11月10日ES本机

厨 神2025-11-15 13:15

准备

  • 下载 tar.gz
  • 解压
  • 配置 jdk
shell 复制代码 
vim .bash_profile
export ES_JAVA_HOME=/home/fox/elasticsearh-8.14.3/jdk
export ES_HOME=/xxx
source .bash_profile
  • 配置 ES 和 JVM 大小
shell 复制代码 
vi elasticsearch.yaml
# 	开启远程支持
network.host: 0.0.0.0
# d安节点，跳过引导检查
discovery.type: single-node
# 关闭安全认证
xpack.security.enabled: false
vi config/jvm.options
# 调整堆内存大小
-Xms4g
-Xmx4g
  • 启动 ES
shell 复制代码 
./bin/elasticsearch -d
  • 安装 ES 浏览器插件
    • Elasticsearch Head
    • Elasticsearch Tools
    • Elasticvue

使用

索引
shell 复制代码 
{
"analyzer":"ik_max_word",
"text":"道学先生凯迪拉克卷发；的激发了时代峰峻是大连房价阿杜里斯；反抗精神独立开发建设独立开发建设的拉开副驾驶的六块腹肌"
}

POST + /index/_mapping
{
"properties":{
  "content":{
    "type":"text",
    "analyzer":"ik_max_word",
    "search_analyzer": "ik_smart"
  }
}
}
插入文档 POST /my_index/_create/1 curl -XPOST xxxx:9200/index/_create/1 -H "Content-Type:application/json" -d'{"content":"xxxxxxx"}'
插入索引 PUT /my_index
插入文档 POST /my_index/_doc plain POST /my-index/_doc { "id": "park_rocky-mountain", "title": "Rocky Mountain", "description": "Bisected north to south by the Continental Divide, this portion of the Rockies has ecosystems varying from over 150 riparian lakes to montane and subalpine forests to treeless alpine tundra." }
根据索引搜索文档 GET /my_index/_search?q="rocky mountain" shell GET /my-index/_search?q="rocky mountain" { "query": {"match":{"content":"xx"}}, "highlight":{ "pre_tags": ["<tag1">,"<tag2>"], "post_tags":["</tag1>","</tag2>"], "fileds":{"content":{}} } }
kibana
  • 下载解压 tar.gz
  • 配置
shell 复制代码 
vim config/kibana.yml
# 端口号
serer.port: 5601
# 主机地址，默认监听本地网卡
server.host: "0.0.0.0"
elasticsearch.hosts: ["http://localhost:9200"]
# 默认英文en。改成zh-CN中文
i18n.locale: "zh-CN"
  • 启动
shell 复制代码 
nohup bin/kibana &
tail -f nohup.out
  • 在集成工具写代码
创建索引 查看索引 删除索引 修改索引(settings/mapping/aliases) PUT GET DELETE PUT /my_index /student_index /student_index /student_index/{_settings/_mapping} shell PUT /student_index { "settings": { "number_of_replicas": 1, "number_of_shards": 1 }, "mappings": { "properties": { "name":{ "type": "text" }, "age":{ "type": "integer" }, "enrolled_date":{ "type": "date" } } } } #修改索引 PUT /student_index/_settings { "index":{ "number_of_replicas":"2" } } PUT /student_index/_mapping { "properties":{ "grade":{ "type":"text" } } } #别名，可以直接通过别名访问 PUT /myindex { "aliases":{ "teacher_index":{} }, "settings": { "refresh_interval": "30s", "number_of_replicas": 0 , "number_of_shards": 1 } }
修改索引 创建 mapping 修改 settings POST /my_index/_mapping /student_index/_settings plain POST /index/_mapping { "properties":{ "content":{ "type":"text", "analyzer":"ik_max_word", "search_analyzer":"ik_smart" } } }
插入文档 POST (无需指定 ID) PUT（需要指定 ID） /index/_doc /index/_create/1 /index/_doc/1 plain POST /index/_create/1 {"content":"原神，你玩原神吗，这是米哈游的游戏"} POST /index/_create/2 {"content":"崩坏～星穹铁道，也是老米的"} POST /index/_create/3 {"content":"还有绝区零呢，也是米桑的"} POST /index/_create/4 {"content":"最近有一个星罗布谷，米哈游的"}
查找所有文档 GET /my_index/_search GET /index/_search shell GET /index/_search { "query": { "match_all": {} } }
根据文档条件查找 GET /my_index/_search {"query":{ "match":{"content":"xxx"}}} plain GET /index/_search { "query":{ "match":{ "content":"米" } } }
添加索引 POST shell POST /_aliases { "actions":[ {"add":{ "index":"myindex", "alias":"teacher_all_index" }}] }

别名

可以通过设置别名进行快速查找多个索引的数据，相当于物理索引的软链接，一个别名可以链接多个物理

shell 复制代码 
GET /teacher_index
# 添加别名
POST /_aliases
{
  "actions":[
    {"add":{
      "index":"myindex",
      "alias":"teacher_all_index"
    }}]
}
GET teacher_all_index


# 不使用别名：使用逗号分割、多个索引合起来查询
POST xxx,ttt,yyy,zzz/_search
# 不使用别名：使用通配符进行检索
POST xxx_*./_search
POST aaa*/_search

# 使用别名：别名关联已有索引
PUT xxx
PUT test1
PUT yyy
POST _aliases
{
  "actions": [
    {
      "add":{
      "index":"xxx",
      "alias":"tlmall_logs_2025"
    }},
    {
      "add": {
        "index": "test1",
        "alias": "tlmall_logs_2025"
      }
    },
      {
      "add":{
      "index":"yyy",
      "alias":"tlmall_logs_2025"
    }
    }
    
  ]
}
# 使用别名：使用别名进行检索
GET tlmall_logs_2025
POST tlmall_logs_2025/_search

文档

增加

shell 复制代码 
# 文档操作（增删改查 + 实践）
# POST增加，自动生成唯一ID，有幂等性问题
POST /xxx/_doc
{
  "hello":"world",
  "bye":"text"
}

POST /yyy/_doc
{
  "word":"abc",
  "key":"op"
}
# PUT增加，需要指定ID，ID不存在久新建，存在就替换
PUT /xxx/_doc/1
{
  "hello":"world"
}

PUT /yyy/_doc/1
{
  "word":"efg"
}

# 批量操作：index批量创建文档/替换文档，update：更新现有文档，delete：删除指定文档，create创建文档，已存在报错
POST /_bulk
{"index":{"_index":"xxx","_id":"4"}}
{"原神":"启动","mi崩铁":"启动"}
{"index":{"_index":"yyy","_id":"4"}}
{"原神":"启动","mi崩铁":"启动"}
{"index":{"_index":"test1","_id":"4"}}
{"原神":"启动","mi崩铁":"启动"}

更新

PUT更新整个文档内容，POST可以通过_update更新文档中的特定字段，不替换整个文档

shell 复制代码 
POST /employee/_update/2
{
  "doc":{
    "age":25
  }
}

查找

shell 复制代码 
GET /xxx/_doc/1

文档实战

shell 复制代码 
# 实战
# 1.创建索引并设置settings和mappings
PUT /employee
{
  "settings":
  {
    "number_of_replicas":1,
    "number_of_shards": 1
  },
  "mappings": {
    "properties": {
      "name":{
        "type": "keyword"
      },
      "sex":{
        "type":"integer"
      },
      "age":{
        "type": "integer"
      },
      "address":{
        "type":"text",
        "analyzer": "ik_max_word",
        "fields":{
          "keyword":{
            "type":"keyword"
          }
        }
      },
      "remark":{
        "type":"text",
        "analyzer": "ik_smart",
        "fields":{
          "keyword":{
          "type":"keyword"
          }
        }
      }
    }
  }
}
# 2.批量导入数据
POST /_bulk
{"index":{"_index":"employee","_id":1}}
{"name":"琴","sex":2,"age":24,"address":"蒙德地区","remark":"蒙德西风骑士团代理团长"}
{"index":{"_index":"employee","_id":2}}
{"name":"迪卢克","sex":1,"age":22,"address":"蒙德地区","remark":"晨曦酒庄主人"}
{"index":{"_index":"employee","_id":3}}
{"name":"可莉","sex":2,"age":10,"address":"蒙德地区","remark":"西风骑士团火花骑士"}
{"index":{"_index":"employee","_id":4}}
{"name":"钟离","sex":1,"age":10000,"address":"璃月地区","remark":"往生堂客卿"}
{"index":{"_index":"employee","_id":5}}
{"name":"甘雨","sex":2,"age":200,"address":"璃月地区","remark":"月海亭秘书"}
{"index":{"_index":"employee","_id":6}}
{"name":"胡桃","sex":2,"age":19,"address":"璃月地区","remark":"往生堂堂主"}
{"index":{"_index":"employee","_id":7}}
{"name":"雷电将军","sex":2,"age":1000,"address":"稻妻地区","remark":"稻妻幕府将军"}
{"index":{"_index":"employee","_id":8}}
{"name":"神里绫华","sex":2,"age":17,"address":"稻妻地区","remark":"神里家大小姐"}
{"index":{"_index":"employee","_id":9}}
{"name":"八重神子","sex":2,"age":500,"address":"稻妻地区","remark":"鸣神大社宫司"}
{"index":{"_index":"employee","_id":10}}
{"name":"纳西妲","sex":2,"age":500,"address":"须弥地区","remark":"须弥小吉祥草王"}

# 3.查询文档
GET /employee/_search
GET /employee/_doc/1
# 批量id查找
GET /employee/_mget
{
  "ids":[1,2,3,4,"5"]
}
# 文本字段匹配
GET /employee/_search
{
  "query":{
    "match": {
      "address":"蒙"
    }
  }
}
# 查看分词效果
POST _analyze
{
  "analyzer": "ik_max_word",
  "text":"蒙德地区"
}
# 精准匹配
GET /employee/_search
{
  "query":{
    "term":{
      "address":"地区"
    }
  }
}
# 范围匹配gte：greater/lte：little
GET /employee/_search
{
  "query":{
    "range":{
      "age":{
        "gte":18,
        "lte":30
      }
    }
  }
}
# 查找所有
GET /employee/_search
{
  "query":{
    "match_all": {}
  }
}

# 4. 删除文档
DELETE /employee/_doc/1
GET /employee/_doc/1
# 批量删除文档
POST _bulk
{"delete":{"_index":"employee","_id":3}}
{"delete":{"_index":"employee","_id":4}}
{"delete":{"_index":"employee","_id":5}}
POST /employee/_bulk
{"delete":{"_id":6}}
{"delete":{"_id":7}}
{"delete":{"_id":8}}
GET /employee/_doc/7
# 5. 更新文档 POST+_update是更新，不然就是替换，PUT为替换
POST /employee/_update/2
{
  "doc":{
    "age":25
  }
}
# 批量更新
POST /employee/_bulk
{"update":{"_id":1}}
{"doc":{"sex":0}}
{"update":{"_id":2}}
{"doc":{"sex":1}}
{"update":{"_id":3}}
{"doc":{"sex":0}}
{"update":{"_id":4}}
{"doc":{"sex":1}}
{"update":{"_id":5}}
{"doc":{"sex":0}}
{"update":{"_id":6}}
{"doc":{"sex":0}}
{"update":{"_id":7}}
{"doc":{"sex":0}}
{"update":{"_id":8}}
{"doc":{"sex":0}}
{"update":{"_id":9}}
{"doc":{"sex":0}}
{"update":{"_id":10}}
{"doc":{"sex":0}}
# 查找并更新
GET /employee/_search
{"query":{
    "match_all":{}
    }
}
}
POST /employee/_update_by_query
{
  "query":{
    "match_all":{}
  },
  "script": {
    "source": "ctx._source.remark=ctx._source.address+'-'+ctx._source.remark",
    "lang": "painless"
  }
}
# 更新保证线程安全：使用if_seq_no和if_primary_term进行判断
# 版本_seq_no,根据版本来同一时间一个用户进行更改-乐观锁
GET /employee/_doc/1
POST /employee/_doc/1?if_seq_no=167&if_primary_term=1
{
  "name":"琴1"
}
POST /employee/_doc/1?if_seq_no=167&if_primary_term=1
{
  "name":"琴2"
}
文档建模实践
shell 复制代码 
# 创建索引
PUT /product_info
{
    "settings": {
        "number_of_replicas": 1,
        "number_of_shards": 1
    },
    "mappings": {
        "properties": {
            "productName":{
                "type":"text",
                "analyzer": "ik_smart"
            },
            "annual_rate":{
                "type":"keyword"
            },
            "describe":{
                "type":"text",
                "analyzer": "ik_smart"
            }
        }
    }
}
# 导入数据
POST /_bulk
{"index":{"_index":"product_info","_id":1}}
{"productName":"蒙德苹果派","annual_rate":"25摩拉","describe":"用新鲜苹果和酥脆面皮制成，香甜而不腻，是蒙德城居民的经典甜点。"}
{"index":{"_index":"product_info","_id":2}}
{"productName":"璃月蜜酱叉烧","annual_rate":"50摩拉","describe":"选用肥瘦相间的猪肉，配上甜蜜蜜的酱汁慢烤，入口香而不油。"}
{"index":{"_index":"product_info","_id":3}}
{"productName":"稻妻海鳗寿司","annual_rate":"60摩拉","describe":"新鲜海鳗经过炙烧，配上醋饭与紫菜卷制而成，口感柔软，伴有海香。"}
{"index":{"_index":"product_info","_id":4}}
{"productName":"璃月清心豆腐汤","annual_rate":"40摩拉","describe":"豆腐与清心花同煮，汤色清澈、香气清雅，据说有助于宁神。"}
{"index":{"_index":"product_info","_id":5}}
{"productName":"蒙德南瓜浓汤","annual_rate":"35摩拉","describe":"香甜的南瓜与奶油调和成细腻汤汁，在寒冷的日子里格外受欢迎。"}
{"index":{"_index":"product_info","_id":6}}
{"productName":"须弥香料炖肉","annual_rate":"70摩拉","describe":"加了多种香料的炖肉，层次丰富的味道让人食欲大增。"}
{"index":{"_index":"product_info","_id":7}}
{"productName":"稻妻红豆团子","annual_rate":"20摩拉","describe":"细腻红豆包裹在糯米团内，外形小巧，甜而不腻。"}
{"index":{"_index":"product_info","_id":8}}
{"productName":"璃月脆皮鱼","annual_rate":"55摩拉","describe":"鲜鱼裹上薄面糊炸至金黄，脆香外皮与鲜嫩鱼肉融合。"}
{"index":{"_index":"product_info","_id":9}}
{"productName":"蒙德蜂蜜烤鸡翅","annual_rate":"45摩拉","describe":"烤鸡翅表面涂抹蜂蜜与香草汁，香气四溢。"}
{"index":{"_index":"product_info","_id":10}}
{"productName":"须弥花果沙拉","annual_rate":"30摩拉","describe":"混合多种热带水果与花瓣的清爽沙拉，伴有淡淡的花香。"}
# 全文检索
GET /product_info/_search
{
    "query": {
        "match": {
          "productName": "璃月蒙德沙拉"
        }
    }
}
GET /product_info/_search
{
    "query": {
        "range": {
          "annual_rate": {
            "gte": "10",
            "lte": "50"
          }
        }
    }
}
限制创建字段
shell 复制代码 
# 严格限制mapping：dynamic：strict
PUT /user
{
    "mappings":{
        "dynamic":"strict",
    
        "properties":{
            "name":{"type":"text"},
            "address":{"type":"object","dynamic":"true"}
        }
    }
}
# 避免空值：空值变0:null_value:0
# aggs取平均值：aggs-avg-avg

全文/精准匹配

match_all匹配所有文档、精确匹配、全文检索、组合查询、highlight高亮显示、地理空间位置查询、ES8向量检索

全文匹配 ：match_all

精准匹配：term/terms/range + exists/ids/prefix/wildcard/fuzzy/regexp/term set

全文检索: match分词查询\multi_match多字段查询\match_phrase短语查询\query_string与或非表达式查询\simple_query_string

全文检索需要分析，进行匹配：适用于评论、文章等非结构化文本数据

组合查询：布尔查询，按照布尔条件：按照max_score:搜索上下文must/should，filter:过滤上下文无需算分filter/must_not

term一般在filter过滤上下文

must：and算分，should：or多个条件算分，filter不算分，must_not不算分

shell 复制代码 
# 搜索：match_all匹配所有文档、**精确匹配、全文检索、组合查询**、highlight高亮显示、地理空间位置查询、ES8向量检索
# 相当于match_all
GET /blog/_search 

# 可以设置size，进行分页
GET /blog/_search
{
  "query": {
    "match_all": {}
  },
  "size":3
}
# 数据大的话会超过1万进行拆除
GET /blog/_search
{
  "query": {
    "match_all": {}
  },
  "from":3,
  "size":3
}
# 可以结合sort排序 sort:[{"":desc/asc}]
# _source返回指定字段

GET /blog/_search
{
  "query": {
    "match_all": {}
  },
  "sort": [
    {
      "time": "asc"
    }
  ],
  "from":2,
  "size":5,
  "_source":["time","content"]
}
GET /employee/_search
# 精确匹配：term/terms/range + exists/ids/prefix/wildcard/fuzzy/regexp/term set
# term单字段
# 可以多值（数组）匹配
# #      "address":{
 #       "type":"text",
 #       "analyzer": "ik_max_word",
 #       "fields":{
 #         "keyword":{
 #           "type":"keyword"
 #         }
 #       }
 #     },
 
GET /employee/_search
{
  "query":{
    "term":{
      "address.keyword":{
        "value":"蒙德地区"
      }
    }
  },
  "size":2
}
# (可以通过filter减少计算max_score，提高性能(将query转为filter)），max_score为1，精确值不需要算分
GET /employee/_search
{
  "query": {
    "constant_score":{
      "filter":{
        "term": {
          "address.keyword": "蒙德地区"
          
        }
      }
    }

  }
}
# terms多值匹配 query-terms-field_name- v1/v2/v3/v4
GET /employee/_search
GET /employee/_search
{
  "query": {
    "terms": {
      "address.keyword": [
        "蒙德地区",
        "璃月地区"
      ]
    }
  }
}

# range范围

GET /employee/_search
{
  "query": {
    "range": {
      "age": {
        "gte": 10,
        "lte": 200
      }
    }
  }
}
# 日期查询，1分片0副本，
PUT /notes
{
  "settings":{
    "number_of_shards": 1,
    "number_of_replicas": 0
  },
  "mappings":{
    "properties":{
      "title":{"type":"text"},
      "content":{"type":"text"},
      "created_at":{"type":"date","format":"yyyy-MM-dd HH:mm:ss"}
    }
  }
}
POST /_bulk
{"index":{"_index":"notes","_id":1}}
{"title":"2025原神年度更新展望","content":"预计2025年将开放全新大陆与剧情篇章，增加独特的环境机关和探索任务。","created_at":"2025-01-08 09:00:00"}
{"index":{"_index":"notes","_id":2}}
{"title":"新角色前瞻","content":"2025年可能会推出融合多元素的复合战斗角色，给战斗策略带来全新思路。","created_at":"2025-02-15 14:30:00"}
{"index":{"_index":"notes","_id":3}}
{"title":"周年庆猜想","content":"在2025年的周年庆，或将有跨国度的大型协作副本，以及全球玩家共同参与的活动。","created_at":"2025-03-20 12:00:00"}
{"index":{"_index":"notes","_id":4}}
{"title":"元素反应新机制","content":"官方可能会在2025年强化元素反应系统，增加更多独特的元素联动效果。","created_at":"2025-04-05 16:45:00"}
{"index":{"_index":"notes","_id":5}}
{"title":"新地图风景","content":"2025年新地图或将融合沙漠与海岛两种风格，为玩家带来截然不同的探索体验。","created_at":"2025-05-10 10:10:00"}
{"index":{"_index":"notes","_id":6}}
{"title":"多人模式升级","content":"在2025年多人合作模式可能会引入跨服匹配及更多团队战术副本。","created_at":"2025-06-01 18:20:00"}
{"index":{"_index":"notes","_id":7}}
{"title":"须弥支线补完","content":"预计会扩展须弥地区的支线任务，揭示部分角色未解的背景故事。","created_at":"2025-07-14 08:50:00"}
{"index":{"_index":"notes","_id":8}}
{"title":"新武器系统","content":"可能会引入灵活的武器属性调整机制，让装备搭配更加多样化。","created_at":"2025-08-25 21:40:00"}
{"index":{"_index":"notes","_id":9}}
{"title":"宠物互动玩法","content":"2025年或将开放宠物系统，玩家可以收集、培养并与宠物互动。","created_at":"2025-09-12 13:15:00"}
{"index":{"_index":"notes","_id":10}}
{"title":"交互界面优化","content":"版本更新可能会对UI布局进行优化，让冒险信息一目了然。","created_at":"2025-10-30 17:55:00"}

GET /notes/_search
{
  "query": {"range":{
    "created_at": {
      "gte": "2025-02-01 00:00:00",
      "lte": "2025-05-30 23:59:59"
    }
  }},
  "_source": ["title","content","created_at"]
  
}
# now当前，now-1d前一天，d天day，w星期week，M月Month，y年year，h小时hour ，+后，-前
GET /notes/_search
{
  "query": {"range":{
    "created_at": {
      "gte": "now-3M",
      "lte": "now"
    }
  }},
  "_source": ["title","content","created_at"]
}
# exists是否存在、ids一组id，
# exists：query-exists-field：是否存在的字段
GET /notes/_search
{
  "query": {
    "exists": {
      "field": "title"
    }
  }
}

# ids：根据一组id查询
GET /notes/_search
{
  "query": {
    "ids":{"values":[1,2,3,4]}
  }
}


# 模糊匹配：prefix前缀、wildcard通配符、fuzzy编辑距离、regexp正则、term set多值字段
# prefix不会分词，传入的前缀就是要找的前缀，max_score=1
GET /employee/_search
# keyword为空，因为不是德开头的
GET /employee/_search
{
  "query": {
    "prefix": {
      "address.keyword": {
        "value": "德"
      }
    }

  }
}
# 不为空，因为分词了
GET /employee/_search
{
  "query": {
    "prefix": {
      "address": {
        "value": "德"
      }
    }
  }
}
# wildcard 通配符匹配，*代表0或多个字符，?表示1个字符，任意单个字符，有较高计算负担，最好别用
GET /employee/_search
{
  "query": {
    "wildcard": {
      "remark.keyword": {
        "value": "*大*"
      }
    }
  }
}
# regexp正则，（.*）任意字符串
GET /employee/_search
{
  "query": {
    "regexp": {
      "remark.keyword": {
        "value": ".*大.*"
      }
    }
  }
}
# fuzzy编辑距离的模糊查询，可能输入错误字符串，hello，helle的fuzziness距离1，github，guthub的fuzziness距离2
# fuzziness为0/1/2/AUTO，prefix_length为前缀长度，默认0代表整个词
GET /employee/_search
{
  "query": {
    "fuzzy":{
      "address.keyword": {
        "value": "稻妻地区",
        "fuzziness": 2,
        "prefix_length": 0
      }
    }
  }
}
# term_set 多值，一个字段多个词,支持字段minimum_should_match：[匹配多少个number]、minimum_should_match_field和脚本minimum_should_match_script
DELETE /movies
PUT /movies
{
  "mappings": {
    "properties": {
      "title":{
        "type": "text"
      },
      "tags":{
        "type":"keyword"
      },
      "tags_count":{
        "type":"integer"
      }
    }
  }
}
POST /_bulk
{"index":{"_index":"movies","_id":1}}
{"title":"斗罗大陆","tags":["国漫","玄幻","热血"],"tags_count":3}
{"index":{"_index":"movies","_id":2}}
{"title":"一人之下","tags":["国漫","都市","超能力"],"tags_count":3}
{"index":{"_index":"movies","_id":3}}
{"title":"狐妖小红娘","tags":["国漫","爱情","奇幻"],"tags_count":3}
{"index":{"_index":"movies","_id":4}}
{"title":"火影忍者","tags":["日漫","热血","忍者"],"tags_count":3}
{"index":{"_index":"movies","_id":5}}
{"title":"海贼王","tags":["日漫","冒险","热血"],"tags_count":3}
{"index":{"_index":"movies","_id":6}}
{"title":"进击的巨人","tags":["日漫","奇幻","战斗"],"tags_count":3}
{"index":{"_index":"movies","_id":7}}
{"title":"鬼灭之刃","tags":["日漫","奇幻","战斗"],"tags_count":3}
{"index":{"_index":"movies","_id":8}}
{"title":"名侦探柯南","tags":["日漫","推理","悬疑"],"tags_count":3}
{"index":{"_index":"movies","_id":9}}
{"title":"镇魂街","tags":["国漫","奇幻","热血"],"tags_count":3}
{"index":{"_index":"movies","_id":10}}
{"title":"灌篮高手","tags":["日漫","体育","校园"],"tags_count":3}
{"index":{"_index":"movies","_id":10}}
{"title":"凡人修仙传","tags":["国漫","奇幻","战斗"],"tags_count":1}
# 使用match，直接指定值
GET /movies/_search
{
  "query": {
    "terms_set":{
      "tags":{
        "terms":["奇幻","国漫"],
        "minimum_should_match":2
      }
    }
  }
}
# 根据字段值的值匹配，可以动态化
GET /movies/_search
{
  "query": {
    "terms_set":{
      "tags":{
        "terms":["国漫","热血","奇幻"],
        "minimum_should_match_field": "tags_count"
      }
    }
  }
}
#根据脚本
GET /movies/_search
{
  "query": {
    "terms_set":{
      "tags":{
        "terms":["国漫","热血"],
        "minimum_should_match_script":{
          "source":"2"
        }
      }
    }
  }
}
# 脚本获取tag_count动态匹配
GET /movies/_search
{
  "query": {
    "terms_set":{
      "tags":{
        "terms":["国漫","热血","奇幻"],
        "minimum_should_match_script":{
          "source":"doc['tags_count'].value*0.7"
        }
      }
    }
  }
}


# 全文检索：match分词查询\multi_match多字段查询\match_phrase短语查询\query_string与或非表达式查询\simple_query_string
# 全文检索需要分析，进行匹配：适用于评论、文章等非结构化文本数据

# match：分词查询：分词-匹配计算-结果返回
GET /movies/_search
GET /movies/_search
{
  "query": {
    "match": {
      "title": "人修"
    }
  }
}
# multi_match多字段查询 ,默认是or
GET /movies/_search
{
  "query": {
    "multi_match": {
      "query": "凡人",
      "fields": ["tags","title"],
      "operator": "and"
    }
  }
}
GET /movies/_search
{
  "query": {
    "multi_match": {
      "query": "凡人",
      "fields": ["tags","title"],
      "operator": "or"
    }
  }
}
GET /employee/_search
GET /employee/_search
{
  "query": {
    "match": {
      "address": {
        "query":"蒙璃",
        "minimum_should_match":1
      }
    }
  }
}


GET /employee/_search
{
  "query": {
    "match": {
      "address": {
        "query": "蒙德璃月",
        "operator": "and"
      }
    }
  }
}

# match_phrase短语查询：顺序，不仅匹配整个短语，还考虑顺序和位置
POST _analyze
{
  "analyzer":"ik_max_word",
  "text":"蒙德地区-西风骑士团火花骑士"
}
GET /employee/_search
GET /employee/_search
{
  "query": {
    "match_phrase": {
      "remark":"蒙德地区-西风骑士"
    }
  }
}

# slop代表相隔多远视为匹配
GET /employee/_search
{
  "query": {
    "match_phrase": {
      "remark":{
      "query":"蒙德地区-西风骑士",
      "slop": 2
    }}
  }
}

GET /employee/_search
{
  "query": {
    "match_phrase": {
      "remark": "蒙德地区-西风骑士团"
    }
  }
}

# query_string用于高级搜索、数据分析、特定需求，要求支持与或非表达式的复杂查询任务,记得大写
# 多条件：+AND.  |OR.   -NOT
GET /employee/_search
{
  "query": {
    "query_string": {
      "default_field": "address",
      "query": "(蒙德 OR 稻妻) AND 地区"
    }
  }
}

# 单一条件
GET /employee/_search
{
  "query": {
    "query_string": {
      "default_field": "remark",
      "query": "璃月 AND 往生堂  "
    }
  }
}

GET /_analyze
{
  "analyzer":"ik_max_word",
  "text":"璃月地区-月海亭秘书"
}

# 多条件+多字段
GET /employee/_search
{
  "query": {
    "query_string": {
      "fields": ["remark","name"],
      "query": "(璃月 AND 钟离) OR 蒙德 "
    }
  }
}


# simple_query_string 简化的querystring
GET /employee/_search
{
  "query": {
    "simple_query_string": {
      "query": "璃月",
      "fields": ["name","address"],
      "default_operator": "AND"
    }
  }
}

GET /employee/_search
{
  "query": {
    "simple_query_string": {
      "query": "璃 + 月",
      "fields": ["name","address"]
    }
  }
}

GET /employee/_search
{
  "query": {
    "simple_query_string": {
      "query": "璃 + 月",
      "fields": ["name","address"]
    }
  }
}

# 组合查询：布尔查询，按照布尔条件：按照max_score:搜索上下文must/should，filter:过滤上下文无需算分filter/must_not
# term一般在filter过滤上下文
# must：and算分，should：or多个条件算分，filter不算分，must_not不算分
PUT /books
{
  "settings": {
    "number_of_replicas": 1,
    "number_of_shards": 1
  },
  "mappings": {
    "properties": {
      "id":{"type":"long"},
      "title":{"type":"text","analyzer":"ik_max_word"},
      "language":{"type":"keyword"},
      "author":{"type":"keyword"},
      "price":{"type":"double"},
      "publish_time":{"type":"date","format":"yyyy-MM-dd"},
      "description":{"type":"text","analyzer":"ik_max_word"}
    }
  }
}
POST /_bulk
{"index":{"_index":"books","_id":"1"}}
{"id":1,"title":"Java编程思想","language":"java","author":"Bruce Eckel","price":70.20,"publish_time":"2007-10-01","description":"Java学习必读经典，殿堂级著作！赢得了全球程序员的广泛赞誉。"}
{"index":{"_index":"books","_id":"2"}}
{"id":2,"title":"Java程序性能优化","language":"java","author":"葛一鸣","price":46.5,"publish_time":"2012-08-01","description":"让你的Java程序更快、更稳定。"}
{"index":{"_index":"books","_id":"3"}}
{"id":3,"title":"深入理解计算机系统","language":"c","author":"Randal E.Bryant","price":85.00,"publish_time":"2016-05-10","description":"系统性介绍计算机内部工作原理。"}
{"index":{"_index":"books","_id":"4"}}
{"id":4,"title":"Effective Java","language":"java","author":"Joshua Bloch","price":66.00,"publish_time":"2018-01-01","description":"Java开发最佳实践集合。"}
{"index":{"_index":"books","_id":"5"}}
{"id":5,"title":"Python数据分析","language":"python","author":"Wes McKinney","price":59.00,"publish_time":"2019-03-15","description":"基于Pandas和NumPy的数据分析指南。"}
GET /books/_search
GET /books/_search
{
  "query":{
    "bool":{
      "must":[
        {"match":{"title":"Java"}},
        {"match":{"description":"Java"}}
      ]
    }
  }
}
GET /books/_search
{
  "query":{
    "bool":{
      "should":[
        {"match":{"title":"Java"}},
        {"match":{"description":"Java"}}
      ]
    }
  }
}
GET /books/_search
{
  "query":{
    "bool":{
      "filter":[
        {"term":{"language":"java"}},
        {"range":{"price":{
          "lte":100
        }}}
      ]
    }
  }

全文检索

全文检索会进行算分，filter 不会算分，此类算分的检索，用于非结构文本数据，比如文章和评论等。

  • match 分词匹配，只能查找一个字段,可以使用 opertor 设置 AND /OR(在字段名后加上 query 和 operator), / minimum_should_match 设置匹配至少多少个
  • multi_match 多字段查询，字段太多性能下降，方便一次查多个字段
  • match_phrase 短语查询，精确匹配词组，查找范围严格，需要按照顺序，可以用 slop,在字段后加上 kv：slop 和 query
  • query_string 与或非表达式，功能最强，单语法错误会报错，记得大写 AND OR NOT
  • simple_query_string 功能类似 <font style="color:rgb(13, 18, 57);">query_string</font>，但不会因为语法错误而直接报错，非常适合用户输入不可控的场景（<font style="color:rgb(13, 18, 57);">+</font> 必须包含<font style="color:rgb(13, 18, 57);">-</font> 排除）
shell 复制代码 
GET _search
{
  "query": {
    "match_all": {}
  }
}
PUT /blog
{
    "mappings": {
        "properties": {
            "content":{
                "type": "text"
            },
            "time":{
                "type": "date"
            },
            "user":{
                "properties": {
                    "city":{
                        "type":"text"
                    },
                    "userid":{
                        "type":"long"
                    },
                    "username":{
                        "type": "keyword"
                    }
                }
            }
        }
    }
}
POST /products/_bulk
{"index":{"_index":"products","_id":1}}
{"name":"原神主题抱枕","description":"印有原神角色图案的舒适抱枕"}
{"index":{"_index":"products","_id":2}}
{"name":"原神桌面鼠标垫","description":"适合游戏玩家的防滑鼠标垫"}
{"index":{"_index":"products","_id":3}}
{"name":"明日方舟徽章","description":"印有明日方舟角色图案的金属徽章"}
{"index":{"_index":"products","_id":4}}
{"name":"原神周边茶杯","description":"原神主题的陶瓷茶杯，礼盒包装"}

GET /products/_search
{
  "query": {
    "match": {
      "description": "原神"
    }
  }
}
GET /products/_search
{
  "query": {
    "match": {
      "description":{
        "operator": "and",
        "query":"原神抱枕"
      }
    }
  }
}
GET /products/_search
{
  "query": {
    "match": {
      "description":{
        "query":"原神抱枕",
        "minimum_should_match": 1
      }
    }
  }
}
GET /products/_search
{
  "query": {
    "match": {
      "description":{
        "operator": "or",
        "query":"原神抱枕"
        
      }
    }
  }
}
GET /products/_search
{
  "query": {
    "multi_match": {
      "query": "原神",
      "fields": ["name", "description"]
    }
  }
}
GET /_analyze
{
  "analyzer": "ik_smart",
  "text":"原神主题的陶瓷茶杯，礼盒包装"
}

GET /products/_search
{
  "query": {
    "match_phrase": {
      "description": "原神的主题"
    }
  }
}

GET /products/_search
{
  "query": {
    "match_phrase": {
      "description":{
        "query": "原神的主题",
        "slop": 4
      }
    }
  }
}
GET /products/_search
{
  "query": {
    "query_string": {
      "query": "原神 AND 周边 OR 抱枕 NOT 鼠标垫"
    }
  }
}
GET /products/_search
{
  "query": {
    "query_string": {
      "query": "原神 AND 周边 OR 抱枕 NOT 鼠标垫",
      "default_field": "name"
    }
  }
}
GET /products/_search
{
  "query": {
    "query_string": {
      "query": "原神 AND 周边 OR 抱枕 NOT 鼠标垫",
      "fields": ["name", "description"]
    }
  }
}
GET /products/_search
{
  "query": {
    "simple_query_string": {
      "query": "原神 +周边 -鼠标垫",
      "fields": ["name", "description"],
      "default_operator": "and"
    }
  }
}

组合查询

  • 布尔查询

搜索上下文（算分）和过滤上下文（不算分）

算： must：均满足/should：一个满足，shoul 可以结合minimum_should_match 加上可以匹配最少多少个，默认 1

不算： filter：过滤均满足/must_not：均不满足

query-bool-must/should/filter/must_not---match/multi_match/range/term

shell 复制代码 
# 组合查询
PUT /genshin_posts
{
  "settings": {
    "number_of_shards": 1,
    "number_of_replicas": 0,
    "analysis": {
      "analyzer": {
        "my_ik": {
          "type": "ik_max_word"
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "title": { "type": "text", "analyzer": "my_ik" },
      "content": { "type": "text", "analyzer": "my_ik" },
      "region": { "type": "keyword" }, 
      "element": { "type": "keyword" },
      "created_at": { "type": "date", "format": "yyyy-MM-dd" }
    }
  }
}
POST /_bulk
{"index":{"_index":"genshin_posts","_id":1}}
{"title":"钟离护盾体验","content":"测试钟离的岩元素护盾，非常适合探索和打世界BOSS","region":"璃月","element":"岩","created_at":"2024-07-10"}
{"index":{"_index":"genshin_posts","_id":2}}
{"title":"胡桃与夜兰配队","content":"胡桃的高爆发配合夜兰，打精英怪效果拔群","region":"璃月","element":"火","created_at":"2024-07-11"}
{"index":{"_index":"genshin_posts","_id":3}}
{"title":"雷电将军BOSS战","content":"挑战雷电将军的周本，需要灵活闪避和及时输出","region":"稻妻","element":"雷","created_at":"2024-05-18"}
{"index":{"_index":"genshin_posts","_id":4}}
{"title":"纳西妲探索草原","content":"草元素的纳西妲在须弥探索效率极高","region":"须弥","element":"草","created_at":"2024-04-03"}
{"index":{"_index":"genshin_posts","_id":5}}
{"title":"神里绫华冰冻控制","content":"冰元素绫华配合申鹤，深渊控制能力很强","region":"稻妻","element":"冰","created_at":"2024-06-22"}
POST /_bulk
{"index":{"_index":"genshin_posts","_id":6}}
{"title":"温迪风场探索技巧","content":"原神中温迪的风元素技能可短时间制造上升气流，帮助快速登高与探索秘境","region":"蒙德","element":"风","created_at":"2024-03-12"}
{"index":{"_index":"genshin_posts","_id":7}}
{"title":"魈跳跃攻击","content":"魈的风元素跃击在原神世界探索中既炫酷又实用","region":"璃月","element":"风","created_at":"2024-02-20"}
{"index":{"_index":"genshin_posts","_id":8}}
{"title":"刻晴雷斩测试","content":"刻晴的雷元素瞬移与斩击能快速切入战斗","region":"璃月","element":"雷","created_at":"2024-01-15"}
{"index":{"_index":"genshin_posts","_id":9}}
{"title":"雷泽野外战斗","content":"雷泽的雷元素技能搭配狼魂，适合原神野外清怪","region":"蒙德","element":"雷","created_at":"2024-07-01"}
{"index":{"_index":"genshin_posts","_id":10}}
{"title":"香菱火锅派对","content":"香菱的火元素技能配合锅巴，既能战斗也能烹饪","region":"璃月","element":"火","created_at":"2024-06-05"}
{"index":{"_index":"genshin_posts","_id":11}}
{"title":"安柏火箭射术","content":"安柏的火元素箭雨在原神任务中清理敌群效果不错","region":"蒙德","element":"火","created_at":"2024-05-11"}
{"index":{"_index":"genshin_posts","_id":12}}
{"title":"重云冰刀测试","content":"重云的冰元素领域在深渊环境下非常稳定","region":"璃月","element":"冰","created_at":"2024-04-21"}
{"index":{"_index":"genshin_posts","_id":13}}
{"title":"七七治疗能力","content":"七七的冰元素治疗可在原神战场快速为队友补血","region":"璃月","element":"冰","created_at":"2024-07-02"}
{"index":{"_index":"genshin_posts","_id":14}}
{"title":"行秋雨剑","content":"行秋的水元素技能让战斗更安全，原神玩家必练","region":"璃月","element":"水","created_at":"2024-03-10"}
{"index":{"_index":"genshin_posts","_id":15}}
{"title":"珊瑚宫心海治疗实验","content":"心海的水元素治疗配合冰元素角色可轻松控场","region":"稻妻","element":"水","created_at":"2024-06-06"}
{"index":{"_index":"genshin_posts","_id":16}}
{"title":"九条裟罗雷羽","content":"九条裟罗的雷元素增伤技能非常适合爆发队伍","region":"稻妻","element":"雷","created_at":"2024-05-23"}
{"index":{"_index":"genshin_posts","_id":17}}
{"title":"宵宫焰火","content":"宵宫的火元素技能在原神夜景下格外绚丽","region":"稻妻","element":"火","created_at":"2024-08-12"}
{"index":{"_index":"genshin_posts","_id":18}}
{"title":"早柚风滚草","content":"早柚的风元素翻滚可以快速移动与探索","region":"稻妻","element":"风","created_at":"2024-07-19"}
{"index":{"_index":"genshin_posts","_id":19}}
{"title":"鹿野院平藏格斗技巧","content":"平藏的风元素与近战技能在原神战斗中很独特","region":"稻妻","element":"风","created_at":"2024-06-26"}
{"index":{"_index":"genshin_posts","_id":20}}
{"title":"提纳里射击测试","content":"提纳里的草元素蓄力箭在须弥探险高效","region":"须弥","element":"草","created_at":"2024-05-09"}
{"index":{"_index":"genshin_posts","_id":21}}
{"title":"柯莱投掷攻势","content":"柯莱的草元素飞叶技能适合中距离攻击","region":"须弥","element":"草","created_at":"2024-04-28"}
{"index":{"_index":"genshin_posts","_id":22}}
{"title":"艾尔海森双剑测试","content":"艾尔海森的草元素双技能在原神中兼顾爆发与持续","region":"须弥","element":"草","created_at":"2024-02-14"}
{"index":{"_index":"genshin_posts","_id":23}}
{"title":"白术治疗实验","content":"白术的草元素治疗可配合护盾提高生存率","region":"璃月","element":"草","created_at":"2024-07-04"}
{"index":{"_index":"genshin_posts","_id":24}}
{"title":"申鹤冰增伤研究","content":"申鹤的冰元素增伤让冰队伤害倍增","region":"璃月","element":"冰","created_at":"2024-06-15"}
{"index":{"_index":"genshin_posts","_id":25}}
{"title":"迪卢克大剑爆发","content":"迪卢克的火元素爆发技能适合快速击杀BOSS","region":"蒙德","element":"火","created_at":"2024-05-30"}

GET /genshin_posts/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "content": "原神" } },
        { "match": { "content": "护盾" } }
      ]
    }
  }
}

GET /genshin_posts/_search
{
  "query": {
    "bool": {
      "should": [
        { "match": { "content": "冰冻" } },
        { "match": { "content": "控制" } }
      ],
      "minimum_should_match": 1
    }
  }
}
GET /genshin_posts/_search
{
  "query": {
    "bool": {
      "must": { "match": { "content": "队伍" } },
      "filter": [
        { "term": { "region": "稻妻" } },
        { "term": { "element": "雷" } }
      ]
    }
  }
}
GET /genshin_posts/_search
{
  "query": {
    "bool": {
      "must": { "match": { "content": "原神" } },
      "must_not": [
        { "term": { "element": "冰" } }
      ]
    }
  }
}

高亮

highlight 关键字可以让符合条件的关键词高亮：

ES 高亮默认只高亮 query 匹配的字段，

  • pre_rags：前缀:[""][""][""]
  • post_tags：后缀:[""][""][""]
  • tags_schema：设置 style
  • require_field_match：多字段需要 设置为 false，true 默认代表搜索 query 字段高亮，false，没有 query 只要匹配就高亮， multi_match 或跨字段搜索时用，当你使用 multi_match 或跨字段搜索时，建议 <font style="color:rgb(13, 18, 57);">require_field_match: false</font>，这样所有包含关键词的字段都会高亮，方便前端一次性渲染。如果你只想高亮用户指定查询的字段，保留 <font style="color:rgb(13, 18, 57);">true</font>。
    • <font style="color:rgb(13, 18, 57);">true</font>：只高亮正在搜索的字段（更精准），只有 query+fields 里面的会高亮，*代表 query 里面的字段
    • <font style="color:rgb(13, 18, 57);">false</font>：所有字段只要包含匹配词，就高亮（更宽松）,fields 里面的都会被高亮，*所有字段
  • highlight-filelds：*代表前面的 query，可以指定 k(字段名)v（{}）
shell 复制代码 
# 高亮
GET /genshin_posts/_search
{
  "query": {
    "bool": {
      "must": { "match": { "content": "原神" } },
      "must_not": [
        { "term": { "element": "冰" } }
      ]
    }
  },
  "highlight": {
    "fields": {
      "*":{}
    },
    "require_field_match": "true"
  }
}

GET /genshin_posts/_search
{
  "query": {
    "bool": {
      "must": [
        { "match_phrase": { "content": "夜景" } }
      ],
      "filter": [
        { "match_phrase": { "region": "稻妻" } }
      ]
    }
  },
  "highlight": {
    "pre_tags": ["<em style='color:red'>"],
    "post_tags": ["</em>"],
    "fields": {
      "title": {},
      "content": {}
    },
    "require_field_match": "false"
  }
}
GET /genshin_posts/_search
{
  "query": {
    "bool": {
      "should": [
        { "match": { "content": "爆发" } },
        { "match": { "content": "控制" } }
      ],
      "minimum_should_match": 1
    }
  },
  "highlight": {
    "pre_tags": ["<mark>"],
    "post_tags": ["</mark>"],
    "fields": {
      "title": {},
      "content": {}
    }
  }
}
GET /genshin_posts/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "content": "原神" } }
      ],
      "must_not": [
        { "term": { "element": "冰" } }
      ]
    }
  },
  "highlight": {
    "pre_tags": ["<strong style='color:blue'>"],
    "post_tags": ["</strong>"],
    "fields": {
      "title": {},
      "content": {}
    }
  }
}
GET /genshin_posts/_search
{
  "query": {
    "match": {
      "content": "火"
    }
  },
  "highlight": {
    "require_field_match": false,
    "fields": {
      "*": {}
      
    }
  }
}
GET /genshin_posts/_search
{
  "query": {
    "match": {
      "content": "火"
    }
  },
  "highlight": {
    "require_field_match": true,
    "fields": {
      "*": {}
      
    }
  }
}

地理空间位置

  • geo_point 支持 <font style="color:rgb(13, 18, 57);">geo_distance</font>（圆范围）、<font style="color:rgb(13, 18, 57);">geo_bounding_box</font>（矩形）、<font style="color:rgb(13, 18, 57);">geo_polygon</font>（多边形）等查询。
  • 场景：附近 NPC/商店、怪物刷新点、交通点位等。
  • 如果是地图可视化，可以输出到 kibana map 或前端地图组件。

geo_distance 是一个地理距离查询，允许指定一个距离和一个点的坐标

distance 时查询最大距离，单位 m 或 km

distance_type ：arc 弧长/plane 直线距离为单位，建议 arc

location：参考点，包含经度纬度足疗
圆形范围：query - bool - filter - geo_distance - distance(value)/location(lat/lon) 位置

距离排序：query/match_all. + sort - [_geo_distance - location(lat/lon) / order(asc) / unit(km) / distance_type(arc)]

矩形：query - geo_bounding_box - location - top_left(lat/lon) / bottom_right(lat/lon)

多边形：query - geo_polygon - location - points -{lat/lon}/{lat/lon}/{lat/lon}

shell 复制代码 
# 地理空间位置查询 mappings - properties - location - type:"geo_point"
PUT /genshin_locations
{
  "settings": {
    "number_of_shards": 1,
    "number_of_replicas": 0
  },
  "mappings": {
    "properties": {
      "name": { "type": "text" },
      "region": { "type": "keyword" },
      "element": { "type": "keyword" },
      "location": { "type": "geo_point" }
    }
  }
}

POST /_bulk
{"index":{"_index":"genshin_locations","_id":1}}
{"name":"蒙德城广场","region":"蒙德","element":"风","location":{"lat":48.1351,"lon":11.5820}}
{"index":{"_index":"genshin_locations","_id":2}}
{"name":"璃月港口","region":"璃月","element":"岩","location":{"lat":29.5539,"lon":106.5678}}
{"index":{"_index":"genshin_locations","_id":3}}
{"name":"稻妻城中心","region":"稻妻","element":"雷","location":{"lat":35.6528,"lon":139.8395}}
{"index":{"_index":"genshin_locations","_id":4}}
{"name":"须弥雨林遗迹","region":"须弥","element":"草","location":{"lat":4.2105,"lon":101.9758}}
{"index":{"_index":"genshin_locations","_id":5}}
{"name":"雪山龙脊","region":"蒙德","element":"冰","location":{"lat":30.6586,"lon":104.0648}}

GET /genshin_locations/_search
{
  "query": {
    "bool": {
      "filter": {
        "geo_distance": {
          "distance": "300km",
          "location": {
            "lat": 30.657,
            "lon": 104.066
          }
        }
      }
    }
  }
}

GET /genshin_locations/_search
{
  "query": {
    "match_all": {}
  },
  "sort": [
    {
      "_geo_distance": {
        "location": {
          "lat": 30.657,
          "lon": 104.066
        },
        "order": "asc",
        "unit": "km",
        "distance_type": "arc"
      }
    }
  ]
}


GET /genshin_locations/_search
{
  "query": {
    "geo_bounding_box": {
      "location": {
        "top_left": {
          "lat": 50,
          "lon": 105
        },
        "bottom_right": {
          "lat": 25,
          "lon": 110
        }
      }
    }
  }
}
GET /genshin_locations/_search
{
  "query": {
    "geo_polygon": {
      "location": {
        "points": [
          { "lat": 30, "lon": 103 },
          { "lat": 31, "lon": 105 },
          { "lat": 29, "lon": 106 }
        ]
      }
    }
  }
}

向量检索

比如通过 KNN 算法支持向量近邻检索（用于机器学习、数据分析、推荐系统等领域）

基本思路是，将文档表示为高维向量，并使用这些向量执行相似性搜索，在 ES 中，这些向量被存储在 dense_vector 类型的字段中，使用 KNN 算法来找到与给定向量最相似的其它向量

shell 复制代码 
#dims: 向量维度（这里为了演示设 4，真实 NLP 模型一般是 384、768、1536）。
#index: true: 启用向量索引。
# similarity: 相似度算法（cosine / l2_norm / dot_product）
DELETE /genshin_vectors
PUT /genshin_vectors
{
  "settings": {
    "number_of_shards": 1,
    "number_of_replicas": 0
  },
  "mappings": {
    "properties": {
      "title": { "type": "text" },
      "content": { "type": "text" },
      "region": { "type": "keyword" },
      "element": { "type": "keyword" },
      "vector": {
        "type": "dense_vector",
        "dims": 4,
        "index": true,
        "similarity": "cosine"  
      }
    }
  }
}

POST /_bulk
{"index":{"_index":"genshin_vectors","_id":1}}
{"title":"钟离的护盾","content":"钟离的岩元素护盾抗击打能力超强","region":"璃月","element":"岩","vector":[0.12,0.85,0.33,0.20]}
{"index":{"_index":"genshin_vectors","_id":2}}
{"title":"胡桃的爆发","content":"胡桃的火元素高爆发适合快速击杀","region":"璃月","element":"火","vector":[0.88,0.11,0.22,0.31]}
{"index":{"_index":"genshin_vectors","_id":3}}
{"title":"雷电将军的攻击节奏","content":"雷电将军的雷元素攻击节奏稳定，适合长时间输出","region":"稻妻","element":"雷","vector":[0.34,0.67,0.12,0.33]}
{"index":{"_index":"genshin_vectors","_id":4}}
{"title":"神里绫华冰冻控制","content":"绫华的冰元素配合申鹤，形成强冰冻控制","region":"稻妻","element":"冰","vector":[0.20,0.33,0.85,0.18]}





POST /genshin_vectors/_knn_search
{
  "knn": {
    "field": "vector",
    "query_vector": [0.10,0.80,0.30,0.25],
    "k": 2,
    "num_candidates": 4
  },
  "_source": ["title","content","region","element"]
}



GET /genshin_vectors/_search
{
  "query": {
    "script_score": {
      "query": { "match_all": {} },
      "script": {
        "source": "cosineSimilarity(params.query_vector, 'vector') + 1.0",
        "params": {
          "query_vector": [0.10,0.80,0.30,0.25]
        }
      }
    }
  },
  "_source": ["title","content","region","element"]
}

权重

使用 "fields": ["title^2", "content"] 或者{"match":{"title":{"query":"原神","boost":3}}} 可以增加权重

shell 复制代码 
GET /csdn_blogs/_search
{
  "query": {
    "multi_match": {
      "query": "原神",
      "fields": ["title^2", "content"]
    }
  },
  "highlight": {
    "fields": {
      "*": {}
    },
    "require_field_match": false
  }
}
{ "match": { "title": { "query": "原神", "boost": 3 } } }

csdn 搜索实战

shell 复制代码 
# 实战-csdn的搜索
# 1.创建索引，文章和标题一定要分词，为text
PUT csdn_blogs
{
  "settings": {
    "number_of_replicas": 1,
    "number_of_shards": 1
  },
  "mappings": {
    "properties": {
      "title":{
        "type": "text",
        "analyzer": "ik_max_word",
        "search_analyzer": "ik_smart"
      },
      "content":{
        "type": "text",
        "analyzer": "ik_max_word",
        "search_analyzer": "ik_smart"
      },
      "author":{
        "type": "keyword"
      },
      "tags":{
        "type":"keyword"
      },
      "date":{
        "type":"date",
        "format": "yyyy-MM-dd HH:mm:ss"
      }
    }
  }
}


POST /_bulk
{"index":{"_index":"csdn_blogs","_id":1}}
{"title":"Elasticsearch 高亮显示原神关键词实践","content":"本文详细讲解如何在Elasticsearch中使用IK分词器对原神相关内容进行搜索和高亮显示，同时对搜索上下文和过滤上下文做了对比。","author":"gamer101","tags":["Elasticsearch","原神","搜索"],"date":"2024-07-15 10:12:00"}
{"index":{"_index":"csdn_blogs","_id":2}}
{"title":"原神角色数据批量导入到Elasticsearch","content":"通过Bulk API将原神角色信息批量导入ES索引，并结合must、should等查询条件实现灵活检索。","author":"dev_cat","tags":["Elasticsearch","原神","BulkAPI"],"date":"2024-07-14 09:30:00"}
{"index":{"_index":"csdn_blogs","_id":3}}
{"title":"向量搜索结合原神语义匹配实例","content":"利用dense_vector存储原神角色描述的向量，并通过KNN实现语义搜索，找到最相似的攻略帖子。","author":"data_master","tags":["Elasticsearch","原神","向量搜索"],"date":"2024-07-13 14:20:00"}
{"index":{"_index":"csdn_blogs","_id":4}}
{"title":"Elasticsearch过滤上下文性能优化","content":"介绍如何在原神攻略搜索中利用filter上下文提高性能，例如按地区或元素进行快速过滤。","author":"tech_pro","tags":["Elasticsearch","过滤上下文","性能优化"],"date":"2024-07-12 20:10:00"}
{"index":{"_index":"csdn_blogs","_id":5}}
{"title":"原神地理位置索引与附近地点搜索","content":"通过geo_point类型存储原神地图上的坐标信息，实现附近地点搜索功能。","author":"map_lover","tags":["Elasticsearch","原神","地理位置"],"date":"2024-07-12 12:00:00"}
{"index":{"_index":"csdn_blogs","_id":6}}
{"title":"Elasticsearch 多字段搜索实战","content":"利用multi_match查询实现原神相关数据在标题和正文的双字段搜索，提升检索覆盖率。","author":"search_guru","tags":["Elasticsearch","multi_match","原神"],"date":"2024-07-11 11:45:00"}
{"index":{"_index":"csdn_blogs","_id":7}}
{"title":"Query String 查询原神攻略","content":"用query_string实现原神攻略的复杂与或非查询，满足进阶搜索需求。","author":"syntax_maniac","tags":["Elasticsearch","原神","query_string"],"date":"2024-07-10 10:10:00"}
{"index":{"_index":"csdn_blogs","_id":8}}
{"title":"原神短语搜索与高亮","content":"通过match_phrase实现原神短语的精准匹配，并对匹配词进行高亮展示。","author":"phrase_hunter","tags":["Elasticsearch","原神","match_phrase"],"date":"2024-07-09 22:50:00"}
{"index":{"_index":"csdn_blogs","_id":9}}
{"title":"原神周边商品搜索","content":"结合Elasticsearch keyword字段查询原神周边商品标签，实现精确匹配与分类统计。","author":"market_dev","tags":["Elasticsearch","原神","电商"],"date":"2024-07-09 09:20:00"}
{"index":{"_index":"csdn_blogs","_id":10}}
{"title":"原神跨字段高亮与搜索结合","content":"同时在标题和内容字段高亮原神关键词，让搜索结果更醒目。","author":"frontend_magic","tags":["Elasticsearch","原神","高亮"],"date":"2024-07-08 16:40:00"}

GET /csdn_blogs/_search

GET /csdn_blogs/_search
{
  "query": {
    "multi_match": {
      "query": "原神",
      "fields": ["title","content^5"]
    }
  },
  "highlight": {
    "fields": {
      "*":{}
    },
    "require_field_match": "false"
    
  }
}

GET /csdn_blogs/_search
{
  "query": {
    "bool": {
      "must": [
        {"multi_match": {
          "query": "原神",
          "fields": ["tiele","content"]
        }},
        {
          "range": {
            "date": {
              "gte": "2024-07-12 10:12:00",
              "lte": "2024-07-16 10:12:00"
            }
          }
        },
        {
          "term": {
            "tags": {
              "value": "Elasticsearch"
            }
          }
        }
      ]
    }
  }
}

相关性

es5 之前是使用 TF-IDF 实现，5 之后，使用改进的 Okapi BM25

TF-IDF：

  • 分词词频越高相关性越高
  • 逆向频率检索词在索引中频率越低相关性越高
  • 检索次在内容越短的字段中相关性越高

BM25:

  • TF无限增加跨度太大，BM25算分会趋于一个数值（归一值）

BM25:TF无限增加跨度太大，BM25算分会趋于一个数值（归一值）

shell 复制代码 
GET /csdn_blogs/_search
{
  "query": {"match": {
    "title": "原神"
  }},
  "explain": true
}

GET /csdn_blogs/_explain/2
{
  "query":{
    "match":{
      "title":"原神"
    }
  }
}

自定义评分

indices_boost 索引级加权 跨索引搜索时给某个索引整体加权。

query - boosting - positive/negative 文档提高/降低 同时包含正向条件和负向条件，负向只降分不排除。

negative_boost 文档下降 Boosting Query的参数，用来降低负向文档分数。

query - function_score/boost_mode /functions - filter/weight 修改计算公式 _score 用函数和条件自定义分数逻辑，非常灵活。

rescore - window_size/query_weight/rescore_query_weight/(query - rescore_query - match_phrase - k:v) 二次打分 结果集先查出来，再精细匹配重新打分。

  • Index Boost：跨索引搜索时给某个索引整体加权。
  • Boosting Query：同时包含正向条件和负向条件，负向只降分不排除。
  • negative_boost：Boosting Query的参数，用来降低负向文档分数。
  • Function Score：用函数和条件自定义分数逻辑，非常灵活。
  • Rescore：结果集先查出来，再精细匹配重新打分。

  • ES自定义评分：修改评分修改文档相关性

Index Boost在索引层面修改相关性、boosting修改文档相关性、negative_boost降低相关性、function_score自定义评分、rescore_query查询后二次打分

索引层面

shell 复制代码 
# ES自定义评分：修改评分修改文档相关性
# Index Boost在索引层面修改相关性、boosting修改文档相关性、negative_boost降低相关性、function_score自定义评分、rescore_query查询后二次打分

PUT /my_index100
{
  "mappings": { 
  "properties":{
    "subject":{
      "type":"keyword"
    }
  }}
}

PUT /my_index101
{
  "mappings": { 
  "properties":{
    "subject":{
      "type":"keyword"
    }
  }}
}


PUT /my_index102
{
  "mappings": { 
  "properties":{
    "subject":{
      "type":"keyword"
    }
  }}
}
POST /_bulk
{"index":{"_index":"my_index100","_id":1}}
{"subject":"subject 1"}
{"index":{"_index":"my_index101","_id":1}}
{"subject":"subject 1"}
{"index":{"_index":"my_index102","_id":1}}
{"subject":"subject 1"}

GET /my_index*/_search
{
  "query": {
    "term": {
      "subject": {
        "value": "subject 1"
      }
    }
  }
}




# indices_boost 设置索引的相关性 ，值为权重
GET /my_index*/_search
{
  "indices_boost": [
    {
      "my_index100": 2
    },
    {
      "my_index101": 3
    },
        {
      "my_index102": 1
    }
  ], 
  "query": {
    "term": {
      "subject": {
        "value": "subject 1"
      }
    }
  }
}

文档层面修改相关性

shell 复制代码 
# boost设置文档的权重
GET /my_index*/_search
{
  "query": {
    "bool": {
      "should": [
        {"match":{
          "title":{
            "query":"",
            "boost":4
          }
        }},
        {
          "match":{
            "content":{
              "query": "",
              "boost":1
            }
          }
        }
      ]
    }
  }
}

降低文档negative_boost

0-1 降低，大于 1 增加

shell 复制代码 
# negative 降低文档相关性 / bool - [must / must_not]也能排除
GET /my_index*/_search
{
  "query": {
    "bool": {
      "must": [
        {"content":"apple"}
      ],
      "must_not": [
        {"content":"pin"}
      ]
    }
  }
}
# query-boosting - postitive/negative/negative_boost(仅对negative生效+一般取值0-1，比如0.3，会乘法计算)
GET /my_index*/_search
{
  "query": {
    "boosting": {
      "negative": {
        "match": {
          "FIELD": "TEXT"
        }
      },
      "positive": {
        "match": {
          "FIELD": "TEXT"
        }
      },
      "negative_boost": 0.2
    }
  }
}

自定义 function_score

shell 复制代码 
# function_score自定义评分  query-function_score -- (query -- match_all)/(script_score -- script -- source)
GET /csdn_blogs/_search
{
  "query": {
    "function_score": {
      "query": {
        "match": { "content": "原神" }
      },
      "boost_mode": "sum",    
      "functions": [
        {
          "filter": { "term": { "tags": "Elasticsearch" } },
          "weight": 2  
        },
        {
          "filter": { "range": { "date": { "gte": "2024-07-10" } } },
          "weight": 1.5
        }
      ]
    }
  }
}

GET /csdn_blogs/_search
GET /csdn_blogs/_search
{
  "query": {
    "function_score": {
      "query": {"match_all": {}},
      "script_score": {
        "script": "_score * doc['f'].value + doc['sales'].value"
      }
    }
  }
}

二次打分 rescore

rescore- query-(rescore_querty /rescore_query_weight / query_weight ) / window_size

window_size:50 😕/ 只重新评分前50条

rescore_query: 二次算分的查询条件

#rescore_query_weight：二次算分的权重

query_score : 一开始算分的权重0.7

shell 复制代码 
#二次算分：只针对结果进行算分，在检索结果基础上增加更复杂的评分逻辑，提供更准确的结果排序，但也会增加查询的计算成本和响应时间
# window_size:50 :// 只重新评分前50条
# rescore_query: 二次算分的查询条件
#rescore_query_weight：二次算分的权重
# query_score : 一开始算分的权重0.7
GET /csdn_blogs/_search
{
  "query": {
    "match": {
      "FIELD": "TEXT"
    }
  },
  "rescore": {
    "query": {
      "rescore_query":{
        "match":{}
      },
      "rescore_query_weight":1.2,
      "query_weight":0.7
    },
    "window_size": 50
  }
}
GET /csdn_blogs/_search
{
  "query": {
    "match": {
      "content": "原神 搜索 高亮"
    }
  },
  "rescore": {
    "window_size": 50,   
    "query": {
      "rescore_query": {
        "match_phrase": { "content": "原神 搜索 高亮" }
      },
      "query_weight": 0.7,
      "rescore_query_weight": 1.2
    }
  }
}

多字段搜索场景优化

dis_max:只取最大的

tie_breaker:其它字段匹配分数乘这个再加到最佳分(用于 dis_max 实现平衡，两个字段匹配分相加，让匹配更多字段的文档的分更高) - 评分求和

tie_breaker:0为最佳匹配，1代表都很重要，（获得最佳-- 将其他不是最佳的与tie_breaker相乘，最佳和相乘后的其他求和并规范化）

最终 = 最佳得分 + 其他匹配字段得分 * tie_breaker

multi_match 就是默认 best_fields,可以指定 tie_breaker

shell 复制代码 
# 多字段搜索场景优化

#最佳best：多个字段返回评分最高 
#多数字段most：多字段返回字段评分和
#混合字段Cross：都显示
GET /csdn_blogs/_search




# 取最大的
# tie_breaker:0为最佳匹配，1代表都很重要，（获得最佳-- 将其他不是最佳的与tie_breaker相乘，最佳和相乘后的其他求和并规范化）
# 最终 = 最佳得分 + 其他匹配字段得分 * tie_breaker
GET /csdn_blogs/_search
{
  "explain": true,
  "query": {
    "dis_max": {
      ""tie_breaker": 0.7,
      "boost": 1.2,
      "queries": [
        {
          "match": {
            "FIELD": "TEXT"
          }
        },
        {
          "match": {
            "FIELD": "TEXT"
          }
        }]
    }
  }
}


#会把多个字段的分词结果像一个大字段一样匹配
#适合搜索时标题和内容互补，比如标题里有 Elasticsearch，正文里有 原神，也能匹配出来
#operator:"and" 保证所有词都要匹配（跨字段组合也行）
GET /csdn_blogs/_search
{
  "explain": true,
  "query": {
    "multi_match": {
      "query": "Elasticsearch 原神",
      "type": "cross_fields",
      "fields": ["title^2", "content"],
      "operator": "and"
    }
  }
}

# best_fields ：最佳字段的得分，最终=其他*0.1 + 最佳，默认就是best_fields，不用指定，等价于dis_max
POST /csdn_blogs/_search
{
  "query": {
    "multi_match": {
      "query": "原神启动",
      "fields": ["title","body"],
      "tie_breaker":0.2
      "type":"best_fields"
    }
  }
}


# 取最多:most_fields:所有should撇配的字段分数相加，匹配更多字段的文档排得更前，适合多字段同时重要的情况
#直接用 bool/should，它会把多个 should 的匹配分数合起来
#全求和，每个字段对于最终评分的贡献可以通过自定值boost来控制fields:["title^10"]
GET /csdn_blogs/_explain/3
{
  "query":{
    "multi_match":{
      "query":"讲解 原神 对比",
      "type":"most_fields",
      "fields":["title","content"]
    }
  }
}

GET /csdn_blogs/_search
{
  "explain": true, 
  "query": {
    
    "bool": {
      "should": [
        {
          "match": {
            "title": "Elasticsearch"
          }
        },
        {
          "match": {
            "content": "原神"
          }
        }
      ]
    }
  }
}

# cross_fields，支持and，就把字段都必须匹配到
GET /csdn_blogs/_search
{
  "explain": true,
  "query": {
    "multi_match": {
      "query": "Elasticsearch 原神",
      "type": "cross_fields",
      "fields": ["title^2", "content"],
      "operator": "and"
    }
  }
}
# copy_to 将多个字段值复制到组字段中，然后将其作为单个字段进行查询
PUT /address
{
  "mappings": {
    "properties": {
      "province":{
        "type":"keyword",
        "copy_to": "full_address"
      },
      "city":{
        "type":"text",
        "copy_to": "full_address"
      }
    }
  }
}
POST /address/_bulk
{"index":{"_index":"address","_id":1}}
{"province":"浙江省","city":"杭州市"}
{"index":{"_index":"address","_id":2}}
{"province":"广东省","city":"深圳市"}
{"index":{"_index":"address","_id":3}}
{"province":"北京市","city":"北京市"}
{"index":{"_index":"address","_id":4}}
{"province":"四川省","city":"成都市"}

# 可以搜，但是看不到，只能用于搜索，额外增加存储空间，但是省掉了multi_matc
GET /address/_search
{
  "query": {
    "match": {
      "full_address": "浙江 杭州"
    }
  }
}

聚合

聚合：指标聚合（count）、桶聚合（group）、管道聚合（上一次结果用于下次输入）

指标聚合（Metrics Aggregations）

→ 统计字段的最大、最小、平均、总和等指标

桶聚合（Bucket Aggregations）

→ 分组统计，比如按元素类型分组、按地区分组、按星级区间分布

管道聚合（Pipeline Aggregations）

→ 对上一步聚合的结果做二次计算，比如最大值、百分比、排序等

同级： (max/min/avg/sum) bucket/(stats/extended status)bucket /percentiles bucket

父子：derivative求导/cumultive sum累计求和/moving function 移动平均值

累计求和:cumulative_sum必须是父子关系，且父亲是histogram, date_histogram or auto_date_histogram as parent

看结果：aggregations

shell 复制代码 
# 聚合：指标聚合（count）、桶聚合（group）、管道聚合（上一次结果用于下次输入）
指标聚合（Metrics Aggregations）min、max、avg、sum、stats、extended_stats、percentiles
→ 统计字段的最大、最小、平均、总和等指标:# aggs/{k}/{min/max/...}
桶聚合（Bucket Aggregations）aggs/{k}/{文本terms需开启fieldata/数字range、datarange、histogram、date histogram}可以嵌套（桶里还有桶）
→ 分组统计，比如按元素类型分组、按地区分组、按星级区间分布
直方图aggs/{key}/histogram/[field,interval,extend_bounds]
管道聚合（Pipeline Aggregations）
→ 对上一步聚合的结果做二次计算，比如最大值、百分比、排序等
同级： (max/min/avg/sum) bucket/(stats/extended status)bucket /percentiles bucket
父子：derivative求导/cumultive sum累计求和/moving function 移动平均值
 累计求和:cumulative_sum必须是父子关系，且父亲是histogram, date_histogram or auto_date_histogram as parent 
PUT /genshin_chars
{
  "mappings": {
    "properties": {
      "name": { "type": "keyword" },      
      "element": { "type": "keyword" },   
      "region": { "type": "keyword" },     
      "rarity": { "type": "integer" },   
      "hp": { "type": "integer" },       
      "atk": { "type": "integer" },      
      "def": { "type": "integer" }      
    }
  }
}

POST /_bulk
{"index":{"_index":"genshin_chars","_id":1}}
{"name":"温迪","element":"风","region":"蒙德","rarity":5,"hp":10531,"atk":263,"def":669}
{"index":{"_index":"genshin_chars","_id":2}}
{"name":"钟离","element":"岩","region":"璃月","rarity":5,"hp":14495,"atk":251,"def":738}
{"index":{"_index":"genshin_chars","_id":3}}
{"name":"胡桃","element":"火","region":"璃月","rarity":5,"hp":15552,"atk":106,"def":876}
{"index":{"_index":"genshin_chars","_id":4}}
{"name":"甘雨","element":"冰","region":"璃月","rarity":5,"hp":9797,"atk":335,"def":630}
{"index":{"_index":"genshin_chars","_id":5}}
{"name":"刻晴","element":"雷","region":"璃月","rarity":5,"hp":13103,"atk":323,"def":799}
{"index":{"_index":"genshin_chars","_id":6}}
{"name":"香菱","element":"火","region":"璃月","rarity":4,"hp":10875,"atk":225,"def":669}
{"index":{"_index":"genshin_chars","_id":7}}
{"name":"行秋","element":"水","region":"璃月","rarity":4,"hp":10222,"atk":202,"def":758}
{"index":{"_index":"genshin_chars","_id":8}}
{"name":"宵宫","element":"火","region":"稻妻","rarity":5,"hp":10164,"atk":323,"def":615}
{"index":{"_index":"genshin_chars","_id":9}}
{"name":"雷电将军","element":"雷","region":"稻妻","rarity":5,"hp":12907,"atk":337,"def":789}
POST /_bulk
{"index":{"_index":"genshin_chars","_id":11}}
{"name":"琴","element":"风","region":"蒙德","rarity":5,"hp":14695,"atk":239,"def":729}
{"index":{"_index":"genshin_chars","_id":12}}
{"name":"迪卢克","element":"火","region":"蒙德","rarity":5,"hp":12981,"atk":311,"def":671}
{"index":{"_index":"genshin_chars","_id":13}}
{"name":"魈","element":"风","region":"璃月","rarity":5,"hp":12773,"atk":349,"def":799}
{"index":{"_index":"genshin_chars","_id":14}}
{"name":"阿贝多","element":"岩","region":"蒙德","rarity":5,"hp":11347,"atk":303,"def":876}
{"index":{"_index":"genshin_chars","_id":15}}
{"name":"枫原万叶","element":"风","region":"稻妻","rarity":5,"hp":13348,"atk":298,"def":753}


# 指标聚合：min、max、avg、sum、stats、extended_stats、percentiles
# 单值min/max/avg/sum/Cardinality（类似distinct Count）
# 多值stats/extended stats / percentile/percentile rank /top hits
POST /genshin_chars/_search
# 最高、平均和最低、总和、统计hp和按照元素去重
# size:0,可以不显示文档
GET /genshin_chars/_search
{
  "size":0,
  "aggs":{
    "hp_max":{
      "max": {
        "field": "hp"
      }
    },
    "hp_avg":{
        "avg":
        {
          "field":"hp"
        }
    },
    "hp_min":{
      "min":
      {
        "field":"hp"
      }
    },
    "hp_sum":{
      "sum":
      {
        "field":"hp"
      }
    },
    "stats_hp":{
      "stats": {
        "field": "hp"
      }
    },
    "cardinate":{
      "cardinality": {
        "field": "element"
      }
    },
    "order_elemnt":{
      "terms": {
        "field": "element",
        "size": 3,
        "order": {
          "_count": "desc"
        }
      }
    }
    
  }
}
# aggs/{k}/stats/min/max
GET /genshin_chars/_search
{
  "size": 0,
  "aggs": {
    "atk_stats": {
      "stats": { "field": "atk" }
    }
  }
}

# 
# agg/{k}/terms/field，size:0表示不显示字段
# 按照element进行分组
# 设置order可以进行排序
# size：可以进行显示排序后的前3条数据

GET /genshin_chars/_search
{
  "size": 0,
  "aggs": {
    "by_element": {
      "terms": {
        "field": "element",
        "size": 3,
        "order": {
          "_count": "desc"
        }
      }
    }
  }
}

# 如果text分组失败了，需要打开fieldata
PUT /csdn_blogs/_mapping
{
  "properties":{
    "NAME":{
      "type":"text",
      "fieldata":true
    }
  }
}
GET /genshin_chars/_search
{
  "size": 0,
  "aggs": {
    "by_rarity": {
      "terms": { "field": "rarity" },
      "aggs": {
        "avg_atk": { "avg": { "field": "atk" } }
      }
    }
  }
}

# 可以设置key进行显示，默认是from-to，默认*
GET /genshin_chars/_search
{
  "size": 0,
  "aggs": {
    "atk_ranges": {
      "range": {
        "field": "atk",
        "ranges": [
          { "to": 200 },
          { "from": 200, "to": 300 },
          { "from": 300 }
        ]
      }
    }
  }
}
# 直方图aggs/{key}/histogram/[field,interval,extend_bounds]
GET /genshin_chars/_search
{
  "size":0,
  "aggs": {
    "test_graph": {
      "histogram": {
        "field": "hp",
        "interval": 5000,
        "extended_bounds": {
          "min": 1000,
          "max": 15000
        }
      }
    }
  }
}
# 各组攻击力平均值的最大值 ：按照element分组，在分组里面再算平均值，根据平均值计算最大值
GET /genshin_chars/_search
{
  "size": 0,
  "aggs": {
    "by_element": {
      "terms": { "field": "element" },
      "aggs": {
        "avg_atk1": { "top_hits": { "sort":
        [ {"atk":
        {"order":"desc"}
        }
        ] ,"size":1} }
        }
      }
  }
}
# 指定size2，找出不同元素中血量hp最大的：先按照element分组，然后获取hp进行降序，size获取前2
POST /genshin_chars/_search
{
  "size": 0, 
  "aggs": {
    "by_element": {
      "terms": {
        "field": "element"
      },
      "aggs": {
        "hp_max": {
          "top_hits": {
            "size": 2,
            "sort": [
              {
                "hp":{
                  "order": "desc"
                }
              }]
          }
        }
      }
    }
  }
}

# 统计各个地区hp
GET /genshin_chars/_search
{
  "size":0,
  "aggs": {
    "state_by_region": {
      "terms": {
        "field": "region"
      },
      "aggs": {
        "region_stats": {
          "stats": {
            "field": "hp"
          }
        }
      }
    }
  }
}
# 统计各地区+各个元素的hp,嵌套3层
GET /genshin_chars/_search
{
  "size": 0,
  "aggs": {
    "by_region": {
      "terms": {
        "field": "region"
      },
      "aggs": {
        "by_element": {
          "terms": {
            "field": "element"
          },
          "aggs": {
            "hp_stats": {
              "stats": {
                "field": "hp"
              }
            }
          }
        }
      }
    }
  }
}


#元素分组 + 每组攻击力 & 生命值平均 + 找出攻击力平均最高的元素
GET /genshin_chars/_search
{
  "size": 0,
  "aggs": {
    "by_element": {
      "terms": { "field": "element" },
      "aggs": {
        "avg_atk": { "avg": { "field": "atk" } },
        "avg_hp": { "avg": { "field": "hp" } }
      }
    },
    "max_avg_atk_group": {
      "max_bucket": { "buckets_path": "by_element>avg_atk" }
    }
  }
}


# 管道聚合-对聚合分析的结果，再次进行聚合
# 同级： (max/min/avg/sum) bucket/(stats/extended status)bucket /percentiles bucket
# 父子：derivative求导/cumultive sum累计求和/moving function 移动平均值
# 元素分组--获取组内平均攻击力--找出最高的攻击力---结果在max_avg_atk里面，结果是冰：335,stats_avg_atk统计平均攻击力的各个指标,percentiles_bucket是进行百分位统计
GET /genshin_chars/_search
{
  "size": 0,
  "aggs": {
    "by_element": {
      "terms": { "field": "element" },
      "aggs": {
        "avg_atk": { "avg": { "field": "atk" } }
     
        }
      },
      "max_avg_atk": {
          "max_bucket": { "buckets_path": "by_element>avg_atk" }
    },
     "stats_avg_atk": {
          "stats_bucket": { "buckets_path": "by_element>avg_atk" }
    },
    "percentiles_avg_atk":{
      "percentiles_bucket": {
        "buckets_path": "by_element>avg_atk"
      }
    }
  }
}

# 累计求和:cumulative_sum必须是父子关系，且父亲是histogram, date_histogram or auto_date_histogram as parent 
POST /genshin_chars/_search
{
  "size":0,
  "aggs": {
    "hp_histogram": {
      "histogram": {
        "field": "hp",
        "interval": 1000,
        "min_doc_count": 0
      },
      "aggs": {
        "avg_atk": {
          "avg": {
            "field": "atk"
          }
        },
        "cumulative_atk":{
          "cumulative_sum": {
            "buckets_path": "avg_atk"
          }
        }
      }
    }
  }
}

优化

报错

  1. 报错说咱们集群太 low 了：
shell 复制代码 
# 索引文件
vim /etc/security/limits.conf
*	soft	nofile	65536
* hard	nofile	65536
*	soft	nproc	4096
*	hard	nproc	4096
#线程
vim /etc/security/limits.d/20-nrpoc.conf
*	soft	nproc	4096
# 内存
vim /etc/sysctl.conf
vm.max_map_count=262144
sysctl -p
# 缺少节点配置
vim config/elasticsearch.yml
discovery.seed_hosts: ["127.0.0.1"]
cluster.initial_master_nodes: ["node-1
# 或者直接单节点
discovery.type: single-node
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