【Docker+springboot】集成部署ES+Kibana+IK（下）-阿里云开发者社区

3）Mapping

（1）字段类型

（2）映射

Mapping(映射)

Maping是用来定义一个文档（document），以及它所包含的属性（field）是如何存储和索引的。比如：使用maping来定义：

哪些字符串属性应该被看做全文本属性（full text fields）；

哪些属性包含数字，日期或地理位置；

文档中的所有属性是否都嫩被索引（all 配置）；

日期的格式；

自定义映射规则来执行动态添加属性；

查看mapping信息

GET bank/_mapping

  {
    "bank" : {
      "mappings" : {
        "properties" : {
          "account_number" : {
            "type" : "long"
          },
          "address" : {
            "type" : "text",
            "fields" : {
              "keyword" : {
                "type" : "keyword",
                "ignore_above" : 256
              }
            }
          },
          "age" : {
            "type" : "long"
          },
          "balance" : {
            "type" : "long"
          },
          "city" : {
            "type" : "text",
            "fields" : {
              "keyword" : {
                "type" : "keyword",
                "ignore_above" : 256
              }
            }
          },
          "email" : {
            "type" : "text",
            "fields" : {
              "keyword" : {
                "type" : "keyword",
                "ignore_above" : 256
              }
            }
          },
          "employer" : {
            "type" : "text",
            "fields" : {
              "keyword" : {
                "type" : "keyword",
                "ignore_above" : 256
              }
            }
          },
          "firstname" : {
            "type" : "text",
            "fields" : {
              "keyword" : {
                "type" : "keyword",
                "ignore_above" : 256
              }
            }
          },
          "gender" : {
            "type" : "text",
            "fields" : {
              "keyword" : {
                "type" : "keyword",
                "ignore_above" : 256
              }
            }
          },
          "lastname" : {
            "type" : "text",
            "fields" : {
              "keyword" : {
                "type" : "keyword",
                "ignore_above" : 256
              }
            }
          },
          "state" : {
            "type" : "text",
            "fields" : {
              "keyword" : {
                "type" : "keyword",
                "ignore_above" : 256
              }
            }
          }
        }
      }
    }
  }

修改mapping信息

（3）新版本改变

ElasticSearch7-去掉type概念

关系型数据库中两个数据表示是独立的，即使他们里面有相同名称的列也不影响使用，但ES中不是这样的。elasticsearch是基于Lucene开发的搜索引擎，而ES中不同type下名称相同的filed最终在Lucene中的处理方式是一样的。

两个不同type下的两个user_name，在ES同一个索引下其实被认为是同一个filed，你必须在两个不同的type中定义相同的filed映射。否则，不同type中的相同字段名称就会在处理中出现冲突的情况，导致Lucene处理效率下降。

去掉type就是为了提高ES处理数据的效率。

Elasticsearch 7.x URL中的type参数为可选。比如，索引一个文档不再要求提供文档类型。

Elasticsearch 8.x 不再支持URL中的type参数。

解决：

将索引从多类型迁移到单类型，每种类型文档一个独立索引

将已存在的索引下的类型数据，全部迁移到指定位置即可。详见数据迁移

Elasticsearch 7.x

Specifying types in requests is deprecated. For instance, indexing a document no longer requires a document type. The new index APIs are PUT {index}/_doc/{id} in case of explicit ids and POST {index}/_doc for auto-generated ids. Note that in 7.0, _doc is a permanent part of the path, and represents the endpoint name rather than the document type.

The include_type_name parameter in the index creation, index template, and mapping APIs will default to false. Setting the parameter at all will result in a deprecation warning.

The _default_ mapping type is removed.

Elasticsearch 8.x

Specifying types in requests is no longer supported.

The include_type_name parameter is removed.

创建映射

创建索引并指定映射

PUT /my_index
{
  "mappings": {
    "properties": {
      "age": {
        "type": "integer"
      },
      "email": {
        "type": "keyword"
      },
      "name": {
        "type": "text"
      }
    }
  }
}

输出：

{
  "acknowledged" : true,
  "shards_acknowledged" : true,
  "index" : "my_index"
}

查看映射

GET /my_index

输出结果：

{
  "my_index" : {
    "aliases" : { },
    "mappings" : {
      "properties" : {
        "age" : {
          "type" : "integer"
        },
        "email" : {
          "type" : "keyword"
        },
        "employee-id" : {
          "type" : "keyword",
          "index" : false
        },
        "name" : {
          "type" : "text"
        }
      }
    },
    "settings" : {
      "index" : {
        "creation_date" : "1588410780774",
        "number_of_shards" : "1",
        "number_of_replicas" : "1",
        "uuid" : "ua0lXhtkQCOmn7Kh3iUu0w",
        "version" : {
          "created" : "7060299"
        },
        "provided_name" : "my_index"
      }
    }
  }
}

添加新的字段映射

PUT /my_index/_mapping
{
  "properties": {
    "employee-id": {
      "type": "keyword",
      "index": false
    }
  }
}

输出：

{
  "acknowledged" : true,
  "shards_acknowledged" : true,
  "index" : "my_index"
}

查看映射

GET /my_index

输出结果：

{
  "my_index" : {
    "aliases" : { },
    "mappings" : {
      "properties" : {
        "age" : {
          "type" : "integer"
        },
        "email" : {
          "type" : "keyword"
        },
        "employee-id" : {
          "type" : "keyword",
          "index" : false
        },
        "name" : {
          "type" : "text"
        }
      }
    },
    "settings" : {
      "index" : {
        "creation_date" : "1588410780774",
        "number_of_shards" : "1",
        "number_of_replicas" : "1",
        "uuid" : "ua0lXhtkQCOmn7Kh3iUu0w",
        "version" : {
          "created" : "7060299"
        },
        "provided_name" : "my_index"
      }
    }
  }
}

添加新的字段映射

PUT /my_index/_mapping
{
  "properties": {
    "employee-id": {
      "type": "keyword",
      "index": false
    }
  }
}

这里的 “index”: false，表明新增的字段不能被检索，只是一个冗余字段。

更新映射

对于已经存在的字段映射，我们不能更新。更新必须创建新的索引，进行数据迁移。

数据迁移

先创建new_twitter的正确映射。然后使用如下方式进行数据迁移。

POST reindex [固定写法]
{
  "source":{
      "index":"twitter"
   },
  "dest":{
      "index":"new_twitters"
   }
}

将旧索引的type下的数据进行迁移

POST reindex [固定写法]
{
  "source":{
      "index":"twitter",
      "twitter":"twitter"
   },
  "dest":{
      "index":"new_twitters"
   }
}

GET /bank/_search

{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1000,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "bank",
        "_type" : "account",//类型为account
        "_id" : "1",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 1,
          "balance" : 39225,
          "firstname" : "Amber",
          "lastname" : "Duke",
          "age" : 32,
          "gender" : "M",
          "address" : "880 Holmes Lane",
          "employer" : "Pyrami",
          "email" : "amberduke@pyrami.com",
          "city" : "Brogan",
          "state" : "IL"
        }
      },
      ...

GET /bank/_search

想要将年龄修改为integer

PUT /newbank
{
  "mappings": {
    "properties": {
      "account_number": {
        "type": "long"
      },
      "address": {
        "type": "text"
      },
      "age": {
        "type": "integer"
      },
      "balance": {
        "type": "long"
      },
      "city": {
        "type": "keyword"
      },
      "email": {
        "type": "keyword"
      },
      "employer": {
        "type": "keyword"
      },
      "firstname": {
        "type": "text"
      },
      "gender": {
        "type": "keyword"
      },
      "lastname": {
        "type": "text",
        "fields": {
          "keyword": {
            "type": "keyword",
            "ignore_above": 256
          }
        }
      },
      "state": {
        "type": "keyword"
      }
    }
  }
}

查看“newbank”的映射：

GET /newbank/_mapping

能够看到age的映射类型被修改为了integer.

将bank中的数据迁移到newbank中

POST _reindex
{
  "source": {
    "index": "bank",
    "type": "account"
  },
  "dest": {
    "index": "newbank"
  }
}

运行输出：

#! Deprecation: [types removal] Specifying types in reindex requests is deprecated.
{
  "took" : 768,
  "timed_out" : false,
  "total" : 1000,
  "updated" : 0,
  "created" : 1000,
  "deleted" : 0,
  "batches" : 1,
  "version_conflicts" : 0,
  "noops" : 0,
  "retries" : {
    "bulk" : 0,
    "search" : 0
  },
  "throttled_millis" : 0,
  "requests_per_second" : -1.0,
  "throttled_until_millis" : 0,
  "failures" : [ ]
}

查看newbank中的数据

4）分词

一个tokenizer（分词器）接收一个字符流，将之分割为独立的tokens（词元，通常是独立的单词），然后输出tokens流。

例如：whitespace tokenizer遇到空白字符时分割文本。它会将文本“Quick brown fox!”分割为[Quick,brown,fox!]。

该tokenizer（分词器）还负责记录各个terms(词条)的顺序或position位置（用于phrase短语和word proximity词近邻查询），以及term（词条）所代表的原始word（单词）的start（起始）和end（结束）的character offsets（字符串偏移量）（用于高亮显示搜索的内容）。

elasticsearch提供了很多内置的分词器，可以用来构建custom analyzers（自定义分词器）。

关于分词器： https://www.elastic.co/guide/en/elasticsearch/reference/7.6/analysis.html

POST _analyze
{
  "analyzer": "standard",
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}

执行结果：

{
  "tokens" : [
    {
      "token" : "the",
      "start_offset" : 0,
      "end_offset" : 3,
      "type" : "<ALPHANUM>",
      "position" : 0
    },
    {
      "token" : "2",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "<NUM>",
      "position" : 1
    },
    {
      "token" : "quick",
      "start_offset" : 6,
      "end_offset" : 11,
      "type" : "<ALPHANUM>",
      "position" : 2
    },
    {
      "token" : "brown",
      "start_offset" : 12,
      "end_offset" : 17,
      "type" : "<ALPHANUM>",
      "position" : 3
    },
    {
      "token" : "foxes",
      "start_offset" : 18,
      "end_offset" : 23,
      "type" : "<ALPHANUM>",
      "position" : 4
    },
    {
      "token" : "jumped",
      "start_offset" : 24,
      "end_offset" : 30,
      "type" : "<ALPHANUM>",
      "position" : 5
    },
    {
      "token" : "over",
      "start_offset" : 31,
      "end_offset" : 35,
      "type" : "<ALPHANUM>",
      "position" : 6
    },
    {
      "token" : "the",
      "start_offset" : 36,
      "end_offset" : 39,
      "type" : "<ALPHANUM>",
      "position" : 7
    },
    {
      "token" : "lazy",
      "start_offset" : 40,
      "end_offset" : 44,
      "type" : "<ALPHANUM>",
      "position" : 8
    },
    {
      "token" : "dog's",
      "start_offset" : 45,
      "end_offset" : 50,
      "type" : "<ALPHANUM>",
      "position" : 9
    },
    {
      "token" : "bone",
      "start_offset" : 51,
      "end_offset" : 55,
      "type" : "<ALPHANUM>",
      "position" : 10
    }
  ]
}

（1）安装ik分词器

所有的语言分词，默认使用的都是“Standard Analyzer”，但是这些分词器针对于中文的分词，并不友好。为此需要安装中文的分词器。

注意：不能用默认elasticsearch-plugin install xxx.zip 进行自动安装

https://github.com/medcl/elasticsearch-analysis-ik/releases/download 对应es版本安装

在前面安装的elasticsearch时，我们已经将elasticsearch容器的“/usr/share/elasticsearch/plugins”目录，映射到宿主机的“ /mydata/elasticsearch/plugins”目录下，所以比较方便的做法就是下载“/elasticsearch-analysis-ik-7.6.2.zip”文件，然后解压到该文件夹下即可。安装完毕后，需要重启elasticsearch容器。

如果不嫌麻烦，还可以采用如下的方式。

（1）查看elasticsearch版本号：

[root@hadoop-104 ~]# curl http://localhost:9200
{
  "name" : "0adeb7852e00",
  "cluster_name" : "elasticsearch",
  "cluster_uuid" : "9gglpP0HTfyOTRAaSe2rIg",
  "version" : {
    "number" : "7.6.2",      #版本号为7.6.2
    "build_flavor" : "default",
    "build_type" : "docker",
    "build_hash" : "ef48eb35cf30adf4db14086e8aabd07ef6fb113f",
    "build_date" : "2020-03-26T06:34:37.794943Z",
    "build_snapshot" : false,
    "lucene_version" : "8.4.0",
    "minimum_wire_compatibility_version" : "6.8.0",
    "minimum_index_compatibility_version" : "6.0.0-beta1"
  },
  "tagline" : "You Know, for Search"
}
[root@hadoop-104 ~]#

（2）进入es容器内部plugin目录

docker exec -it 容器id /bin/bash

[root@hadoop-104 ~]# docker exec -it elasticsearch /bin/bash
[root@0adeb7852e00 elasticsearch]#

wget https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v7.6.2/elasticsearch-analysis-ik-7.6.2.zip

[root@0adeb7852e00 elasticsearch]# pwd
/usr/share/elasticsearch
#下载ik7.6.2
[root@0adeb7852e00 elasticsearch]# wget https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v7.6.2/elasticsearch-analysis-ik-7.6.2.zip

unzip 下载的文件

[root@0adeb7852e00 elasticsearch]# unzip elasticsearch-analysis-ik-7.6.2.zip -d ink
Archive:  elasticsearch-analysis-ik-7.6.2.zip
   creating: ik/config/
  inflating: ik/config/main.dic      
  inflating: ik/config/quantifier.dic  
  inflating: ik/config/extra_single_word_full.dic  
  inflating: ik/config/IKAnalyzer.cfg.xml  
  inflating: ik/config/surname.dic   
  inflating: ik/config/suffix.dic    
  inflating: ik/config/stopword.dic  
  inflating: ik/config/extra_main.dic  
  inflating: ik/config/extra_stopword.dic  
  inflating: ik/config/preposition.dic  
  inflating: ik/config/extra_single_word_low_freq.dic  
  inflating: ik/config/extra_single_word.dic  
  inflating: ik/elasticsearch-analysis-ik-7.6.2.jar  
  inflating: ik/httpclient-4.5.2.jar  
  inflating: ik/httpcore-4.4.4.jar   
  inflating: ik/commons-logging-1.2.jar  
  inflating: ik/commons-codec-1.9.jar  
  inflating: ik/plugin-descriptor.properties  
  inflating: ik/plugin-security.policy  
[root@0adeb7852e00 elasticsearch]#
#移动到plugins目录下
[root@0adeb7852e00 elasticsearch]# mv ik plugins/

rm -rf *.zip

[root@0adeb7852e00 elasticsearch]# rm -rf elasticsearch-analysis-ik-7.6.2.zip

确认是否安装好了分词器

（2）测试分词器

使用默认

GET my_index/_analyze
{
   "text":"我是中国人"
}

请观察执行结果：

{
  "tokens" : [
    {
      "token" : "我",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "<IDEOGRAPHIC>",
      "position" : 0
    },
    {
      "token" : "是",
      "start_offset" : 1,
      "end_offset" : 2,
      "type" : "<IDEOGRAPHIC>",
      "position" : 1
    },
    {
      "token" : "中",
      "start_offset" : 2,
      "end_offset" : 3,
      "type" : "<IDEOGRAPHIC>",
      "position" : 2
    },
    {
      "token" : "国",
      "start_offset" : 3,
      "end_offset" : 4,
      "type" : "<IDEOGRAPHIC>",
      "position" : 3
    },
    {
      "token" : "人",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "<IDEOGRAPHIC>",
      "position" : 4
    }
  ]
}

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

输出结果：

{
  "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
    }
  ]
}

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

输出结果：

{
  "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
    }
  ]
}

（3）自定义词库

修改/usr/share/elasticsearch/plugins/ik/config中的IKAnalyzer.cfg.xml
/usr/share/elasticsearch/plugins/ik/config

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE properties SYSTEM "http://java.sun.com/dtd/properties.dtd">
<properties>
  <comment>IK Analyzer 扩展配置</comment>
  <!--用户可以在这里配置自己的扩展字典 -->
  <entry key="ext_dict"></entry>
   <!--用户可以在这里配置自己的扩展停止词字典-->
  <entry key="ext_stopwords"></entry>
  <!--用户可以在这里配置远程扩展字典 -->
  <entry key="remote_ext_dict">http://192.168.137.14/es/fenci.txt</entry> 
  <!--用户可以在这里配置远程扩展停止词字典-->
  <!-- <entry key="remote_ext_stopwords">words_location</entry> -->
</properties>

原来的xml

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE properties SYSTEM "http://java.sun.com/dtd/properties.dtd">
<properties>
  <comment>IK Analyzer 扩展配置</comment>
  <!--用户可以在这里配置自己的扩展字典 -->
  <entry key="ext_dict"></entry>
   <!--用户可以在这里配置自己的扩展停止词字典-->
  <entry key="ext_stopwords"></entry>
  <!--用户可以在这里配置远程扩展字典 -->
  <!-- <entry key="remote_ext_dict">words_location</entry> -->
  <!--用户可以在这里配置远程扩展停止词字典-->
  <!-- <entry key="remote_ext_stopwords">words_location</entry> -->
</properties>

修改完成后，需要重启elasticsearch容器，否则修改不生效。

更新完成后，es只会对于新增的数据用更新分词。历史数据是不会重新分词的。如果想要历史数据重新分词，需要执行：

POST my_index/_update_by_query?conflicts=proceed

http://192.168.137.14/es/fenci.txt，这个是nginx上资源的访问路径

在运行下面实例之前，需要安装nginx（安装方法见安装nginx），然后创建“fenci.txt”文件，内容如下：

echo "樱桃萨其马，带你甜蜜入夏" > /mydata/nginx/html/fenci.txt

测试效果：

GET my_index/_analyze
{
   "analyzer": "ik_max_word", 
   "text":"樱桃萨其马，带你甜蜜入夏"
}

输出结果：

{
  "tokens" : [
    {
      "token" : "樱桃",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "CN_WORD",
      "position" : 0
    },
    {
      "token" : "萨其马",
      "start_offset" : 2,
      "end_offset" : 5,
      "type" : "CN_WORD",
      "position" : 1
    },
    {
      "token" : "带你",
      "start_offset" : 6,
      "end_offset" : 8,
      "type" : "CN_WORD",
      "position" : 2
    },
    {
      "token" : "甜蜜",
      "start_offset" : 8,
      "end_offset" : 10,
      "type" : "CN_WORD",
      "position" : 3
    },
    {
      "token" : "入夏",
      "start_offset" : 10,
      "end_offset" : 12,
      "type" : "CN_WORD",
      "position" : 4
    }
  ]
}

4、elasticsearch-Rest-Client

1）9300: TCP

spring-data-elasticsearch:transport-api.jar;

springboot版本不同，ransport-api.jar不同，不能适配es版本

7.x已经不建议使用，8以后就要废弃

2）9200: HTTP

jestClient: 非官方，更新慢；

RestTemplate：模拟HTTP请求，ES很多操作需要自己封装，麻烦；

HttpClient：同上；

Elasticsearch-Rest-Client：官方RestClient，封装了ES操作，API层次分明，上手简单；

最终选择Elasticsearch-Rest-Client（elasticsearch-rest-high-level-client）；

https://www.elastic.co/guide/en/elasticsearch/client/java-rest/current/java-rest-high.html

5、附录：安装Nginx

随便启动一个nginx实例，只是为了复制出配置

docker run -p80:80 --name nginx -d nginx:1.10

将容器内的配置文件拷贝到/mydata/nginx/conf/ 下

mkdir -p /mydata/nginx/html
mkdir -p /mydata/nginx/logs
mkdir -p /mydata/nginx/conf
docker container cp nginx:/etc/nginx/*  /mydata/nginx/conf/ 
#由于拷贝完成后会在config中存在一个nginx文件夹，所以需要将它的内容移动到conf中
mv /mydata/nginx/conf/nginx/* /mydata/nginx/conf/
rm -rf /mydata/nginx/conf/nginx

终止原容器：

docker stop nginx

执行命令删除原容器：

docker rm nginx

创建新的Nginx，执行以下命令

docker run -p 80:80 --name nginx \
 -v /mydata/nginx/html:/usr/share/nginx/html \
 -v /mydata/nginx/logs:/var/log/nginx \
 -v /mydata/nginx/conf/:/etc/nginx \
 -d nginx:1.10

设置开机启动ngin

docker update nginx --restart=always

创建“/mydata/nginx/html/index.html”文件，测试是否能够正常访问

echo '<h2>hello nginx!</h2>' >index.html

访问：http://ngix所在主机的IP:80/index.html

SpringBoot整合ElasticSearch

1、导入依赖

这里的版本要和所按照的ELK版本匹配。

<dependency>
    <groupId>org.elasticsearch.client</groupId>
    <artifactId>elasticsearch-rest-high-level-client</artifactId>
    <version>7.6.2</version>
</dependency>

在spring-boot-dependencies中所依赖的ELK版本位6.8.7

<elasticsearch.version>6.8.7</elasticsearch.version>

需要在项目中将它改为7.6.2

<properties>
        ...
        <elasticsearch.version>7.6.2</elasticsearch.version>
    </properties>

2、编写测试类

1）测试保存数据

https://www.elastic.co/guide/en/elasticsearch/client/java-rest/current/java-rest-high-document-index.html

    @Test
    public void indexData() throws IOException {
        IndexRequest indexRequest = new IndexRequest ("users");
        User user = new User();
        user.setUserName("张三");
        user.setAge(20);
        user.setGender("男");
        String jsonString = JSON.toJSONString(user);
        //设置要保存的内容
        indexRequest.source(jsonString, XContentType.JSON);
        //执行创建索引和保存数据
        IndexResponse index = client.index(indexRequest, GulimallElasticSearchConfig.COMMON_OPTIONS);
        System.out.println(index);
    }

测试前：

测试后：

2）测试获取数据

https://www.elastic.co/guide/en/elasticsearch/client/java-rest/current/java-rest-high-search.html

    @Test
    public void searchData() throws IOException {
        GetRequest getRequest = new GetRequest(
                "users",
                "_-2vAHIB0nzmLJLkxKWk");
        GetResponse getResponse = client.get(getRequest, RequestOptions.DEFAULT);
        System.out.println(getResponse);
        String index = getResponse.getIndex();
        System.out.println(index);
        String id = getResponse.getId();
        System.out.println(id);
        if (getResponse.isExists()) {
            long version = getResponse.getVersion();
            System.out.println(version);
            String sourceAsString = getResponse.getSourceAsString();
            System.out.println(sourceAsString);
            Map<String, Object> sourceAsMap = getResponse.getSourceAsMap();
            System.out.println(sourceAsMap);
            byte[] sourceAsBytes = getResponse.getSourceAsBytes();
        } else {
        }
    }

查询state="AK"的文档：

{
  "took": 1,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 22,   //匹配到了22条
      "relation": "eq"
    },
    "max_score": 3.7952394,
    "hits": [{
      "_index": "bank",
      "_type": "account",
      "_id": "210",
      "_score": 3.7952394,
      "_source": {
        "account_number": 210,
        "balance": 33946,
        "firstname": "Cherry",
        "lastname": "Carey",
        "age": 24,
        "gender": "M",
        "address": "539 Tiffany Place",
        "employer": "Martgo",
        "email": "cherrycarey@martgo.com",
        "city": "Fairacres",
        "state": "AK"
      }
    },
           ....//省略其他
          ]
  }
}

搜索address中包含mill的所有人的年龄分布以及平均年龄，平均薪资

GET bank/_search
{
  "query": {
    "match": {
      "address": "Mill"
    }
  },
  "aggs": {
    "ageAgg": {
      "terms": {
        "field": "age",
        "size": 10
      }
    },
    "ageAvg": {
      "avg": {
        "field": "age"
      }
    },
    "balanceAvg": {
      "avg": {
        "field": "balance"
      }
    }
  }
}

java实现

    /**
     * 复杂检索:在bank中搜索address中包含mill的所有人的年龄分布以及平均年龄，平均薪资
     * @throws IOException
     */
    @Test
    public void searchData() throws IOException {
        //1. 创建检索请求
        SearchRequest searchRequest = new SearchRequest();
        //1.1）指定索引
        searchRequest.indices("bank");
        //1.2）构造检索条件
        SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
        sourceBuilder.query(QueryBuilders.matchQuery("address","Mill"));
        //1.2.1)按照年龄分布进行聚合
        TermsAggregationBuilder ageAgg=AggregationBuilders.terms("ageAgg").field("age").size(10);
        sourceBuilder.aggregation(ageAgg);
        //1.2.2)计算平均年龄
        AvgAggregationBuilder ageAvg = AggregationBuilders.avg("ageAvg").field("age");
        sourceBuilder.aggregation(ageAvg);
        //1.2.3)计算平均薪资
        AvgAggregationBuilder balanceAvg = AggregationBuilders.avg("balanceAvg").field("balance");
        sourceBuilder.aggregation(balanceAvg);
        System.out.println("检索条件："+sourceBuilder);
        searchRequest.source(sourceBuilder);
        //2. 执行检索
        SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
        System.out.println("检索结果："+searchResponse);
        //3. 将检索结果封装为Bean
        SearchHits hits = searchResponse.getHits();
        SearchHit[] searchHits = hits.getHits();
        for (SearchHit searchHit : searchHits) {
            String sourceAsString = searchHit.getSourceAsString();
            Account account = JSON.parseObject(sourceAsString, Account.class);
            System.out.println(account);
        }
        //4. 获取聚合信息
        Aggregations aggregations = searchResponse.getAggregations();
        Terms ageAgg1 = aggregations.get("ageAgg");
        for (Terms.Bucket bucket : ageAgg1.getBuckets()) {
            String keyAsString = bucket.getKeyAsString();
            System.out.println("年龄："+keyAsString+" ==> "+bucket.getDocCount());
        }
        Avg ageAvg1 = aggregations.get("ageAvg");
        System.out.println("平均年龄："+ageAvg1.getValue());
        Avg balanceAvg1 = aggregations.get("balanceAvg");
        System.out.println("平均薪资："+balanceAvg1.getValue());
    }

可以尝试对比打印的条件和执行结果，和前面的ElasticSearch的检索语句和检索结果进行比较；

其他

1. kibana控制台命令

ctrl+home：回到文档首部；

ctril+end：回到文档尾部。

【Docker+springboot】集成部署ES+Kibana+IK（下）

3）Mapping

（1）字段类型

（2）映射

（3）新版本改变

查看映射

添加新的字段映射

查看映射

添加新的字段映射

更新映射

数据迁移

4）分词

（1）安装ik分词器

（2）进入es容器内部plugin目录

（2）测试分词器

（3）自定义词库

4、elasticsearch-Rest-Client

1）9300: TCP

2）9200: HTTP

5、附录：安装Nginx

SpringBoot整合ElasticSearch

1、导入依赖

2、编写测试类

1）测试保存数据

2）测试获取数据

其他

1. kibana控制台命令

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技术资料

【Docker+springboot】集成部署ES+Kibana+IK（下）

3）Mapping

（1）字段类型

（2）映射

（3）新版本改变

查看映射

添加新的字段映射

查看映射

添加新的字段映射

更新映射

数据迁移

4）分词

（1）安装ik分词器

（2）进入es容器内部plugin目录

（2）测试分词器

（3）自定义词库

4、elasticsearch-Rest-Client

1）9300: TCP

2）9200: HTTP

5、附录：安装Nginx

SpringBoot整合ElasticSearch

1、导入依赖

2、编写测试类

1）测试保存数据

2）测试获取数据

其他

1. kibana控制台命令

热门文章

最新文章

相关课程

相关电子书

推荐镜像