ELK高级搜索（三）

11．索引Index入门

11.1 索引管理

直接put数据 PUT index/_doc/1，es会自动生成索引，并建立动态映射dynamic mapping。

在生产上，需要自己手动建立索引和映射，为了更好地管理索引。就像数据库的建表语句一样。

11.1.1 创建索引

创建索引的语法

PUT /index
{
    "settings": { ... any settings ... },
    "mappings": {
       "properties" : {
            "field1" : { "type" : "text" }
        }
    },
    "aliases": {
    	"default_index": {}
  } 
}

举例：

PUT /my_index
{
  "settings": {
    "number_of_shards": 1,
    "number_of_replicas": 1
  },
  "mappings": {
    "properties": {
      "field1":{
        "type": "text"
      },
      "field2":{
        "type": "text"
      }
    }
  },
  "aliases": {
    "default_index": {}
  } 
}

索引别名

插入数据

POST /my_index/_doc/1
{
	"field1":"java",
	"field2":"js"
}

查询数据 都可以查到

GET /my_index/_doc/1

GET /default_index/_doc/1

11.1.2 查询索引

GET /my_index/_mapping

GET /my_index/_setting

11.1.3 修改索引

修改副本数

PUT /my_index/_settings
{
    "index" : {
        "number_of_replicas" : 2
    }
}

11.1.4 删除索引

DELETE /my_index

DELETE /index_one,index_two

DELETE /index_*

DELETE /_all

为了安全起见，防止恶意删除索引，删除时必须指定索引名：

elasticsearch.yml

action.destructive_requires_name: true

11.2 定制分词器

11.2.1 默认的分词器

standard

分词三个组件，character filter，tokenizer，token filter

standard tokenizer：以单词边界进行切分

standard token filter：什么都不做

lowercase token filter：将所有字母转换为小写

stop token filer（默认被禁用）：移除停用词，比如a the it等等

11.2.2 修改分词器的设置

启用english停用词token filter

PUT /my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "es_std": {
          "type": "standard",
          "stopwords": "_english_"
        }
      }
    }
  }
}

测试分词

GET /my_index/_analyze
{
  "analyzer": "standard", 
  "text": "a dog is in the house"
}

GET /my_index/_analyze
{
  "analyzer": "es_std",
  "text":"a dog is in the house"
}

11.2.3 定制化自己的分词器

PUT /my_index
{
  "settings": {
    "analysis": {
      "char_filter": {
        "&_to_and": {
          "type": "mapping",
          "mappings": ["&=> and"]
        }
      },
      "filter": {
        "my_stopwords": {
          "type": "stop",
          "stopwords": ["the", "a"]
        }
      },
      "analyzer": {
        "my_analyzer": {
          "type": "custom",
          "char_filter": ["html_strip", "&_to_and"],
          "tokenizer": "standard",
          "filter": ["lowercase", "my_stopwords"]
        }
      }
    }
  }
}

测试

GET /my_index/_analyze
{
  "analyzer": "my_analyzer",
  "text": "tom&jerry are a friend in the house, , HAHA!!"
}

设置字段使用自定义分词器

PUT /my_index/_mapping/
{
  "properties": {
    "content": {
      "type": "text",
      "analyzer": "my_analyzer"
    }
  }
}

11.3 type底层结构

11.3.1 type是什么

type，是一个index中用来区分类似的数据的，类似的数据，但是可能有不同的fields，而且有不同的属性来控制索引建立、分词器。
field的value，在底层的lucene中建立索引的时候，全部是opaque bytes类型，不区分类型的。
lucene是没有type的概念的，在document中，实际上将type作为一个document的field来存储，即_type，es通过_type来进行type的过滤和筛选。

11.3.2 es中不同type存储机制

一个index中的多个type，实际上是放在一起存储的，因此一个index下，不能有多个type重名，而类型或者其他设置不同的，因为那样是无法处理的

{
   "goods": {
      "mappings": {
         "electronic_goods": {
            "properties": {
               "name": {
                  "type": "string",
               },
               "price": {
                  "type": "double"
               },
               "service_period": {
                  "type": "string"
                   }			
                }
         },
         "fresh_goods": {
            "properties": {
               "name": {
                  "type": "string",
               },
               "price": {
                  "type": "double"
               },
               "eat_period": {
              		"type": "string"
               }
                }
         }
      }
   }
}

PUT /goods/electronic_goods/1
{
  "name": "小米空调",
  "price": 1999.0,
  "service_period": "one year"
}

PUT /goods/fresh_goods/1
{
  "name": "澳洲龙虾",
  "price": 199.0,
  "eat_period": "one week"
}

es文档在底层的存储

{
   "goods": {
      "mappings": {
        "_type": {
          "type": "text",
          "index": "false"
        },
        "name": {
          "type": "text"
        }
        "price": {
          "type": "double"
        }
        "service_period": {
          "type": "text"
        },
        "eat_period": {
          "type": "text"
        }
      }
   }
}

底层数据存储格式

{
  "_type": "electronic_goods",
  "name": "小米空调",
  "price": 1999.0,
  "service_period": "one year",
  "eat_period": ""
}

{
  "_type": "fresh_goods",
  "name": "澳洲龙虾",
  "price": 199.0,
  "service_period": "",
  "eat_period": "one week"
}

11.3.3 type弃用

同一索引下，不同type的数据存储其他type的field 大量空值，造成资源浪费。

所以，不同类型数据，要放到不同的索引中。

es9中，将会彻底删除type。

11.4 定制dynamic mapping

11.4.1 定制dynamic策略

• true：遇到陌生字段，就进行dynamic mapping

• false：新检测到的字段将被忽略。这些字段将不会被索引，因此将无法搜索，但仍将出现在返回点击的源字段中。这些字段不会添加到映射中，必须显式添加新字段

• strict：遇到陌生字段，就报错

创建mapping

PUT /my_index
{
    "mappings": {
      "dynamic": "strict",
       "properties": {
        "title": {
          "type": "text"
        },
        "address": {
          "type": "object",
          "dynamic": "true"
        }
	    }
    }
}

插入数据

PUT /my_index/_doc/1
{
  "title": "my article",
  "content": "this is my article",
  "address": {
    "province": "guangdong",
    "city": "guangzhou"
  }
}

报错

{
  "error": {
    "root_cause": [
      {
        "type": "strict_dynamic_mapping_exception",
        "reason": "mapping set to strict, dynamic introduction of [content] within [_doc] is not allowed"
      }
    ],
    "type": "strict_dynamic_mapping_exception",
    "reason": "mapping set to strict, dynamic introduction of [content] within [_doc] is not allowed"
  },
  "status": 400
}

11.4.2 自定义dynamic mapping策略

es会根据传入的值，推断类型。

date_detection 日期探测

默认会按照一定格式识别date，比如yyyy-MM-dd。但是如果某个field先过来一个2017-01-01的值，就会被自动dynamic mapping成date，后面如果再来一个"hello world"之类的值，就会报错。可以手动关闭某个type的date_detection，如果有需要，自己手动指定某个field为date类型。

PUT /my_index
{
    "mappings": {
      "date_detection": false,
       "properties": {
        "title": {
          "type": "text"
        },
        "address": {
          "type": "object",
          "dynamic": "true"
        }
	    }
    }
}

测试

PUT /my_index/_doc/1
{
  "title": "my article",
  "content": "this is my article",
  "address": {
    "province": "guangdong",
    "city": "guangzhou"
  },
  "post_date":"2019-09-10"
}

查看映射

GET /my_index/_mapping

自定义日期格式

PUT my_index
{
  "mappings": {
    "dynamic_date_formats": ["MM/dd/yyyy"]
  }
}

插入数据

PUT my_index/_doc/1
{
  "create_date": "09/25/2019"
}

numeric_detection 数字探测

虽然json支持本机浮点和整数数据类型，但某些应用程序或语言有时可能将数字呈现为字符串。通常正确的解决方案是显式地映射这些字段，但是可以启用数字检测（默认情况下禁用）来自动完成这些操作。

PUT my_index
{
  "mappings": {
    "numeric_detection": true
  }
}

PUT my_index/_doc/1
{
  "my_float":   "1.0", 
  "my_integer": "1" 
}

11.4.3 定制自己的dynamic mapping template

PUT /my_index
{
    "mappings": {
            "dynamic_templates": [
                { 
                  "en": {
                      "match":              "*_en", 
                      "match_mapping_type": "string",
                      "mapping": {
                          "type":           "text",
                          "analyzer":       "english"
                      }
                }                  
            }
        ]
	}
}

插入数据

PUT /my_index/_doc/1
{
  "title": "this is my first article"
}

PUT /my_index/_doc/2
{
  "title_en": "this is my first article"
}

搜索

GET my_index/_search?q=first
GET my_index/_search?q=is

title没有匹配到任何的dynamic模板，默认就是standard分词器，不会过滤停用词，is会进入倒排索引，用is来搜索是可以搜索到的

title_en匹配到了dynamic模板，就是english分词器，会过滤停用词，is这种停用词就会被过滤掉，用is来搜索就搜索不到了

模板写法

PUT my_index
{
  "mappings": {
    "dynamic_templates": [
      {
        "integers": {
          "match_mapping_type": "long",
          "mapping": {
            "type": "integer"
          }
        }
      },
      {
        "strings": {
          "match_mapping_type": "string",
          "mapping": {
            "type": "text",
            "fields": {
              "raw": {
                "type":  "keyword",
                "ignore_above": 256
              }
            }
          }
        }
      }
    ]
  }
}

模板参数

"match":   "long_*",
"unmatch": "*_text",
"match_mapping_type": "string",
"path_match":   "name.*",
"path_unmatch": "*.middle",

"match_pattern": "regex",
"match": "^profit_\d+$"

场景

1 结构化搜索

默认情况下，elasticsearch将字符串字段映射为带有子关键字字段的文本字段。但是，如果只对结构化内容进行索引，而对全文搜索不感兴趣，则可以仅将“字段”映射为“关键字”。请注意，这意味着为了搜索这些字段，必须搜索索引所用的完全相同的值。

	{
        "strings_as_keywords": {
          "match_mapping_type": "string",
          "mapping": {
            "type": "keyword"
          }
        }
      }

2 仅搜索

与前面的示例相反，如果您只关心字符串字段的全文搜索，并且不打算对字符串字段运行聚合、排序或精确搜索，您可以告诉弹性搜索将其仅映射为文本字段（这是5之前的默认行为）

	 {
        "strings_as_text": {
          "match_mapping_type": "string",
          "mapping": {
            "type": "text"
          }
        }
      }

3 norms 不关心评分

norms是指标时间的评分因素。如果您不关心评分，例如，如果您从不按评分对文档进行排序，则可以在索引中禁用这些评分因子的存储并节省一些空间。

{
        "strings_as_keywords": {
          "match_mapping_type": "string",
          "mapping": {
            "type": "text",
            "norms": false,
            "fields": {
              "keyword": {
                "type": "keyword",
                "ignore_above": 256
              }
            }
          }
        }
      }

11.5 零停机重建索引

11.5.1 零停机重建索引

场景：

一个field的设置是不能被修改的，如果要修改一个Field，应该重新按照新的mapping建立一个index，然后将数据批量查询出来，重新用bulk api写入index中。

批量查询的时候，建议采用scroll api，并且采用多线程并发的方式来reindex数据，每次scoll就查询指定日期的一段数据，交给一个线程即可。

（1）一开始，依靠dynamic mapping，插入数据，但是不小心有些数据是2019-09-10这种日期格式的，所以title这种field被自动映射为了date类型，实际上它应该是string类型的。

PUT /my_index/_doc/1
{
  "title": "2019-09-10"
}

PUT /my_index/_doc/2
{
  "title": "2019-09-11"
}

（2）当后期向索引中加入string类型的title值的时候，就会报错。

PUT /my_index/_doc/3
{
  "title": "my first article"
}

报错

{
  "error": {
    "root_cause": [
      {
        "type": "mapper_parsing_exception",
        "reason": "failed to parse [title]"
      }
    ],
    "type": "mapper_parsing_exception",
    "reason": "failed to parse [title]",
    "caused_by": {
      "type": "illegal_argument_exception",
      "reason": "Invalid format: \"my first article\""
    }
  },
  "status": 400
}

（3）如果此时想修改title的类型，是不可能的。

PUT /my_index/_mapping
{
  "properties": {
    "title": {
      "type": "text"
   	}
  }
}

报错

{
  "error": {
    "root_cause": [
      {
        "type": "illegal_argument_exception",
        "reason": "mapper [title] of different type, current_type [date], merged_type [text]"
      }
    ],
    "type": "illegal_argument_exception",
    "reason": "mapper [title] of different type, current_type [date], merged_type [text]"
  },
  "status": 400
}

（4）此时，唯一的办法，就是进行reindex，也就是说，重新建立一个索引，将旧索引的数据查询出来，再导入新索引。

（5）如果说旧索引的名字，是old_index，新索引的名字是new_index，终端java应用，已经在使用old_index在操作了，难道还要去停止java应用，修改使用的index为new_index，才重新启动java应用吗？这个过程中，就会导致java应用停机，可用性降低。

（6）所以说，给java应用一个别名，这个别名是指向旧索引的，java应用先用着，java应用先用prod_index alias来操作，此时实际指向的是旧的my_index。

PUT /my_index/_alias/prod_index

（7）新建一个index，调整其title的类型为string。

PUT /my_index_new
{
  "mappings": {
    "properties": {
		"title": {
         "type": "text"
        }
    }
  }
}

（8）使用scroll api将数据批量查询出来。

GET /my_index/_search?scroll=1m
{
    "query": {
        "match_all": {}
    },    
    "size":  1
}

返回

{
  "_scroll_id": "DnF1ZXJ5VGhlbkZldGNoBQAAAAAAADpAFjRvbnNUWVZaVGpHdklqOV9zcFd6MncAAAAAAAA6QRY0b25zVFlWWlRqR3ZJajlfc3BXejJ3AAAAAAAAOkIWNG9uc1RZVlpUakd2SWo5X3NwV3oydwAAAAAAADpDFjRvbnNUWVZaVGpHdklqOV9zcFd6MncAAAAAAAA6RBY0b25zVFlWWlRqR3ZJajlfc3BXejJ3",
  "took": 1,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 3,
    "max_score": null,
    "hits": [
      {
        "_index": "my_index",
        "_type": "my_type",
        "_id": "1",
        "_score": null,
        "_source": {
          "title": "2019-01-02"
        },
        "sort": [
          0
        ]
      }
    ]
  }
}

（9）采用bulk api将scoll查出来的一批数据，批量写入新索引。

POST /_bulk
{ "index":  { "_index": "my_index_new", "_id": "1" }}
{ "title":    "2019-09-10" }

（10）反复循环8~9，查询一批又一批的数据出来，采取bulk api将每一批数据批量写入新索引。

（11）将prod_index alias切换到my_index_new上去，java应用会直接通过index别名使用新的索引中的数据，java应用程序不需要停机，零提交，高可用。

POST /_aliases
{
    "actions": [
        { "remove": { "index": "my_index", "alias": "prod_index" }},
        { "add":    { "index": "my_index_new", "alias": "prod_index" }}
    ]
}

（12）直接通过prod_index别名来查询，是否ok。

GET /prod_index/_search

11.5.2 生产实践：基于alias对client透明切换index

PUT /my_index_v1/_alias/my_index

client对my_index进行操作

reindex操作，完成之后，切换v1到v2

POST /_aliases
{
    "actions": [
        { "remove": { "index": "my_index_v1", "alias": "my_index" }},
        { "add":    { "index": "my_index_v2", "alias": "my_index" }}
    ]
}

12．中文分词器 IK分词器

12.1 Ik分词器安装使用

12.1.1 中文分词器

standard 分词器，仅适用于英文。

GET /_analyze
{
  "analyzer": "standard",
  "text": "中华人民共和国人民大会堂"
}

想要的效果是什么：中华人民共和国，人民大会堂

IK分词器就是目前最流行的es中文分词器

12.1.2 安装

官网：https://github.com/medcl/elasticsearch-analysis-ik

下载地址：https://github.com/medcl/elasticsearch-analysis-ik/releases

根据es版本下载相应版本包。

解压到 es/plugins/ik中。

重启es

12.1.3 ik分词器基础知识

ik_max_word: 会将文本做最细粒度的拆分，比如会将“中华人民共和国人民大会堂”拆分为“中华人民共和国，中华人民，中华，华人，人民共和国，人民大会堂，人民大会，大会堂”，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“中华人民共和国人民大会堂”拆分为“中华人民共和国，人民大会堂”。

12.1.4 ik分词器的使用

存储时，使用ik_max_word，搜索时，使用ik_smart

PUT /my_index 
{
  "mappings": {
      "properties": {
        "text": {
          "type": "text",
          "analyzer": "ik_max_word",
          "search_analyzer": "ik_smart"
        }
      }
  }
}

搜索

GET /my_index/_search?q=中华人民共和国人民大会堂

12.2 ik配置文件

12.2.1 ik配置文件

ik配置文件地址：es/plugins/ik/config目录

• IKAnalyzer.cfg.xml：用来配置自定义词库

• main.dic：ik原生内置的中文词库，总共有27万多条，只要是这些单词，都会被分在一起

• preposition.dic: 介词

• quantifier.dic：放了一些单位相关的词，量词

• suffix.dic：放了一些后缀

• surname.dic：中国的姓氏

• stopword.dic：英文停用词

ik原生最重要的两个配置文件：

• main.dic：包含了原生的中文词语，会按照这个里面的词语去分词

• stopword.dic：包含了英文的停用词

停用词，stopword

a the and at but 停用词，会在分词的时候，直接被干掉，不会建立在倒排索引中

12.2.2 自定义词库

（1）自己建立词库：每年都会涌现一些特殊的流行词，网红，蓝瘦香菇，喊麦，鬼畜，一般不会在ik的原生词典里

• 自己补充自己的最新的词语，到ik的词库里面

• IKAnalyzer.cfg.xml：ext_dict，创建mydict.dic

• 补充自己的词语，然后需要重启es，才能生效

（2）自己建立停用词库：比如了，的，啥，么，我们可能并不想去建立索引，让人家搜索

• custom/ext_stopword.dic，已经有了常用的中文停用词，可以补充自己的停用词，然后重启es

12.3 使用mysql热更新

12.3.1 热更新

每次都是在es的扩展词典中，手动添加新词语，很坑

（1）每次添加完，都要重启es才能生效，非常麻烦

（2）es是分布式的，可能有数百个节点，你不能每次都一个一个节点上面去修改

es不停机，直接我们在外部某个地方添加新的词语，es中立即热加载到这些新词语

热更新的方案

（1）基于ik分词器原生支持的热更新方案，部署一个web服务器，提供一个http接口，通过modified和tag两个http响应头，来提供词语的热更新

（2）修改ik分词器源码，然后手动支持从mysql中每隔一定时间，自动加载新的词库

用第二种方案，第一种，ik git社区官方都不建议采用，觉得不太稳定

12.3.2 步骤

1、下载源码

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

ik分词器，是个标准的java maven工程，直接导入eclipse就可以看到源码

2、修改源

• org.wltea.analyzer.dic.Dictionary类，160行Dictionary单例类的初始化方法，在这里需要创建一个我们自定义的线程，并且启动它

• org.wltea.analyzer.dic.HotDictReloadThread类：就是死循环，不断调用Dictionary.getSingleton().reLoadMainDict()，去重新加载词典

• Dictionary类，399行：this.loadMySQLExtDict(); 加载mymsql字典

• Dictionary类，609行：this.loadMySQLStopwordDict();加载mysql停用词

• config下jdbc-reload.properties。mysql配置文件

3、mvn package打包代码

target\releases\elasticsearch-analysis-ik-7.3.0.zip

4、解压缩ik压缩包

将mysql驱动jar，放入ik的目录下

5、修改jdbc相关配置

6、重启es

观察日志，日志中就会显示我们打印的那些东西，比如加载了什么配置，加载了什么词语，什么停用词

7、在mysql中添加词库与停用词

8、分词实验，验证热更新生效

GET /_analyze
{
  "analyzer": "ik_smart",
  "text": "传智播客"
}

13．java api 实现索引管理

package com.itheima.es;

import org.elasticsearch.action.ActionListener;
import org.elasticsearch.action.admin.indices.alias.Alias;
import org.elasticsearch.action.admin.indices.close.CloseIndexRequest;
import org.elasticsearch.action.admin.indices.delete.DeleteIndexRequest;
import org.elasticsearch.action.admin.indices.open.OpenIndexRequest;
import org.elasticsearch.action.admin.indices.open.OpenIndexResponse;
import org.elasticsearch.action.support.ActiveShardCount;
import org.elasticsearch.action.support.master.AcknowledgedResponse;
import org.elasticsearch.client.IndicesClient;
import org.elasticsearch.client.RequestOptions;
import org.elasticsearch.client.RestHighLevelClient;
import org.elasticsearch.client.indices.CreateIndexRequest;
import org.elasticsearch.client.indices.CreateIndexResponse;
import org.elasticsearch.client.indices.GetIndexRequest;
import org.elasticsearch.common.settings.Settings;
import org.elasticsearch.common.unit.TimeValue;
import org.elasticsearch.common.xcontent.XContentType;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.test.context.junit4.SpringRunner;

import java.io.IOException;

/**

- @author Administrator

- @version 1.0
  **/
  @SpringBootTest
  @RunWith(SpringRunner.class)
  public class TestIndex {

  @Autowired
  RestHighLevelClient client;

//    @Autowired
//    RestClient restClient;

​```
//创建索引
@Test
public void testCreateIndex() throws IOException {
    //创建索引对象
    CreateIndexRequest createIndexRequest = new CreateIndexRequest("itheima_book");
    //设置参数
    createIndexRequest.settings(Settings.builder().put("number_of_shards", "1").put("number_of_replicas", "0"));
    //指定映射1
    createIndexRequest.mapping(" {\n" +
            " \t\"properties\": {\n" +
            "            \"name\":{\n" +
            "             \"type\":\"keyword\"\n" +
            "           },\n" +
            "           \"description\": {\n" +
            "              \"type\": \"text\"\n" +
            "           },\n" +
            "            \"price\":{\n" +
            "             \"type\":\"long\"\n" +
            "           },\n" +
            "           \"pic\":{\n" +
            "             \"type\":\"text\",\n" +
            "             \"index\":false\n" +
            "           }\n" +
            " \t}\n" +
            "}", XContentType.JSON);

    //指定映射2
​```

//        Map message = new HashMap<>();
//        message.put("type", "text");
//        Map properties = new HashMap<>();
//        properties.put("message", message);
//        Map mapping = new HashMap<>();
//        mapping.put("properties", properties);
//        createIndexRequest.mapping(mapping);

​```
    //指定映射3
​```

//        XContentBuilder builder = XContentFactory.jsonBuilder();
//        builder.startObject();
//        {
//            builder.startObject("properties");
//            {
//                builder.startObject("message");
//                {
//                    builder.field("type", "text");
//                }
//                builder.endObject();
//            }
//            builder.endObject();
//        }
//        builder.endObject();
//        createIndexRequest.mapping(builder);

​```
    //设置别名
    createIndexRequest.alias(new Alias("itheima_index_new"));

    // 额外参数
    //设置超时时间
    createIndexRequest.setTimeout(TimeValue.timeValueMinutes(2));
    //设置主节点超时时间
    createIndexRequest.setMasterTimeout(TimeValue.timeValueMinutes(1));
    //在创建索引API返回响应之前等待的活动分片副本的数量，以int形式表示
    createIndexRequest.waitForActiveShards(ActiveShardCount.from(2));
    createIndexRequest.waitForActiveShards(ActiveShardCount.DEFAULT);

    //操作索引的客户端
    IndicesClient indices = client.indices();
    //执行创建索引库
    CreateIndexResponse createIndexResponse = indices.create(createIndexRequest, RequestOptions.DEFAULT);

    //得到响应（全部）
    boolean acknowledged = createIndexResponse.isAcknowledged();
    //得到响应 指示是否在超时前为索引中的每个分片启动了所需数量的碎片副本
    boolean shardsAcknowledged = createIndexResponse.isShardsAcknowledged();

    System.out.println("!!!!!!!!!!!!!!!!!!!!!!!!!!!" + acknowledged);
    System.out.println(shardsAcknowledged);

}

//异步新增索引
@Test
public void testCreateIndexAsync() throws IOException {
    //创建索引对象
    CreateIndexRequest createIndexRequest = new CreateIndexRequest("itheima_book2");
    //设置参数
    createIndexRequest.settings(Settings.builder().put("number_of_shards", "1").put("number_of_replicas", "0"));
    //指定映射1
    createIndexRequest.mapping(" {\n" +
            " \t\"properties\": {\n" +
            "            \"name\":{\n" +
            "             \"type\":\"keyword\"\n" +
            "           },\n" +
            "           \"description\": {\n" +
            "              \"type\": \"text\"\n" +
            "           },\n" +
            "            \"price\":{\n" +
            "             \"type\":\"long\"\n" +
            "           },\n" +
            "           \"pic\":{\n" +
            "             \"type\":\"text\",\n" +
            "             \"index\":false\n" +
            "           }\n" +
            " \t}\n" +
            "}", XContentType.JSON);

    //监听方法
    ActionListener<CreateIndexResponse> listener =
            new ActionListener<CreateIndexResponse>() {

                @Override
                public void onResponse(CreateIndexResponse createIndexResponse) {
                    System.out.println("!!!!!!!!创建索引成功");
                    System.out.println(createIndexResponse.toString());
                }

                @Override
                public void onFailure(Exception e) {
                    System.out.println("!!!!!!!!创建索引失败");
                    e.printStackTrace();
                }
            };

    //操作索引的客户端
    IndicesClient indices = client.indices();
    //执行创建索引库
    indices.createAsync(createIndexRequest, RequestOptions.DEFAULT, listener);

    try {
        Thread.sleep(5000);
    } catch (InterruptedException e) {
        e.printStackTrace();
    }
​```

​```
}
​```

​```
//删除索引库
@Test
public void testDeleteIndex() throws IOException {
    //删除索引对象
    DeleteIndexRequest deleteIndexRequest = new DeleteIndexRequest("itheima_book2");
    //操作索引的客户端
    IndicesClient indices = client.indices();
    //执行删除索引
    AcknowledgedResponse delete = indices.delete(deleteIndexRequest, RequestOptions.DEFAULT);
    //得到响应
    boolean acknowledged = delete.isAcknowledged();
    System.out.println(acknowledged);

}

//异步删除索引库
@Test
public void testDeleteIndexAsync() throws IOException {
    //删除索引对象
    DeleteIndexRequest deleteIndexRequest = new DeleteIndexRequest("itheima_book2");
    //操作索引的客户端
    IndicesClient indices = client.indices();

    //监听方法
    ActionListener<AcknowledgedResponse> listener =
            new ActionListener<AcknowledgedResponse>() {
                @Override
                public void onResponse(AcknowledgedResponse deleteIndexResponse) {
                    System.out.println("!!!!!!!!删除索引成功");
                    System.out.println(deleteIndexResponse.toString());
                }

                @Override
                public void onFailure(Exception e) {
                    System.out.println("!!!!!!!!删除索引失败");
                    e.printStackTrace();
                }
            };
    //执行删除索引
    indices.deleteAsync(deleteIndexRequest, RequestOptions.DEFAULT, listener);

    try {
        Thread.sleep(5000);
    } catch (InterruptedException e) {
        e.printStackTrace();
    }

}

// Indices Exists API
@Test
public void testExistIndex() throws IOException {
    GetIndexRequest request = new GetIndexRequest("itheima_book");
    request.local(false);//从主节点返回本地信息或检索状态
    request.humanReadable(true);//以适合人类的格式返回结果
    request.includeDefaults(false);//是否返回每个索引的所有默认设置

    boolean exists = client.indices().exists(request, RequestOptions.DEFAULT);
    System.out.println(exists);
}
​```

​```
// Indices Open API
@Test
public void testOpenIndex() throws IOException {
    OpenIndexRequest request = new OpenIndexRequest("itheima_book");

    OpenIndexResponse openIndexResponse = client.indices().open(request, RequestOptions.DEFAULT);
    boolean acknowledged = openIndexResponse.isAcknowledged();
    System.out.println("!!!!!!!!!"+acknowledged);
}

// Indices Close API
@Test
public void testCloseIndex() throws IOException {
    CloseIndexRequest request = new CloseIndexRequest("index");
    AcknowledgedResponse closeIndexResponse = client.indices().close(request, RequestOptions.DEFAULT);
    boolean acknowledged = closeIndexResponse.isAcknowledged();
    System.out.println("!!!!!!!!!"+acknowledged);

}
}

14．search搜索入门

14.1 搜索语法入门

14.1.1 query string search

无条件搜索所有

GET /book/_search

{
  "took" : 969,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 3,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "book",
        "_type" : "_doc",
        "_id" : "1",
        "_score" : 1.0,
        "_source" : {
          "name" : "Bootstrap开发",
          "description" : "Bootstrap是由Twitter推出的一个前台页面开发css框架，是一个非常流行的开发框架，此框架集成了多种页面效果。此开发框架包含了大量的CSS、JS程序代码，可以帮助开发者（尤其是不擅长css页面开发的程序人员）轻松的实现一个css，不受浏览器限制的精美界面css效果。",
          "studymodel" : "201002",
          "price" : 38.6,
          "timestamp" : "2019-08-25 19:11:35",
          "pic" : "group1/M00/00/00/wKhlQFs6RCeAY0pHAAJx5ZjNDEM428.jpg",
          "tags" : [
            "bootstrap",
            "dev"
          ]
        }
      },
      {
        "_index" : "book",
        "_type" : "_doc",
        "_id" : "2",
        "_score" : 1.0,
        "_source" : {
          "name" : "java编程思想",
          "description" : "java语言是世界第一编程语言，在软件开发领域使用人数最多。",
          "studymodel" : "201001",
          "price" : 68.6,
          "timestamp" : "2019-08-25 19:11:35",
          "pic" : "group1/M00/00/00/wKhlQFs6RCeAY0pHAAJx5ZjNDEM428.jpg",
          "tags" : [
            "java",
            "dev"
          ]
        }
      },
      {
        "_index" : "book",
        "_type" : "_doc",
        "_id" : "3",
        "_score" : 1.0,
        "_source" : {
          "name" : "spring开发基础",
          "description" : "spring 在java领域非常流行，java程序员都在用。",
          "studymodel" : "201001",
          "price" : 88.6,
          "timestamp" : "2019-08-24 19:11:35",
          "pic" : "group1/M00/00/00/wKhlQFs6RCeAY0pHAAJx5ZjNDEM428.jpg",
          "tags" : [
            "spring",
            "java"
          ]
        }
      }
    ]
  }
}

解释

• took：耗费了几毫秒

• timed_out：是否超时，这里是没有

• _shards：到几个分片搜索，成功几个，跳过几个，失败几个

• hits.total：查询结果的数量，3个document

• hits.max_score：score的含义，就是document对于一个search的相关度的匹配分数，越相关，就越匹配，分数也高

• hits.hits：包含了匹配搜索的document的所有详细数据

14.1.2 传参

与http请求传参类似

GET /book/_search?q=name:java&sort=price:desc

类比sql: select * from book where name like ’ %java%’ order by price desc

{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [
      {
        "_index" : "book",
        "_type" : "_doc",
        "_id" : "2",
        "_score" : null,
        "_source" : {
          "name" : "java编程思想",
          "description" : "java语言是世界第一编程语言，在软件开发领域使用人数最多。",
          "studymodel" : "201001",
          "price" : 68.6,
          "timestamp" : "2019-08-25 19:11:35",
          "pic" : "group1/M00/00/00/wKhlQFs6RCeAY0pHAAJx5ZjNDEM428.jpg",
          "tags" : [
            "java",
            "dev"
          ]
        },
        "sort" : [
          68.6
        ]
      }
    ]
  }
}

14.1.3 图解timeout

GET /book/_search?timeout=10ms

全局设置：配置文件中设置 search.default_search_timeout：100ms。默认不超时。

14.2 multi-index 多索引搜索

14.2.1 multi-index搜索模式

如何一次性搜索多个index和多个type下的数据

/_search：所有索引下的所有数据都搜索出来
/index1/_search：指定一个index，搜索其下所有的数据
/index1,index2/_search：同时搜索两个index下的数据
/index*/_search：按照通配符去匹配多个索引

应用场景：生产环境log索引可以按照日期分开。

log_to_es_20190910

log_to_es_20190911

log_to_es_20180910

14.2.2 图解简单的搜索原理

搜索原理初步图解

14.3 分页搜索

14.3.1 分页搜索的语法

sql: select * from book limit 1,5

size，from

GET /book/_search?size=10

GET /book/_search?size=10&from=0

GET /book/_search?size=10&from=20

GET /book_search?from=0&size=3

14.3.2 deep paging

什么是deep paging

根据相关度评分倒排序，所以分页过深，协调节点会将大量数据聚合分析。

deep paging 性能问题

1 消耗网络带宽，因为所搜过深的话，各 shard 要把数据传递给 coordinate node，这个过程是有大量数据传递的，消耗网络。

2 消耗内存，各 shard 要把数据传送给 coordinate node，这个传递回来的数据，是被 coordinate node 保存在内存中的，这样会大量消耗内存。

3 消耗cup，coordinate node 要把传回来的数据进行排序，这个排序过程很消耗cpu。

所以：鉴于deep paging的性能问题，所有应尽量减少使用。

14.4 query string基础语法

14.4.1 query string基础语法

GET /book/_search?q=name:java

GET /book/_search?q=+name:java

GET /book/_search?q=-name:java

一个是掌握q=field:search content的语法，还有一个是掌握+和-的含义

14.4.2 _all metadata的原理和作用

GET /book/_search?q=java

直接可以搜索所有的field，任意一个field包含指定的关键字就可以搜索出来。我们在进行中搜索的时候，难道是对document中的每一个field都进行一次搜索吗？不是的。

es中_all元数据。建立索引的时候，插入一条docunment，es会将所有的field值经行全量分词，把这些分词，放到_all field中。在搜索的时候，没有指定field，就在_all搜索。

举例

{
    name:jack
    email:[email protected]
    address:beijing
}

_all : jack,[email protected],beijing

14.5 query DSL入门

14.5.1 DSL

query string 后边的参数原来越多，搜索条件越来越复杂，不能满足需求。

GET /book/_search?q=name:java&size=10&from=0&sort=price:desc

DSL：Domain Specified Language，特定领域的语言

es特有的搜索语言，可在请求体中携带搜索条件，功能强大。

查询全部 GET /book/_search

GET /book/_search
{
  "query": { "match_all": {} }
}

排序 GET /book/_search?sort=price:desc

GET /book/_search 
{
    "query" : {
        "match" : {
            "name" : " java"
        }
    },
    "sort": [
        { "price": "desc" }
    ]
}

分页查询 GET /book/_search?size=10&from=0

GET  /book/_search 
{
  "query": { "match_all": {} },
  "from": 0,
  "size": 1
}

指定返回字段 GET /book/ _search? _source=name,studymodel

GET /book/_search 
{
  "query": { "match_all": {} },
  "_source": ["name", "studymodel"]
}

通过组合以上各种类型查询，实现复杂查询。

14.5.2 Query DSL语法

{
    QUERY_NAME: {
        ARGUMENT: VALUE,
        ARGUMENT: VALUE,...
    }
}

{
    QUERY_NAME: {
        FIELD_NAME: {
            ARGUMENT: VALUE,
            ARGUMENT: VALUE,...
        }
    }
}

GET /test_index/_search 
{
  "query": {
    "match": {
      "test_field": "test"
    }
  }
}

14.5.3 组合多个搜索条件

搜索需求：title必须包含elasticsearch，content可以包含elasticsearch也可以不包含，author_id必须不为111

sql where and or !=

初始数据：

POST /website/_doc/1
{
          "title": "my hadoop article",
          "content": "hadoop is very bad",
          "author_id": 111
}

POST /website/_doc/2
{
          "title": "my elasticsearch  article",
          "content": "es is very bad",
          "author_id": 112
}
POST /website/_doc/3
{
          "title": "my elasticsearch article",
          "content": "es is very goods",
          "author_id": 111
}

搜索：

GET /website/_doc/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "title": "elasticsearch"
          }
        }
      ],
      "should": [
        {
          "match": {
            "content": "elasticsearch"
          }
        }
      ],
      "must_not": [
        {
          "match": {
            "author_id": 111
          }
        }
      ]
    }
  }
}

返回：

{
  "took" : 488,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 0.47000363,
    "hits" : [
      {
        "_index" : "website",
        "_type" : "_doc",
        "_id" : "2",
        "_score" : 0.47000363,
        "_source" : {
          "title" : "my elasticsearch  article",
          "content" : "es is very bad",
          "author_id" : 112
        }
      }
    ]
  }
}

更复杂的搜索需求：

select * from test_index where name=‘tom’ or (hired =true and (personality =‘good’ and rude != true ))

GET /test_index/_search
{
    "query": {
            "bool": {
                "must": { "match":{ "name": "tom" }},
                "should": [
                    { "match":{ "hired": true }},
                    { "bool": {
                        "must":{ "match": { "personality": "good" }},
                        "must_not": { "match": { "rude": true }}
                    }}
                ],
                "minimum_should_match": 1
            }
    }
}

14.6 full-text search 全文检索

14.6.1 全文检索

重新创建book索引

PUT /book/
{
  "settings": {
    "number_of_shards": 1,
    "number_of_replicas": 0
  },
  "mappings": {
    "properties": {
      "name":{
        "type": "text",
        "analyzer": "ik_max_word",
        "search_analyzer": "ik_smart"
      },
      "description":{
        "type": "text",
        "analyzer": "ik_max_word",
        "search_analyzer": "ik_smart"
      },
      "studymodel":{
        "type": "keyword"
      },
      "price":{
        "type": "double"
      },
      "timestamp": {
         "type": "date",
         "format": "yyyy-MM-dd HH:mm:ss||yyyy-MM-dd||epoch_millis"
      },
      "pic":{
        "type":"text",
        "index":false
      }
    }
  }
}

插入数据

PUT /book/_doc/1
{
"name": "Bootstrap开发",
"description": "Bootstrap是由Twitter推出的一个前台页面开发css框架，是一个非常流行的开发框架，此框架集成了多种页面效果。此开发框架包含了大量的CSS、JS程序代码，可以帮助开发者（尤其是不擅长css页面开发的程序人员）轻松的实现一个css，不受浏览器限制的精美界面css效果。",
"studymodel": "201002",
"price":38.6,
"timestamp":"2019-08-25 19:11:35",
"pic":"group1/M00/00/00/wKhlQFs6RCeAY0pHAAJx5ZjNDEM428.jpg",
"tags": [ "bootstrap", "dev"]
}

PUT /book/_doc/2
{
"name": "java编程思想",
"description": "java语言是世界第一编程语言，在软件开发领域使用人数最多。",
"studymodel": "201001",
"price":68.6,
"timestamp":"2019-08-25 19:11:35",
"pic":"group1/M00/00/00/wKhlQFs6RCeAY0pHAAJx5ZjNDEM428.jpg",
"tags": [ "java", "dev"]
}

PUT /book/_doc/3
{
"name": "spring开发基础",
"description": "spring 在java领域非常流行，java程序员都在用。",
"studymodel": "201001",
"price":88.6,
"timestamp":"2019-08-24 19:11:35",
"pic":"group1/M00/00/00/wKhlQFs6RCeAY0pHAAJx5ZjNDEM428.jpg",
"tags": [ "spring", "java"]
}

搜索

GET  /book/_search 
{
    "query" : {
        "match" : {
            "description" : "java程序员"
        }
    }
}

14.6.2 _score初探

{
  "took" : 1,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 2.137549,
    "hits" : [
      {
        "_index" : "book",
        "_type" : "_doc",
        "_id" : "3",
        "_score" : 2.137549,
        "_source" : {
          "name" : "spring开发基础",
          "description" : "spring 在java领域非常流行，java程序员都在用。",
          "studymodel" : "201001",
          "price" : 88.6,
          "timestamp" : "2019-08-24 19:11:35",
          "pic" : "group1/M00/00/00/wKhlQFs6RCeAY0pHAAJx5ZjNDEM428.jpg",
          "tags" : [
            "spring",
            "java"
          ]
        }
      },
      {
        "_index" : "book",
        "_type" : "_doc",
        "_id" : "2",
        "_score" : 0.57961315,
        "_source" : {
          "name" : "java编程思想",
          "description" : "java语言是世界第一编程语言，在软件开发领域使用人数最多。",
          "studymodel" : "201001",
          "price" : 68.6,
          "timestamp" : "2019-08-25 19:11:35",
          "pic" : "group1/M00/00/00/wKhlQFs6RCeAY0pHAAJx5ZjNDEM428.jpg",
          "tags" : [
            "java",
            "dev"
          ]
        }
      }
    ]
  }
}

结果分析

1、建立索引时, description字段 term倒排索引

• java 2,3
• 程序员 3

2、搜索时直接找description中含有java的文档 2,3，并且3号文档含有两个java字段一个程序员，所以得分高排在前面，2号文档含有一个java排在后面。

14.7 DSL 语法练习

14.7.1 match_all

GET /book/_search
{
    "query": {
        "match_all": {}
    }
}

14.7.2 match

GET /book/_search
{
	"query": { 
		"match": { 
			"description": "java程序员"
		}
	}
}

14.7.3 multi_match

GET /book/_search
{
  "query": {
    "multi_match": {
      "query": "java程序员",
      "fields": ["name", "description"]
    }
  }
}

14.7.4 range query 范围查询

GET /book/_search
{
  "query": {
    "range": {
      "price": {
        "gte": 80,
		"lte": 90
      }
    }
  }
}

14.7.5 term query

字段为keyword时，存储和搜索都不分词

GET /book/_search
{
  "query": {
    "term": {
      "description": "java程序员"
    }
  }
}

14.7.6 terms query

GET /book/_search
{
    "query": { "terms": { "tag": [ "search", "full_text", "nosql" ] }}
}

14.7.7 exist query 查询有某些字段值的文档

GET /_search
{
    "query": {
        "exists": {
            "field": "join_date"
        }
    }
}

14.7.8 Fuzzy query

返回包含与搜索词类似的词的文档，该词由Levenshtein编辑距离度量。

包括以下几种情况：

• 更改角色（box→fox）

• 删除字符（aple→apple）

• 插入字符（sick→sic）

• 调换两个相邻字符（ACT→CAT）

GET /book/_search
{
    "query": {
        "fuzzy": {
            "description": {
                "value": "jave"
            }
        }
    }
}

14.7.9 IDs

GET /book/_search
{
    "query": {
        "ids" : {
            "values" : ["1", "4", "100"]
        }
    }
}

14.7.10 prefix 前缀查询

GET /book/_search
{
    "query": {
        "prefix": {
            "description": {
                "value": "spring"
            }
        }
    }
}

14.7.11 regexp query 正则查询

GET /book/_search
{
    "query": {
        "regexp": {
            "description": {
                "value": "j.*a",
                "flags" : "ALL",
                "max_determinized_states": 10000,
                "rewrite": "constant_score"
            }
        }
    }
}

14.8 Filter

14.8.1 filter与query示例

需求：用户查询description中有"java程序员"，并且价格大于80小于90的数据。

GET /book/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "description": "java程序员"
          }
        },
        {
          "range": {
            "price": {
              "gte": 80,
		      "lte": 90
            }
          }
        }
      ]
    }
  }
}

使用filter:

GET /book/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "description": "java程序员"
          }
        }
      ],
      "filter": {
        "range": {
          "price": {
            "gte": 80,
		     "lte": 90
          }
        }
      }
    }
  }
}

14.8.2 filter与query对比

filter，仅仅只是按照搜索条件过滤出需要的数据而已，不计算任何相关度分数，对相关度没有任何影响。

query，会去计算每个document相对于搜索条件的相关度，并按照相关度进行排序。

应用场景：

• 一般来说，如果你是在进行搜索，需要将最匹配搜索条件的数据先返回，那么用query
• 如果你只是要根据一些条件筛选出一部分数据，不关注其排序，那么用filter

14.8.3 filter与query性能

filter，不需要计算相关度分数，不需要按照相关度分数进行排序，同时还有内置的自动cache最常使用filter的数据

query，相反，要计算相关度分数，按照分数进行排序，而且无法cache结果

14.9 定位错误语法

验证错误语句：

GET /book/_validate/query?explain
{
  "query": {
    "mach": {
      "description": "java程序员"
    }
  }
}

返回：

{
  "valid" : false,
  "error" : "org.elasticsearch.common.ParsingException: no [query] registered for [mach]"
}

正确

GET /book/_validate/query?explain
{
  "query": {
    "match": {
      "description": "java程序员"
    }
  }
}

返回

{
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "failed" : 0
  },
  "valid" : true,
  "explanations" : [
    {
      "index" : "book",
      "valid" : true,
      "explanation" : "description:java description:程序员"
    }
  ]
}

一般用在那种特别复杂庞大的搜索下，比如一下写了上百行的搜索，这个时候可以先用validate api去验证一下，搜索是否合法。

合法以后，explain就像mysql的执行计划，可以看到搜索的目标等信息。

14.10 定制排序规则

14.10.1 默认排序规则

默认情况下，是按照_score降序排序的

然而，某些情况下，可能没有有用的_score，比如说filter

GET book/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "description": "java程序员"
          }
        }
      ]
    }
  }
}

当然，也可以是constant_score

14.10.2 定制排序规则

相当于sql中order by ?sort=sprice:desc

GET /book/_search 
{
  "query": {
    "constant_score": {
      "filter" : {
            "term" : {
                "studymodel" : "201001"
            }
        }
    }
  },
  "sort": [
    {
      "price": {
        "order": "asc"
      }
    }
  ]
}

14.11 Text字段排序问题

如果对一个text field进行排序，结果往往不准确，因为分词后是多个单词，再排序就不是我们想要的结果了。

通常解决方案是：

方案一：fielddate：true

方案二：将一个text field建立两次索引，一个分词 用来进行搜索；一个不分词 用来进行排序

PUT /website 
{
  "mappings": {
  "properties": {
    "title": {
      "type": "text",
      "fields": {
        "keyword": {
          "type": "keyword"
        }        
      }      
    },
    "content": {
      "type": "text"
    },
    "post_date": {
      "type": "date"
    },
    "author_id": {
      "type": "long"
    }
  }
 }
}

插入数据

PUT /website/_doc/1
{
  "title": "first article",
  "content": "this is my second article",
  "post_date": "2019-01-01",
  "author_id": 110
}

PUT /website/_doc/2
{
    "title": "second article",
    "content": "this is my second article",
     "post_date": "2019-01-01",
    "author_id": 110
}

PUT /website/_doc/3
{
     "title": "third article",
     "content": "this is my third article",
     "post_date": "2019-01-02",
     "author_id": 110
}

搜索

GET /website/_search
{
  "query": {
    "match_all": {}
  },
  "sort": [
    {
      "title.keyword": {
        "order": "desc"
      }
    }
  ]
}

14.12 Scroll分批查询

场景：下载某一个索引中1亿条数据，到文件或是数据库。不能一下全查出来，系统内存溢出。所以使用scoll滚动搜索技术，一批一批查询。

scoll搜索会在第一次搜索的时候，保存一个当时的视图快照，之后只会基于该旧的视图快照提供数据搜索，如果这个期间数据变更，是不会让用户看到的

每次发送scroll请求，我们还需要指定一个scoll参数，指定一个时间窗口，每次搜索请求只要在这个时间窗口内能完成就可以了。

搜索

GET /book/_search?scroll=1m
{
  "query": {
    "match_all": {}
  },
  "size": 3
}

返回

{
  "_scroll_id" : "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAMOkWTURBNDUtcjZTVUdKMFp5cXloVElOQQ==",
  "took" : 3,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 3,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
     
    ]
  }
}

获得的结果会有一个scoll_id，下一次再发送scoll请求的时候，必须带上这个scoll_id

GET /_search/scroll
{
    "scroll": "1m", 
    "scroll_id" : "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAMOkWTURBNDUtcjZTVUdKMFp5cXloVElOQQ=="
}

与分页区别：

分页给用户看的 deep paging

scroll是用户系统内部操作，如下载批量数据，数据转移。零停机改变索引映射。

15．java api实现搜索

package com.itheima.es;

import org.elasticsearch.action.search.SearchRequest;
import org.elasticsearch.action.search.SearchResponse;
import org.elasticsearch.client.RequestOptions;
import org.elasticsearch.client.RestHighLevelClient;
import org.elasticsearch.index.query.*;
import org.elasticsearch.search.SearchHit;
import org.elasticsearch.search.SearchHits;
import org.elasticsearch.search.builder.SearchSourceBuilder;
import org.elasticsearch.search.sort.SortOrder;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.test.context.junit4.SpringRunner;

import java.io.IOException;
import java.util.Map;

/**
 * creste by itheima.itcast
 */
@SpringBootTest
@RunWith(SpringRunner.class)
public class TestSearch {
    @Autowired
    RestHighLevelClient client;

    //搜索全部记录
    @Test
    public void testSearchAll() throws IOException {
//        GET book/_search
//        {
//            "query": {
//                "match_all": {}
//             }
//        }
        //1构建搜索请求
        SearchRequest searchRequest = new SearchRequest("book");
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(QueryBuilders.matchAllQuery());

        //获取某些字段
        searchSourceBuilder.fetchSource(new String[]{"name"}, new String[]{});
        searchRequest.source(searchSourceBuilder);

        //2执行搜索
        SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
        //3获取结果
        SearchHits hits = searchResponse.getHits();
        //数据数据
        SearchHit[] searchHits = hits.getHits();
        System.out.println("--------------------------");
        for (SearchHit hit : searchHits) {
            String id = hit.getId();
            float score = hit.getScore();
            Map<String, Object> sourceAsMap = hit.getSourceAsMap();
            String name = (String) sourceAsMap.get("name");
            String description = (String) sourceAsMap.get("description");
            Double price = (Double) sourceAsMap.get("price");
            System.out.println("name:" + name);
            System.out.println("description:" + description);
            System.out.println("price:" + price);
            System.out.println("==========================");
        }
    }


    //搜索分页
    @Test
    public void testSearchPage() throws IOException {
//    GET book/_search
//    {
//        "query": {
//          "match_all": {}
//       },
//        "from": 0,
//        "size": 2
//    }
        //1构建搜索请求
        SearchRequest searchRequest = new SearchRequest("book");
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(QueryBuilders.matchAllQuery());

        //第几页
        int page=1;
        //每页几个
        int size=2;
        //下标计算
        int from=(page-1)*size;
        
        searchSourceBuilder.from(from);
        searchSourceBuilder.size(size);
        searchRequest.source(searchSourceBuilder);

        //2执行搜索
        SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
        //3获取结果
        SearchHits hits = searchResponse.getHits();
        //数据数据
        SearchHit[] searchHits = hits.getHits();
        System.out.println("--------------------------");
        for (SearchHit hit : searchHits) {
            String id = hit.getId();
            float score = hit.getScore();
            Map<String, Object> sourceAsMap = hit.getSourceAsMap();
            String name = (String) sourceAsMap.get("name");
            String description = (String) sourceAsMap.get("description");
            Double price = (Double) sourceAsMap.get("price");
            System.out.println("id:" + id);
            System.out.println("name:" + name);
            System.out.println("description:" + description);
            System.out.println("price:" + price);
            System.out.println("==========================");
        }
    }


    //ids搜索
    @Test
    public void testSearchIds() throws IOException {
//    GET /book/_search
//    {
//        "query": {
//           "ids" : {
//             "values" : ["1", "4", "100"]
//          }
//     }
//    }
        //1构建搜索请求
        SearchRequest searchRequest = new SearchRequest("book");
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(QueryBuilders.idsQuery().addIds("1","4","100"));
        searchRequest.source(searchSourceBuilder);

        //2执行搜索
        SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
        //3获取结果
        SearchHits hits = searchResponse.getHits();
        //数据数据
        SearchHit[] searchHits = hits.getHits();
        System.out.println("--------------------------");
        for (SearchHit hit : searchHits) {
            String id = hit.getId();
            float score = hit.getScore();
            Map<String, Object> sourceAsMap = hit.getSourceAsMap();
            String name = (String) sourceAsMap.get("name");
            String description = (String) sourceAsMap.get("description");
            Double price = (Double) sourceAsMap.get("price");
            System.out.println("id:" + id);
            System.out.println("name:" + name);
            System.out.println("description:" + description);
            System.out.println("price:" + price);
            System.out.println("==========================");
        }
    }


    //match搜索
    @Test
    public void testSearchMatch() throws IOException {
//
//    GET /book/_search
//    {
//        "query": {
//           "match": {
//            "description": "java程序员"
//        }
//      }
//    }
        //1构建搜索请求
        SearchRequest searchRequest = new SearchRequest("book");
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(QueryBuilders.matchQuery("description", "java程序员"));
        searchRequest.source(searchSourceBuilder);

        //2执行搜索
        SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
        //3获取结果
        SearchHits hits = searchResponse.getHits();
        //数据数据
        SearchHit[] searchHits = hits.getHits();
        System.out.println("--------------------------");
        for (SearchHit hit : searchHits) {
            String id = hit.getId();
            float score = hit.getScore();
            Map<String, Object> sourceAsMap = hit.getSourceAsMap();
            String name = (String) sourceAsMap.get("name");
            String description = (String) sourceAsMap.get("description");
            Double price = (Double) sourceAsMap.get("price");
            System.out.println("id:" + id);
            System.out.println("name:" + name);
            System.out.println("description:" + description);
            System.out.println("price:" + price);
            System.out.println("==========================");
        }
    }

    //term 搜索
    @Test
    public void testSearchTerm() throws IOException {
//
//    GET /book/_search
//    {
//        "query": {
//           "term": {
//            "description": "java程序员"
//        }
//      }
//    }
        //1构建搜索请求
        SearchRequest searchRequest = new SearchRequest("book");
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(QueryBuilders.termQuery("description", "java程序员"));
        searchRequest.source(searchSourceBuilder);

        //2执行搜索
        SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
        //3获取结果
        SearchHits hits = searchResponse.getHits();
        //数据数据
        SearchHit[] searchHits = hits.getHits();
        System.out.println("--------------------------");
        for (SearchHit hit : searchHits) {
            String id = hit.getId();
            float score = hit.getScore();
            Map<String, Object> sourceAsMap = hit.getSourceAsMap();
            String name = (String) sourceAsMap.get("name");
            String description = (String) sourceAsMap.get("description");
            Double price = (Double) sourceAsMap.get("price");
            System.out.println("id:" + id);
            System.out.println("name:" + name);
            System.out.println("description:" + description);
            System.out.println("price:" + price);
            System.out.println("==========================");
        }
    }


    //multi_match搜索
    @Test
    public void testSearchMultiMatch() throws IOException {
//    GET /book/_search
//    {
//        "query": {
//          "multi_match": {
//            "query": "java程序员",
//            "fields": ["name", "description"]
//        }
//      }
//    }
        //1构建搜索请求
        SearchRequest searchRequest = new SearchRequest("book");
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(QueryBuilders.multiMatchQuery("java程序员","name","description"));
        searchRequest.source(searchSourceBuilder);

        //2执行搜索
        SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
        //3获取结果
        SearchHits hits = searchResponse.getHits();
        //数据数据
        SearchHit[] searchHits = hits.getHits();
        System.out.println("--------------------------");
        for (SearchHit hit : searchHits) {
            String id = hit.getId();
            float score = hit.getScore();
            Map<String, Object> sourceAsMap = hit.getSourceAsMap();
            String name = (String) sourceAsMap.get("name");
            String description = (String) sourceAsMap.get("description");
            Double price = (Double) sourceAsMap.get("price");
            System.out.println("id:" + id);
            System.out.println("name:" + name);
            System.out.println("description:" + description);
            System.out.println("price:" + price);
            System.out.println("==========================");
        }
    }
//    GET /book/_search
//    {
//        "query": {
//        "bool": {
//            "must": [
//            {
//                "multi_match": {
//                "query": "java程序员",
//                        "fields": ["name","description"]
//            }
//            }
//      ],
//            "should": [
//            {
//                "match": {
//                "studymodel": "201001"
//            }
//            }
//      ]
//        }
//    }
//    }

    //bool搜索
    @Test
    public void testSearchBool() throws IOException {
//    GET /book/_search
//    {
//        "query": {
//          "bool": {
//            "must": [
//            {
//                "multi_match": {
//                  "query": "java程序员",
//                  "fields": ["name","description"]
//            }
//            }
//      ],
//            "should": [
//            {
//                "match": {
//                "studymodel": "201001"
//            }
//            }
//      ]
//        }
//    }
//    }
        //1构建搜索请求
        SearchRequest searchRequest = new SearchRequest("book");
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();

        //构建multiMatch请求
        MultiMatchQueryBuilder multiMatchQueryBuilder = QueryBuilders.multiMatchQuery("java程序员", "name", "description");
        //构建match请求
        MatchQueryBuilder matchQueryBuilder = QueryBuilders.matchQuery("studymodel", "201001");

        BoolQueryBuilder boolQueryBuilder=QueryBuilders.boolQuery();
        boolQueryBuilder.must(multiMatchQueryBuilder);
        boolQueryBuilder.should(matchQueryBuilder);

        searchSourceBuilder.query(boolQueryBuilder);
        searchRequest.source(searchSourceBuilder);

        //2执行搜索
        SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
        //3获取结果
        SearchHits hits = searchResponse.getHits();
        //数据数据
        SearchHit[] searchHits = hits.getHits();
        System.out.println("--------------------------");
        for (SearchHit hit : searchHits) {
            String id = hit.getId();
            float score = hit.getScore();
            Map<String, Object> sourceAsMap = hit.getSourceAsMap();
            String name = (String) sourceAsMap.get("name");
            String description = (String) sourceAsMap.get("description");
            Double price = (Double) sourceAsMap.get("price");
            System.out.println("id:" + id);
            System.out.println("name:" + name);
            System.out.println("description:" + description);
            System.out.println("price:" + price);
            System.out.println("==========================");
        }
    }

//    GET /book/_search
//    {
//        "query": {
//          "bool": {
//            "must": [
//            {
//                "multi_match": {
//                "query": "java程序员",
//                        "fields": ["name","description"]
//            }
//            }
//      ],
//            "should": [
//            {
//                "match": {
//                "studymodel": "201001"
//            }
//            }
//      ],
//            "filter": {
//                "range": {
//                    "price": {
//                        "gte": 50,
//                                "lte": 90
//                    }
//                }
//
//            }
//        }
//    }
//    }

    //filter搜索
    @Test
    public void testSearchFilter() throws IOException {
//    GET /book/_search
//    {
//        "query": {
//          "bool": {
//            "must": [
//            {
//                "multi_match": {
//                "query": "java程序员",
//                        "fields": ["name","description"]
//            }
//            }
//      ],
//            "should": [
//            {
//                "match": {
//                "studymodel": "201001"
//            }
//            }
//          ],
//            "filter": {
//                "range": {
//                    "price": {
//                        "gte": 50,
//                         "lte": 90
//                    }
//                }
//
//            }
//        }
//    }
//    }
        //1构建搜索请求
        SearchRequest searchRequest = new SearchRequest("book");
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();

        //构建multiMatch请求
        MultiMatchQueryBuilder multiMatchQueryBuilder = QueryBuilders.multiMatchQuery("java程序员", "name", "description");
        //构建match请求
        MatchQueryBuilder matchQueryBuilder = QueryBuilders.matchQuery("studymodel", "201001");

        BoolQueryBuilder boolQueryBuilder=QueryBuilders.boolQuery();
        boolQueryBuilder.must(multiMatchQueryBuilder);
        boolQueryBuilder.should(matchQueryBuilder);

        boolQueryBuilder.filter(QueryBuilders.rangeQuery("price").gte(50).lte(90));
        searchSourceBuilder.query(boolQueryBuilder);
        searchRequest.source(searchSourceBuilder);

        //2执行搜索
        SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
        //3获取结果
        SearchHits hits = searchResponse.getHits();
        //数据数据
        SearchHit[] searchHits = hits.getHits();
        System.out.println("--------------------------");
        for (SearchHit hit : searchHits) {
            String id = hit.getId();
            float score = hit.getScore();
            Map<String, Object> sourceAsMap = hit.getSourceAsMap();
            String name = (String) sourceAsMap.get("name");
            String description = (String) sourceAsMap.get("description");
            Double price = (Double) sourceAsMap.get("price");
            System.out.println("id:" + id);
            System.out.println("name:" + name);
            System.out.println("description:" + description);
            System.out.println("price:" + price);
            System.out.println("==========================");
        }
    }


    //sort搜索
    @Test
    public void testSearchSort() throws IOException {
//    GET /book/_search
//    {
//        "query": {
//        "bool": {
//            "must": [
//            {
//                "multi_match": {
//                "query": "java程序员",
//                        "fields": ["name","description"]
//            }
//            }
//      ],
//            "should": [
//            {
//                "match": {
//                "studymodel": "201001"
//            }
//            }
//      ],
//            "filter": {
//                "range": {
//                    "price": {
//                        "gte": 50,
//                                "lte": 90
//                    }
//                }
//
//            }
//        }
//    },
//        "sort": [
//        {
//            "price": {
//            "order": "asc"
//        }
//        }
//  ]
//    }
        //1构建搜索请求
        SearchRequest searchRequest = new SearchRequest("book");
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();

        //构建multiMatch请求
        MultiMatchQueryBuilder multiMatchQueryBuilder = QueryBuilders.multiMatchQuery("java程序员", "name", "description");
        //构建match请求
        MatchQueryBuilder matchQueryBuilder = QueryBuilders.matchQuery("studymodel", "201001");

        BoolQueryBuilder boolQueryBuilder=QueryBuilders.boolQuery();
        boolQueryBuilder.must(multiMatchQueryBuilder);
        boolQueryBuilder.should(matchQueryBuilder);
        boolQueryBuilder.filter(QueryBuilders.rangeQuery("price").gte(50).lte(90));
        searchSourceBuilder.query(boolQueryBuilder);

        //按照价格升序
        searchSourceBuilder.sort("price", SortOrder.ASC);
        searchRequest.source(searchSourceBuilder);

        //2执行搜索
        SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
        //3获取结果
        SearchHits hits = searchResponse.getHits();
        //数据数据
        SearchHit[] searchHits = hits.getHits();
        System.out.println("--------------------------");
        for (SearchHit hit : searchHits) {
            String id = hit.getId();
            float score = hit.getScore();
            Map<String, Object> sourceAsMap = hit.getSourceAsMap();
            String name = (String) sourceAsMap.get("name");
            String description = (String) sourceAsMap.get("description");
            Double price = (Double) sourceAsMap.get("price");
            System.out.println("id:" + id);
            System.out.println("name:" + name);
            System.out.println("description:" + description);
            System.out.println("price:" + price);
            System.out.println("==========================");
        }
    }
}