset": 0,
"end_offset": 1,
"type": "<IDEOGRAPHIC>",
"position": 0
},
{
"token": "天",
"start_offset": 1,
"end_offset": 2,
"type": "<IDEOGRAPHIC>",
"position": 1
},
{
"token": "是",
"start_offset": 2,
"end_offset": 3,
"type": "<IDEOGRAPHIC>",
"position": 2
},
{
"token": "个",
"start_offset": 3,
"end_offset": 4,
"type": "<IDEOGRAPHIC>",
"position": 3
},
{
"token": "好",
"start_offset": 4,
"end_offset": 5,
"type": "<IDEOGRAPHIC>",
"position": 4
},
{
"token": "日",
"start_offset": 5,
"end_offset": 6,
"type": "<IDEOGRAPHIC>",
"position": 5
},
{
"token": "子",
"start_offset": 6,
"end_offset": 7,
"type": "<IDEOGRAPHIC>",
"position": 6
}
]
}{
"analyzer": "ik_smart",
"text": "今天是个好日子"
}结果:
{
"tokens": [
{
"token": "今天是",
"start_offset": 0,
"end_offset": 3,
"type": "CN_WORD",
"position": 0
},
{
"token": "个",
"start_offset": 3,
"end_offset": 4,
"type": "CN_CHAR",
"position": 1
},
{
"token": "好日子",
"start_offset": 4,
"end_offset": 7,
"type": "CN_WORD",
"position": 2
}
]
}{
"analyzer": "ik_max_word",
"text": "今天是个好日子"
}结果:
{
"tokens": [
{
"token": "今天是",
"start_offset": 0,
"end_offset": 3,
"type": "CN_WORD",
"position": 0
},
{
"token": "今天",
"start_offset": 0,
"end_offset": 2,
"type": "CN_WORD",
"position": 1
},
{
"token": "是",
"start_offset": 2,
"end_offset": 3,
"type": "CN_CHAR",
"position": 2
},
{
"token": "个",
"start_offset": 3,
"end_offset": 4,
"type": "CN_CHAR",
"position": 3
},
{
"token": "好日子",
"start_offset": 4,
"end_offset": 7,
"type": "CN_WORD",
"position": 4
},
{
"token": "日子",
"start_offset": 5,
"end_offset": 7,
"type": "CN_WORD",
"position": 5
}
]
} 另外一部分,则需要先做聚类、分类处理,将聚合出的分类结果存入ES集群的聚类索引中。数据处理层的聚合结果存入ES中的指定索引,同时将每个聚合主题相关的数据存入每个document下面的某个field下。