1、_cat

2、put/post 新增数据

post新增如果不指定id，会自动生成id。指定id就会修改当前id的数据，并新增版本号。
put必须指定id，一般用于修改操作，不指定id会报错。

3、数据修改乐观锁

_seq_no 并发控制字段，每次更新会+1
_primary_term 同上，主分片重新分配，如重启，会变化
更新携带以上两个字段，实现乐观锁机制 ？if_seq_no=0&_primary_term=1
更新前查询数据，利用 _seq_no 和 _primary_term 去更新数据，如果同时两个操作都在修改同一条数据，当其中一个操作执行完会更新 _seq_no 和 _primary_term 此时，另一个更新操作就会失败，返回status：409错误。

4、Query DSL

4.1 _bulk 批量导入

数据来源官方提供https://raw.githubusercontent.com/elastic/elasticsearch/master/docs/src/test/resources/accounts.json

curl -X POST "localhost:9200/bank/_bulk?pretty&refresh" -H 'Content-Type: application/json' -d'
{"index":{"_id":"1"}}
{"index":{"_id":"6"}}
{"account_number":6,"balance":5686,"firstname":"Hattie","lastname":"Bond","age":36,"gender":"M","address":"671 Bristol Street","employer":"Netagy","email":"hattiebond@netagy.com","city":"Dante","state":"TN"}
{"index":{"_id":"13"}}

4.2 match_all 匹配所有文档

最简单的查询，它匹配所有文档，给出所有文档的分数为1.0
_source：自定义返回数据的字段

curl -XGET "http://localhost:9200/bank/_search" -H 'Content-Type: application/json' -d'
{
"query": {
  "match_all": {}
},
"sort": [
  {
    "balance": {
      "order": "desc"
    }
  }
],
"from": 0,
"size": 20,
"_source": ["balance","firstname"]
}

4.3 match 全文检索

检索字符类型数据为全文检索（模糊查询，空格分词，查询mill不会查询出miller数据），检索数值型数据则为精确匹配

curl -XGET "http://localhost:9200/bank/_search" -H 'Content-Type: application/json' -d'
{
"query": {
  "match": {
    "address": "Miller"
          }
    }
}

4.4 match_phrase 短语匹配

不分词

短语匹配 "address": "Miller Place"

GET bank/_search
{
"query": {
  "match_phrase": {
    "address": "Miller Place"
  }
}
}

不分词匹配还有字段名.keyword作为查询字段来防止分词

GET bank/_search
{
"query": {
  "match": {
    "address.keyword": "666 Miller Place"
  }
}
}

matchphrase 和 *.keyword的区别
match_phrase 的值是整个短语不会被分词，但是会类似%查询值%查询
.keyword查询相当于 _=查询值

4.5 multi_match 多字段匹配

会进行分词

4.6 bool 复合查询

合并多个查询条件
must：必须满足，满足的得分高
must_not ：必须不满足，不会提高文档得分
should ：最好满足，不满足也可以，满足的得分高

##复合查询
GET bank/_search
{
"query": {
  "bool": {
    "must": [
      {"match": {
        "gender":"M"
      }},
      {"match": {
        "address": "mill"
      }}
    ],
    "must_not": [
      {"match": {
        "age": "18"
      }}
    ],
    "should": [
      {"match": {
        "lastname": "Wallace"
      }}
    ]
  }
}
}

4.7 bool-filter 过滤查询

filter条件满足的不会提高数据得分，可用于不参与得分的字段查询，或者对查询出来的数据进行过滤，必须与复合查询一起使用，同级别的有must、must_not、should 都是复合查询中的查询规则。
层级关系：
bool -> must、must_not、should、
filter -> match、match_all、multi_match、match_phrase

##过滤查询
GET bank/_search
{
"query": {
  "bool": {
    "filter": {"range": {
      "age": {
        "gte": 10,
        "lte": 20
      }
    }}
  }
}
}

4.8 term查询

和match一样，匹配某个属性的值。全文检索字段用match，非text字段匹配用term。【规范化查询】

4.9 aggregations 聚合

数据分组，相当于group by，可查询多组聚合，平均值等
只查看聚合结果，不查看查询数据，则添加 “size”:0

GET bank/_search
{
"query": {
  "match": {
    "address": "Miller"
  }
},
"aggs": {
  "ageAgg": {
    "terms": {
      "field": "age",
      "size": 10
    }
  },
  "ageAvg":{
    "avg": {
      "field": "age"
    }
  },
  "balanceAvg":{
    "avg": {
      "field": "balance"
    }
  }
}，
"size":0
}

多级聚合

##按照年龄聚合，并查询各个年龄段的平均工资
GET bank/_search
{
"query": {
  "match_all": {}
},
"aggs": {
  "ageAgg": {
    "terms": {
      "field": "age"
    },
    "aggs": {
      "ageAvg": {
        "avg": {
          "field": "balance"
        }
      }
    }
  }
},
"size": 0
}

##按照年龄分布，并且这些年龄段中M的平均薪资和F的平均薪资以及这个年龄段的总体平均薪资
GET bank/_search
{
"query": {
  "match_all": {}
},
"aggs": {
    "terms": {
      "field": "age"
    },
    "aggs": {
      "genderAgg": {
        "terms": {
          "field": "gender.keyword"
        },
        "aggs": {
          "balanceAvg": {
            "avg": {
              "field": "balance"
            }
          }
        }
      },
      "totalAgg":{
        "avg": {
          "field": "balance"
        }
      }
    }
  }
},
"size": 0
}

5、mapping

es导入数据会自动根据数据创建对应的数据类型，大概为：
文本 -> text
数值 -> long
ES常用的数据类型有：https://www.elastic.co/guide/en/elasticsearch/reference/7.4/mapping-types.html

string text and keyword Numeric long, integer, short, byte, double,
float, half_float, scaled_float Date date Boolean boolean Object
object for single JSON objects Nested nested for arrays of JSON
objects IP ip for IPv4 and IPv6 addresses

##keyword 表示该字段不分词,lastname类型为text表示分词，但fields内的keyword的类型为keyword，则查询lastname.keyword 不分词

PUT /my-index
{
"mappings": {
  "properties": {
    "age":   { "type": "integer" },  
    "email": { "type": "keyword" },
    "name":   { "type": "text" },
    "lastname" : {
        "type" : "text",
        "fields" : {
          "keyword" : {
            "type" : "keyword",
            "ignore_above" : 256
          }
        }
      }
  }
}
}

 "type" : "text",

查询索引mapping
GET my-index/_mapping

6、添加新的映射字段

index 索引选项控制字段值是否被索引。它接受真或假，默认为真。未编入索引的字段不可查询。

PUT /my-index/_mapping
{
"properties": {
  "employee-id": {
    "type": "text",
    "index": false
  }
}
}

7、ik分词器

安装
https://github.com/medcl/elasticsearch-analysis-ik/releases
找到对应版本下载，解压到elasticsearch-7.4.2/plugins下，可以重命名ik，启动es即可
检测是否安装成功
http://localhost:9200/_cat/plugins
检测分词效果

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

热更新 IK 分词使用方法
目前该插件支持热更新 IK 分词，通过上文在 IK 配置文件中提到的如下配置

<!--用户可以在这里配置远程扩展字典 -->
<entry key="remote_ext_dict">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">location</entry>

其中 location 是指一个 url，比如 http://yoursite.com/getCustomDict，该请求只需满足以下两点即可完成分词热更新。
该 http 请求需要返回两个头部(header)，一个是 Last-Modified，一个是 ETag，这两者都是字符串类型，只要有一个发生变化，该插件就会去抓取新的分词进而更新词库。
该 http 请求返回的内容格式是一行一个分词，换行符用 \n 即可。
满足上面两点要求就可以实现热更新分词了，不需要重启 ES 实例。
可以将需自动更新的热词放在一个 UTF-8 编码的 .txt 文件里，放在 nginx 或其他简易 http server 下，当 .txt 文件修改时，http server 会在客户端请求该文件时自动返回相应的 Last-Modified 和 ETag。可以另外做一个工具来从业务系统提取相关词汇，并更新这个 .txt 文件。

QAQ:

自定义词典为什么没有生效？ 请确保你的扩展词典的文本格式为 UTF8 编码 ik_max_word 和 ik_smart 什么区别?
ik_max_word:
会将文本做最细粒度的拆分，比如会将“中华人民共和国国歌”拆分为“中华人民共和国,中华人民,中华,华人,人民共和国,人民,人,民,共和国,共和,和,国国,国歌”，会穷尽各种可能的组合，适合
Term Query(字符型分词查询)； ik_smart:
会做最粗粒度的拆分，比如会将“中华人民共和国国歌”拆分为“中华人民共和国,国歌”，适合 Phrase 查询（短语查询，不分词）。

8、springboot整合high-level-client

• pom引用

<dependency>
   <groupId>org.elasticsearch.client</groupId>
   <artifactId>elasticsearch-rest-high-level-client</artifactId>
   <version>7.4.2</version>
</dependency>

• 初始化client，并加载到spring容器内

@Configuration
public class ElasticSearchClient {
   @Bean
   public RestHighLevelClient esRestClient() {
       RestHighLevelClient client = new RestHighLevelClient(
               RestClient.builder(new HttpHost("localhost", 9200, "http")));
       return client;
  }
}

9、保存数据

1、配置文件添加RequestOptions

@Configuration
public class ElasticSearchClient {
   public static final RequestOptions COMMON_OPTIONS;
   static {
       RequestOptions.Builder builder = RequestOptions.DEFAULT.toBuilder();
//       builder.addHeader("Authorization", "Bearer " + TOKEN);
//       builder.setHttpAsyncResponseConsumerFactory(
//               new HttpAsyncResponseConsumerFactory
//                       .HeapBufferedResponseConsumerFactory(30 * 1024 * 1024 * 1024));
       COMMON_OPTIONS = builder.build();
  }
   @Bean
   public RestHighLevelClient esRestClient() {
       RestHighLevelClient client = new RestHighLevelClient(
               RestClient.builder(new HttpHost("localhost", 9200, "http")));
       return client;
  }
}

2、添加数据
https://www.elastic.co/guide/en/elasticsearch/client/java-rest/7.4/java-rest-high-document-index.html

@Autowired
private RestHighLevelClient restClient;
@Test
public void index() throws IOException {
   IndexRequest request = new IndexRequest("users");
   request.id("1");
   User user = new User();
   user.setAge(18);
   user.setName("wanter");
   user.setGender("男");
   String jsonString = JSON.toJSONString(user);
   request.source(jsonString, XContentType.JSON);
   //同步执行
   IndexResponse indexResponse = restClient.index(request, RequestOptions.DEFAULT);
   System.out.println(indexResponse);
}

输出：IndexResponse[index=users,type=_doc,id=1,version=1,result=created,seqNo=0,primaryTerm=1,shards={“total”:2,”successful”:1,”failed”:0}]

10、检索及解析

https://www.elastic.co/guide/en/elasticsearch/client/java-rest/7.4/java-rest-high-search.html
DSL:address匹配mill，对结果年龄分组，不同年龄组统计性别和平均薪资

DSL：

{
    "size": 10,
    "query": {
        "match": {
            "address": {
                "query": "mill",
                "operator": "OR",
                "prefix_length": 0,
                "max_expansions": 50,
                "fuzzy_transpositions": true,
                "lenient": false,
                "zero_terms_query": "NONE",
                "auto_generate_synonyms_phrase_query": true,
                "boost": 1
            }
        }
    },
    "aggregations": {
        "ageAgg": {
            "terms": {
                "field": "age",
                "size": 10,
                "min_doc_count": 1,
                "shard_min_doc_count": 0,
                "show_term_doc_count_error": false,
                "order": [
                    {
                        "count": "desc"
                    },
                    {
                        "key": "asc"
                    }
                ]
            },
            "aggregations": {
                "genderAgg": {
                    "terms": {
                        "field": "gender.keyword",
                        "size": 10,
                        "min_doc_count": 1,
                        "shard_min_doc_count": 0,
                        "show_term_doc_count_error": false,
                        "order": [
                            {
                                "count": "desc"
                            },
                            {
                                "key": "asc"
                            }
                        ]
                    }
                },
                "totalAgg": {
                    "avg": {
                        "field": "balance"
                    }
                }
            }
        }
    }
}

结果：

{
    "took": 1,
    "timed_out": false,
    "shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 4,
            "relation": "eq"
        },
        "max_score": 5.4032025,
        "hits": [
            {
                "index": "bank",
                "type": "doc",
                "id": "970",
                "score": 5.4032025,
                "source": {
                    "account_number": 970,
                    "balance": 19648,
                    "firstname": "Forbes",
                    "lastname": "Wallace",
                    "age": 28,
                    "gender": "M",
                    "address": "990 Mill Road",
                    "employer": "Pheast",
                    "email": "forbeswallace@pheast.com",
                    "city": "Lopezo",
                    "state": "AK"
                }
            },
            {
                "index": "bank",
                "type": "doc",
                "id": "136",
                "score": 5.4032025,
                "source": {
                    "account_number": 136,
                    "balance": 45801,
                    "firstname": "Winnie",
                    "lastname": "Holland",
                    "age": 38,
                    "gender": "M",
                    "address": "198 Mill Lane",
                    "employer": "Neteria",
                    "email": "winnieholland@neteria.com",
                    "city": "Urie",
                    "state": "IL"
                }
            },
            {
                "index": "bank",
                "type": "doc",
                "id": "345",
                "score": 5.4032025,
                "source": {
                    "account_number": 345,
                    "balance": 9812,
                    "firstname": "Parker",
                    "lastname": "Hines",
                    "age": 38,
                    "gender": "M",
                    "address": "715 Mill Avenue",
                    "employer": "Baluba",
                    "email": "parkerhines@baluba.com",
                    "city": "Blackgum",
                    "state": "KY"
                }
            },
            {
                "index": "bank",
                "type": "doc",
                "id": "472",
                "score": 5.4032025,
                "_source": {
                    "account_number": 472,
                    "balance": 25571,
                    "firstname": "Lee",
                    "lastname": "Long",
                    "age": 32,
                    "gender": "F",
                    "address": "288 Mill Street",
                    "employer": "Comverges",
                    "email": "leelong@comverges.com",
                    "city": "Movico",
                    "state": "MT"
                }
            }
        ]
    },
    "aggregations": {
        "lterms#ageAgg": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
                {
                    "key": 38,
                    "doc_count": 2,
                    "sterms#genderAgg": {
                        "doc_count_error_upper_bound": 0,
                        "sum_other_doc_count": 0,
                        "buckets": [
                            {
                                "key": "M",
                                "doc_count": 2
                            }
                        ]
                    },
                    "avg#totalAgg": {
                        "value": 27806.5
                    }
                },
                {
                    "key": 28,
                    "doc_count": 1,
                    "sterms#genderAgg": {
                        "doc_count_error_upper_bound": 0,
                        "sum_other_doc_count": 0,
                        "buckets": [
                            {
                                "key": "M",
                                "doc_count": 1
                            }
                        ]
                    },
                    "avg#totalAgg": {
                        "value": 19648
                    }
                },
                {
                    "key": 32,
                    "doc_count": 1,
                    "sterms#genderAgg": {
                        "doc_count_error_upper_bound": 0,
                        "sum_other_doc_count": 0,
                        "buckets": [
                            {
                                "key": "F",
                                "doc_count": 1
                            }
                        ]
                    },
                    "avg#totalAgg": {
                        "value": 25571
                    }
                }
            ]
        }
    }
}

@Autowired
private RestHighLevelClient restClient;
@Test
public void search() throws IOException {
   SearchRequest searchRequest = new SearchRequest();
   //设置索引
   searchRequest.indices("bank");
   SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
   //构造条件
   sourceBuilder.query(QueryBuilders.matchQuery("address","mill"));
   TermsAggregationBuilder ageAgg = AggregationBuilders.terms("ageAgg").field("age");
   ageAgg.subAggregation(AggregationBuilders.terms("genderAgg").field("gender.keyword"));
   ageAgg.subAggregation(AggregationBuilders.avg("totalAgg").field("balance"));
   sourceBuilder.aggregation(ageAgg);
   sourceBuilder.size(10);
   searchRequest.source(sourceBuilder);
   System.out.println(sourceBuilder.toString());
   //执行查询
   SearchResponse searchResponse = restClient.search(searchRequest, RequestOptions.DEFAULT);
   System.out.println(searchResponse.toString());
   //获取数据并映射到对象中解析
   SearchHits hits = searchResponse.getHits();
   SearchHit[] searchHits = hits.getHits();
   for (SearchHit searchHit : searchHits) {
       String index = searchHit.getIndex();
       String sourceString = searchHit.getSourceAsString();
       Account account = JSON.parseObject(sourceString, Account.class);
       System.out.println(account);
  }
   //获取统计信息解析
   Aggregations aggregations = searchResponse.getAggregations();
   Terms ageAgg1 = aggregations.get("ageAgg");
   for (Terms.Bucket bucket : ageAgg1.getBuckets()) {
       System.out.println("年龄:"+bucket.getKey()+"==>数量："+bucket.getDocCount());
       Aggregations aggregations1 = bucket.getAggregations();
       Terms genderAgg = aggregations1.get("genderAgg");
       for (Terms.Bucket genderAggBucket : genderAgg.getBuckets()) {
           System.out.println("性别:"+genderAggBucket.getKey()+"==>数量："+genderAggBucket.getDocCount());
      }
       Avg totalAgg = aggregations1.get("totalAgg");
       System.out.println("年龄段平均薪资："+totalAgg.getValue());
  }
}

Account(account_number=970, balance=19648, firstname=Forbes, lastname=Wallace, age=28, gender=M, address=990 Mill Road, employer=Pheast, email=forbeswallace@pheast.com, city=Lopezo, state=AK)
Account(account_number=136, balance=45801, firstname=Winnie, lastname=Holland, age=38, gender=M, address=198 Mill Lane, employer=Neteria, email=winnieholland@neteria.com, city=Urie, state=IL)
Account(account_number=345, balance=9812, firstname=Parker, lastname=Hines, age=38, gender=M, address=715 Mill Avenue, employer=Baluba, email=parkerhines@baluba.com, city=Blackgum, state=KY)
Account(account_number=472, balance=25571, firstname=Lee, lastname=Long, age=32, gender=F, address=288 Mill Street, employer=Comverges, email=leelong@comverges.com, city=Movico, state=MT)
年龄:38==>数量：2
性别:M==>数量：2
年龄段平均薪资：27806.5
年龄:28==>数量：1
性别:M==>数量：1
年龄段平均薪资：19648.0
年龄:32==>数量：1
性别:F==>数量：1
年龄段平均薪资：25571.0