elasticsearch基础使用

标签：name brand 基础 id score elasticsearch 使用 com price

索引操作

mapping映射属性

type：字段数据类型，
- 字符串： text（可分词的文本），keyword（精确值，不可以分词的文本）如下图，邮箱就是keyword，名字就是tex
- 数值： long，integer， short，byte，double，float
- 布尔： boolean
- 日期：date
index：是否创建倒排索引，默认是true
analyzer：使用哪种分词器
properties：该字段使用的子字段

索引库CRUD

创建索引库名

dsl语法如下

创建索引库语法如下

PUT /heima
{
    "mappings": {
        "properties": {
            "info": {
                "type": "text",
                "analyzer": "ik_smart"
            },
            "email": {
                "type": "keyword",
                "index": false
            },
            "name" :{
                "type": "object",
                "properties": {
					"firstName": {
                        "type": "keyword"
                    },
                    "lastName": {
						"type": "keyword"
                    }
                }
            }
        }
    }
}

然后生成了下面的结果

删除和查看索引

查看

GET /heima

删除

DELETE /heima

修改索引库

在es中禁止修改修改索引库，但是我们可以添加新的字段

PUT /index_name/_mapping
{
    "properties": {
        "new_index":{
            "type": "integer"
        }
    }
}

示例

// 添加实例

PUT /heima/_mapping
{
    "properties": {
        "age":{
            "type": "integer"
        }
    }
}
GET /heima

发现增添了 age

文档操作

文档的CRUD

添加文档

// 插入文档
POST /heima/_doc/1 
{
    "info": "梅子留酸软齿牙，芭蕉分绿与窗纱。日长睡起无情思，闲看儿童捉柳花",
    "email": "[email protected]",
    "name": {
        "firstname": "杨",
        "lastname": "万里"
    }
}

查询文档

GET /heima/_doc/1

结果

{
  "_index" : "heima",
  "_type" : "_doc",
  "_id" : "1",
  "_version" : 1,
  "_seq_no" : 0,
  "_primary_term" : 1,
  "found" : true,
  "_source" : {
    "info" : "梅子留酸软齿牙，芭蕉分绿与窗纱。日长睡起无情思，闲看儿童捉柳花",
    "email" : "[email protected]",
    "name" : {
      "firstname" : "杨",
      "lastname" : "万里"
    }
  }
}

删除文档

DELETE /heima/_doc/1

修改文档

全量修改

几乎和索引相似，他还会添加新文档，删除旧的文档
如果没有的文档，他会直接创建
原本有什么字段，就得写出来什么字段，不能少写，不能多写

// 插入文档
POST /heima/_doc/1 
{
    "info": "梅子留酸软齿牙，芭蕉分绿与窗纱。日长睡起无情思，闲看儿童捉柳花1111",
    "email": "[email protected]",
    "name": {
        "firstname": "杨1",
        "lastname": "万里1"
    }
}

增量修改

注意这里是POST ,而不是PUT

POST /heima/_update/1 
{
    "doc": {
		"email": "[email protected]"
    }
}

DSL查询文档

DSL查询分类

Elasticsearch提供了基于JSON的DSL（Domain Specific Language）来定义查询。常见的查询类型包括：

查询所有：查询出所有数据，一般测试用。例如：match_all
全文检索（full text）查询：利用分词器对用户输入内容分词，然后去倒排索引库中匹配。例如：
- match_query
- multi_match_query
精确查询：根据精确词条值查找数据，一般是查找keyword、数值、日期、boolean等类型字段。例如：
- ids
- range
- term
地理（geo）查询：根据经纬度查询。例如：
- geo_distance
- geo_bounding_box
复合（compound）查询：复合查询可以将上述各种查询条件组合起来，合并查询条件。例如：
- bool
- function_score

查询的语法基本一致：

GET /indexName/_search
{
  "query": {
    "查询类型": {
      "查询条件": "条件值"
    }
  }
}

我们以查询所有为例，其中：

查询类型为match_all
没有查询条件

// 查询所有
GET /indexName/_search
{
  "query": {
    "match_all": {
    }
  }
}

其它查询无非就是查询类型、查询条件的变化。

全文检索查询

准备索引

PUT /hotel 
{
    "mappings": {
        "properties": {
            "id": {
                "type": "keyword"
            },
            "name": {
                "type": "text",
                "analyzer": "ik_max_word",
                "copy_to": "all"
            },
            "address": {
                "type": "keyword",
                "index": false
            },
            "price": {
                "type": "integer"
            },
            "socre": {
                "type": "integer"
            },
            "brand": {
                "type": "keyword",
                "copy_to": "all"
            },
            "city": {
                "type": "keyword"
            },
            "starName": {
                "type": "keyword"
            },
            "business": {
                "type": "keyword",
                "copy_to": "all"
            },
            "location": {
                "type": "geo_point"
            },
            "pic": {
                "type": "keyword",
                "index": false
            },
            "all": {
               "type": "text",
                "analyzer": "ik_max_word"
            }
        }
    }
}

插入文档

{"address":"静安交通路40号","brand":"7天酒店","business":"四川北路商业区","city":"上海","id":36934,"location":"31.251433, 121.47522","name":"7天连锁酒店(上海宝山路地铁站店)","pic":"https://m.tuniucdn.com/fb2/t1/G1/M00/3E/40/Cii9EVkyLrKIXo1vAAHgrxo_pUcAALcKQLD688AAeDH564_w200_h200_c1_t0.jpg","price":336,"score":37,"starName":"二钻"}
{"address":"广灵二路126号","brand":"速8","business":"四川北路商业区","city":"上海","id":38609,"location":"31.282444, 121.479385","name":"速8酒店(上海赤峰路店)","pic":"https://m.tuniucdn.com/fb2/t1/G2/M00/DF/96/Cii-TFkx0ImIQZeiAAITil0LM7cAALCYwKXHQ4AAhOi377_w200_h200_c1_t0.jpg","price":249,"score":35,"starName":"二钻"}
{"address":"兰田路38号","brand":"速8","business":"长风公园地区","city":"上海","id":38665,"location":"31.244288, 121.422419","name":"速8酒店上海中山北路兰田路店","pic":"https://m.tuniucdn.com/fb2/t1/G2/M00/EF/86/Cii-Tlk2mV2IMZ-_AAEucgG3dx4AALaawEjiycAAS6K083_w200_h200_c1_t0.jpg","price":226,"score":35,"starName":"二钻"}
{"address":"徐汇龙华西路315弄58号","brand":"7天酒店","business":"八万人体育场地区","city":"上海","id":38812,"location":"31.174377, 121.442875","name":"7天连锁酒店(上海漕溪路地铁站店)","pic":"https://m.tuniucdn.com/fb2/t1/G2/M00/E0/0E/Cii-TlkyIr2IEWNoAAHQYv7i5CkAALD-QP2iJwAAdB6245_w200_h200_c1_t0.jpg","price":298,"score":37,"starName":"二钻"}
{"address":"闵行莘庄镇七莘路299号","brand":"7天酒店","business":"莘庄工业区","city":"上海","id":39106,"location":"31.113812, 121.375869","name":"7天连锁酒店（上海莘庄地铁站店）","pic":"https://m.tuniucdn.com/fb2/t1/G2/M00/D8/11/Cii-T1ku2zGIGR7uAAF1NYY9clwAAKxZAHO8HgAAXVN368_w200_h200_c1_t0.jpg","price":348,"score":41,"starName":"二钻"}
{"address":"杨浦国权路315号","brand":"7天酒店","business":"江湾、五角场商业区","city":"上海","id":39141,"location":"31.290057, 121.508804","name":"7天连锁酒店(上海五角场复旦同济大学店)","pic":"https://m.tuniucdn.com/fb2/t1/G2/M00/C7/E3/Cii-T1knFXCIJzNYAAFB8-uFNAEAAKYkQPcw1IAAUIL012_w200_h200_c1_t0.jpg","price":349,"score":38,"starName":"二钻"}
{"address":"虹桥路100号","brand":"万怡","business":"徐家汇地区","city":"上海","id":45845,"location":"31.192714, 121.434717","name":"上海西藏大厦万怡酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/48GNb9GZpJDCejVAcQHYWwYyU8T_w200_h200_c1_t0.jpg","price":589,"score":45,"starName":"四钻"}
{"address":"新元南路555号","brand":"豪生","business":"滴水湖临港地区","city":"上海","id":45870,"location":"30.871729, 121.81959","name":"上海临港豪生大酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/2F5HoQvBgypoDUE46752ppnQaTqs_w200_h200_c1_t0.jpg","price":896,"score":45,"starName":"四星级"}
{"address":"恒丰路338号","brand":"万怡","business":"上海火车站地区","city":"上海","id":46829,"location":"31.242977, 121.455864","name":"上海浦西万怡酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/x87VCoyaR8cTuYFZmKHe8VC6Wk1_w200_h200_c1_t0.jpg","price":726,"score":46,"starName":"四钻"}
{"address":"施新路958号","brand":"华美达","business":"浦东机场核心区","city":"上海","id":47066,"location":"31.147989, 121.759199","name":"上海浦东东站华美达酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/2pNujAVaQbXACzkHp8bQMm6zqwhp_w200_h200_c1_t0.jpg","price":408,"score":46,"starName":"四钻"}
{"address":"松江荣乐东路677号","brand":"速8","business":"佘山、松江大学城","city":"上海","id":47478,"location":"31.016712, 121.261606","name":"速8酒店(上海松江中心店)","pic":"https://m.tuniucdn.com/filebroker/cdn/res/07/36/073662e1718fccefb7130a9da44ddf5c_w200_h200_c1_t0.jpg","price":428,"score":35,"starName":"二钻"}
{"address":"东方路838号","brand":"万怡","business":"浦东陆家嘴金融贸易区","city":"上海","id":56201,"location":"31.226031, 121.525801","name":"上海齐鲁万怡大酒店","pic":"https://m.tuniucdn.com/fb2/t1/G6/M00/52/B6/Cii-TF3eXKeIJeN7AASiKHbTtx4AAGRegDSBzMABKJA111_w200_h200_c1_t0.jpg","price":873,"score":44,"starName":"四星级"}
{"address":"新金桥路18号","brand":"华美达","business":"浦东金桥地区","city":"上海","id":56214,"location":"31.244916, 121.590752","name":"上海浦东华美达大酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/3jtXiuMKZEXJAuKuAkc47yLCjUBt_w200_h200_c1_t0.jpg","price":830,"score":45,"starName":"四星级"}
{"address":"南桥镇南桥路1号","brand":"万怡","business":"奉贤开发区","city":"上海","id":56227,"location":"30.910917, 121.456525","name":"上海圣淘沙万怡酒店","pic":"https://m.tuniucdn.com/fb2/t1/G6/M00/52/B9/Cii-U13eXSiIdJjXAARSA6FywFYAAGRnwHvy1AABFIb158_w200_h200_c1_t0.jpg","price":899,"score":45,"starName":"四星级"}
{"address":"番禺路400号","brand":"皇冠假日","business":"徐家汇地区","city":"上海","id":56392,"location":"31.202768, 121.429524","name":"上海银星皇冠假日酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/37ucQ38K3UFdcRqntJ8M5dt884HR_w200_h200_c1_t0.jpg","price":809,"score":47,"starName":"五星级"}
{"address":"武东路188号","brand":"豪生","business":"江湾/五角场商业区","city":"上海","id":56852,"location":"31.304182, 121.492936","name":"上海财大豪生大酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/2jGHezLZvPZqC9cBGesbP5vAhCXi_w200_h200_c1_t0.jpg","price":592,"score":46,"starName":"五钻"}
{"address":"月华路9号","brand":"华美达","business":"奉贤开发区","city":"上海","id":56912,"location":"30.814382, 121.464521","name":"上海华凯华美达广场酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/45iaCNCuZavJTxwTLskhVKzwynLD_w200_h200_c1_t0.jpg","price":747,"score":40,"starName":"四钻"}
{"address":"黄兴路1888号","brand":"华美达","business":"江湾/五角场商业区","city":"上海","id":56977,"location":"31.292932, 121.519759","name":"上海五角场华美达大酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/26VREqAQdaGFvJdAJALVtjxcNMpL_w200_h200_c1_t0.jpg","price":499,"score":40,"starName":"三钻"}
{"address":"世纪大道88号（54楼办理入住）","brand":"君悦","business":"浦东陆家嘴金融贸易区","city":"上海","id":60214,"location":"31.235152, 121.506082","name":"上海金茂君悦大酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/7Azm3jvGUHuXe3eS1DrixAWVTXY_w200_h200_c1_t0.jpg","price":699,"score":46,"starName":"五星级"}
{"address":"静安华山路250号","brand":"希尔顿","business":"静安寺地区","city":"上海","id":60223,"location":"31.219306, 121.445427","name":"上海希尔顿酒店","pic":"https://m.tuniucdn.com/filebroker/cdn/res/92/10/9210e74442aceceaf6e196d61fc3b6b1_w200_h200_c1_t0.jpg","price":2688,"score":37,"starName":"五星级"}
{"address":"杨高北路1000号","brand":"皇冠假日","business":"浦东外高桥地区","city":"上海","id":60359,"location":"31.338944, 121.590611","name":"上海外高桥皇冠假日酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/VcKUM9zUSiVgDhFioc6mWQoX9ES_w200_h200_c1_t0.jpg","price":3299,"score":46,"starName":"五星级"}
{"address":"南京西路88号","brand":"丽笙","business":"人民广场地区","city":"上海","id":60363,"location":"31.23462, 121.47327","name":"上海新世界丽笙大酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/2j31b7X3YzGkf4Li3phS6TG1mtwm_w200_h200_c1_t0.jpg","price":1341,"score":46,"starName":"五星级"}
{"address":"邯郸路199号","brand":"皇冠假日","business":"江湾/五角场商业区","city":"上海","id":60398,"location":"31.295382, 121.502537","name":"上海复旦皇冠假日酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/2H1Gk8LHaBWZfYvR6NYYcGTvACmL_w200_h200_c1_t0.jpg","price":924,"score":47,"starName":"五星级"}
{"address":"黄浦路199号","brand":"君悦","business":"外滩地区","city":"上海","id":60487,"location":"31.245409, 121.492969","name":"上海外滩茂悦大酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/2Swp2h1fdj9zCUKsk63BQvVgKLTo_w200_h200_c1_t0.jpg","price":689,"score":44,"starName":"五星级"}
{"address":"汾阳路1号","brand":"豪生","business":"淮海路/新天地地区","city":"上海","id":60522,"location":"31.215497, 121.456297","name":"上海嘉豪淮海国际豪生酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/38UBi4QYuaF8jN94CxQ7tb7tjtmZ_w200_h200_c1_t0.jpg","price":425,"score":45,"starName":"四钻"}
{"address":"沪宜公路3101号","brand":"万怡","business":"嘉定新城","city":"上海","id":60916,"location":"31.368523, 121.258567","name":"上海绿地万怡酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/3VLwG9tTQQnp3M3MTeMTdx9nas9B_w200_h200_c1_t0.jpg","price":328,"score":45,"starName":"四钻"}
{"address":"红松东路1116号","brand":"希尔顿","business":"虹桥地区","city":"上海","id":60922,"location":"31.18746, 121.395312","name":"上海虹桥祥源希尔顿酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/tQRqDTFkHnHzMZiDKjcGV81ekvc_w200_h200_c1_t0.jpg","price":1108,"score":45,"starName":"五钻"}
{"address":"四平路59号","brand":"喜来登","business":"四川北路商业区","city":"上海","id":60935,"location":"31.2579, 121.487954","name":"上海虹口三至喜来登酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/3C3gxLxLjVwnkxJwJm8rd3f38kcd_w200_h200_c1_t0.jpg","price":1899,"score":46,"starName":"五星级"}
{"address":"西藏中路555号","brand":"万豪","business":"人民广场地区","city":"上海","id":61075,"location":"31.236681, 121.473529","name":"上海雅居乐万豪酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/3FoT16PkXavKsssvktVvVq5Si6Cr_w200_h200_c1_t0.jpg","price":1152,"score":46,"starName":"五钻"}
{"address":"自由贸易试验区临港新片区南岛1号","brand":"皇冠假日","business":"滴水湖临港地区","city":"上海","id":61083,"location":"30.890867, 121.937241","name":"上海滴水湖皇冠假日酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/312e971Rnj9qFyR3pPv4bTtpj1hX_w200_h200_c1_t0.jpg","price":971,"score":44,"starName":"五钻"}
{"address":"王府井东街8号","brand":"希尔顿","business":"天安门/王府井地区","city":"北京","id":309208,"location":"39.914539, 116.413392","name":"北京王府井希尔顿酒店","pic":"https://m.tuniucdn.com/fb2/t1/G6/M00/52/10/Cii-TF3ePt2IX9UEAALb6VYBSmoAAGKMgGsuW8AAtwB147_w200_h200_c1_t0.jpg","price":1679,"score":46,"starName":"五钻"}
{"address":"长安街1号东方广场","brand":"君悦","business":"天安门/王府井地区","city":"北京","id":394559,"location":"39.909635, 116.414621","name":"北京东方君悦大酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/3mFqcNSh7eEo9yc3Rw2P5HDNTdDe_w200_h200_c1_t0.jpg","price":686,"score":45,"starName":"五星级"}
{"address":"建国门内大街甲18号","brand":"豪生","business":"北京站/建国门地区","city":"北京","id":394617,"location":"39.905768, 116.428153","name":"北京宝辰饭店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/NEYa6EfDHuhhb19Ct85WBbkKHZU_w200_h200_c1_t0.jpg","price":418,"score":44,"starName":"四星级"}
{"address":"东北三环西坝河太阳宫桥东北角云南大厦","brand":"皇冠假日","business":"国展中心地区","city":"北京","id":394796,"location":"39.972409, 116.434698","name":"北京新云南皇冠假日酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/dfP8K782eTsohQWSRdkd7St9LA2_w200_h200_c1_t0.jpg","price":485,"score":46,"starName":"五星级"}
{"address":"东三环北路东方路1号","brand":"希尔顿","business":"燕莎/朝阳公园商业区","city":"北京","id":395434,"location":"39.952703, 116.462387","name":"北京希尔顿酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/3fwNbKGhk6XCrkdVyxwhC5uGpLVy_w200_h200_c1_t0.jpg","price":350,"score":45,"starName":"五星级"}
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{"address":"宁夏路718号","brand":"凯悦","business":"中山公园商业区","city":"上海","id":1754929048,"location":"31.232041, 121.412492","name":"上海环球港凯悦酒店","pic":"https://m.tuniucdn.com/fb2/t1/G6/M00/45/B6/Cii-U13ZY-CIF-8MAAXkwQoY7FIAAFpQgEE1bgABeTZ750_w200_h200_c1_t0.jpg","price":1336,"score":45,"starName":"五钻"}
{"address":"棕榈大道33号","brand":"万豪","business":"较场尾/大鹏所城","city":"深圳","id":1762661667,"location":"22.569193, 114.459325","name":"深圳佳兆业万豪酒店","pic":"https://m.tuniucdn.com/fb2/t1/G6/M00/45/EC/Cii-TF3ZqE-IYM0NAAY6GIHLZNsAAFr2QDhR8EABjow444_w200_h200_c1_t0.jpg","price":223,"score":46,"starName":"五钻"}
{"address":"西直门北大街49号","brand":"如家","business":"西直门/北京展览馆地区","city":"北京","id":1765008760,"location":"39.945106, 116.353827","name":"如家酒店(北京西直门北京北站店)","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/4CLwbCE9346jYn7nFsJTQXuBExTJ_w200_h200_c1_t0.jpg","price":356,"score":44,"starName":"二钻"}
{"address":"揽海路2888号","brand":"喜来登","business":"崇明岛/长兴岛/横沙岛","city":"上海","id":1880614409,"location":"31.462167, 121.823103","name":"上海崇明由由喜来登酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/21gDCGgRT3xFqCd3FxBh633j6Qsu_w200_h200_c1_t0.jpg","price":2198,"score":45,"starName":"五钻"}
{"address":"西三环南路44号-218","brand":"如家","business":"北京西站/丽泽商务区","city":"北京","id":1889333146,"location":"39.869638, 116.313075","name":"如家酒店(北京西客站丽泽桥店)","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/kG5corYUDC7U1qE8RAY6xCVnGxq_w200_h200_c1_t0.jpg","price":459,"score":47,"starName":"二钻"}
{"address":"陈行公路3701号","brand":"皇冠假日","business":"浦江镇地区","city":"上海","id":1908594080,"location":"31.090063, 121.489728","name":"上海建工浦江皇冠假日酒店","pic":"https://m.tuniucdn.com/fb2/t1/G6/M00/45/B7/Cii-U13ZZDWIePrGAAPyImW93N0AAFpRgMmj4MAA_I6005_w200_h200_c1_t0.jpg","price":843,"score":46,"starName":"五钻"}
{"address":"蟠文路333号","brand":"万怡","business":"虹桥机场/国家会展中心","city":"上海","id":1913922369,"location":"31.185504, 121.287709","name":"上海中建万怡酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/39Afm5Bxgd784eMeFB5DrcsPnhT_w200_h200_c1_t0.jpg","price":889,"score":47,"starName":"四钻"}
{"address":"丹巴路99号","brand":"万怡","business":"长风公园地区","city":"上海","id":1916002015,"location":"31.22292, 121.379912","name":"上海苏宁环球万怡酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/svpYHdmVDck91NqAhjtngcXth2G_w200_h200_c1_t0.jpg","price":689,"score":45,"starName":"四钻"}
{"address":"康新公路4499号","brand":"万豪","business":"迪士尼度假区","city":"上海","id":1928731731,"location":"31.119187, 121.618966","name":"上海康桥万豪酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/3inpPxTnvRjMCEB39K9FuHaXohYw_w200_h200_c1_t0.jpg","price":811,"score":46,"starName":"五钻"}
{"address":"宝安国际机场地面交通中心（GTC）18号出口","brand":"凯悦","business":"宝安机场商圈","city":"深圳","id":1931442052,"location":"22.622498, 113.812341","name":"深圳机场凯悦酒店","pic":"https://m.tuniucdn.com/fb2/t1/G6/M00/45/EC/Cii-TF3ZqVeIJcTPAAUq_Ou_CrcAAFr4gKufPUABSsU446_w200_h200_c1_t0.jpg","price":291,"score":47,"starName":"五钻"}
{"address":"松岗东方大道46号","brand":"万怡","business":"松岗商业中心区","city":"深圳","id":1931602865,"location":"22.760746, 113.856961","name":"深圳联投东方万怡酒店","pic":"https://m.tuniucdn.com/fb2/t1/G6/M00/45/EC/Cii-U13ZqVyIOjdpAARI9aeBh-IAAFr4gOQh7oABEkN297_w200_h200_c1_t0.jpg","price":688,"score":45,"starName":"五钻"}
{"address":"广顺南大街8号院2号楼","brand":"凯悦","business":"望京/酒仙桥/798地区","city":"北京","id":1937347815,"location":"39.991546, 116.476288","name":"北京望京凯悦酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/2gLT4ZgJ8ZuS7sSmXzYoCXnV248p_w200_h200_c1_t0.jpg","price":617,"score":46,"starName":"五钻"}
{"address":"城南街道南口路29号","brand":"万豪","business":"昌平城区/十三陵度假区","city":"北京","id":1942938880,"location":"40.23264, 116.188888","name":"北京乐多港万豪酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/3mBWaZeaqq54E7kX2n7g9b2CZX6q_w200_h200_c1_t0.jpg","price":227,"score":45,"starName":"五钻"}
{"address":"裕民南路1366号","brand":"凯悦","business":"嘉定新城","city":"上海","id":1942992995,"location":"31.352298, 121.263314","name":"上海嘉定凯悦酒店","pic":"https://m.tuniucdn.com/fb2/t1/G6/M00/53/2D/Cii-U13edkqIfZhLAAJEW25WIF4AAGVxQIg38sAAkRz517_w200_h200_c1_t0.jpg","price":758,"score":46,"starName":"五钻"}
{"address":"福海街道宝安大道 6259号","brand":"万怡","business":"深圳国际会展中心商圈","city":"深圳","id":1951709780,"location":"22.678611, 113.805695","name":"深圳同泰万怡酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/3oUfktphxMAWq9hUxD9uqdjRdZGB_w200_h200_c1_t0.jpg","price":617,"score":48,"starName":"五钻"}
{"address":"科技南路16号","brand":"万怡","business":"科技园","city":"深圳","id":1973839294,"location":"22.531101, 113.950615","name":"深圳湾万怡酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/8C9QscRsvTWCx92wt17GAegEMFn_w200_h200_c1_t0.jpg","price":508,"score":47,"starName":"五钻"}
{"address":"南商路84-6号","brand":"如家","business":"海岸城/后海","city":"深圳","id":1975922994,"location":"22.513566, 113.9291","name":"如家酒店·neo(深圳南山海岸城南油地铁站店)","pic":"https://m.tuniucdn.com/fb2/t1/G6/M00/25/5E/Cii-TF3PGD-IQ0FcAAFIZC82AnkAAEVvAKdj4YAAUh8638_w200_h200_c1_t0.jpg","price":238,"score":44,"starName":"二钻"}
{"address":"留仙大道4088号","brand":"喜来登","business":"大学城/西丽动物园","city":"深圳","id":1989806195,"location":"22.582918, 113.97219","name":"深圳博林天瑞喜来登酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/4Rx55fZoneUeKbE3TCRSPB6WQ6bw_w200_h200_c1_t0.jpg","price":1369,"score":48,"starName":"五钻"}
{"address":"紫星路588号3幢","brand":"万怡","business":"交大/闵行经济开发区","city":"上海","id":1996823660,"location":"31.02118, 121.465186","name":"上海紫竹万怡酒店","pic":"https://m.tuniucdn.com/fb2/t1/G6/M00/53/2F/Cii-TF3edraIPzK9AAH_p8vdHKoAAGV3AJgSVEAAf-_019_w200_h200_c1_t0.jpg","price":642,"score":46,"starName":"四钻"}
{"address":"清河小营西路48号汇苑仁和大厦一层","brand":"和颐","business":"上地产业园/西三旗","city":"北京","id":1997830708,"location":"40.034623, 116.323925","name":"和颐至尚酒店(北京上地软件园店)","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/2sKjxS1hFYyBFVKVBqo2x2hSFvGj_w200_h200_c1_t0.jpg","price":753,"score":47,"starName":"四钻"}
{"address":"新松江路1277号","brand":"万怡","business":"佘山/松江大学城","city":"上海","id":2003479905,"location":"31.038198, 121.210178","name":"上海榕港万怡酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/2GM761BYH8k15qkNrJrja3cwfr2D_w200_h200_c1_t0.jpg","price":798,"score":46,"starName":"四钻"}
{"address":"府前二街6号","brand":"和颐","business":"首都机场/新国展地区","city":"北京","id":2009548883,"location":"40.063953, 116.576829","name":"和颐至尚酒店(北京首都机场新国展店)","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/43zCTomkMSkUfZByZxn77YH2XidJ_w200_h200_c1_t0.jpg","price":611,"score":46,"starName":"三钻"}
{"address":"阜康南路1号院1号楼A","brand":"凯悦","business":"延庆休闲度假区","city":"北京","id":2011785622,"location":"40.440732, 115.963259","name":"北京世园凯悦酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/uhGcQze3zZQxe4avSU8BysgYVvx_w200_h200_c1_t0.jpg","price":558,"score":47,"starName":"五星级"}
{"address":"南奉公路3111弄228号","brand":"喜来登","business":"奉贤开发区","city":"上海","id":2022598930,"location":"30.921659, 121.575572","name":"上海宝华喜来登酒店","pic":"https://m.tuniucdn.com/fb2/t1/G6/M00/45/BD/Cii-TF3ZaBmIStrbAASnoOyg7FoAAFpYwEoz9oABKe4992_w200_h200_c1_t0.jpg","price":2899,"score":46,"starName":"五钻"}
{"address":"海景二路1025号1-6层、30-50层","brand":"凯悦","business":"盐田区政府/沙头角","city":"深圳","id":2022881967,"location":"22.551323, 114.23781","name":"深圳盐田凯悦酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/2RFMLSujkczEn1HoybD6dUpN9pzr_w200_h200_c1_t0.jpg","price":650,"score":47,"starName":"五钻"}
{"address":"小黄庄1区15号","brand":"和颐","business":"马甸/安贞地区","city":"北京","id":2031683181,"location":"39.962361, 116.412931","name":"和颐至尚酒店(北京雍和宫和平里店)","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/4Xqm5BN9pZTamwmYS3eLxL417YYt_w200_h200_c1_t0.jpg","price":299,"score":47,"starName":"四钻"}
{"address":"展云路6号","brand":"皇冠假日","business":"深圳国际会展中心商圈","city":"深圳","id":2045052822,"location":"22.686581, 113.777655","name":"深圳国际会展中心皇冠假日酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/4DGZygQpE4iSpcBDCoXJvjNr4oiR_w200_h200_c1_t0.jpg","price":675,"score":47,"starName":"五钻"}
{"address":"高米店南里18号楼","brand":"希尔顿","business":"大兴北京新机场地区","city":"北京","id":2048042240,"location":"39.76875, 116.339199","name":"北京大兴希尔顿酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/3B32F8zSU2CJCWzs1hoH2o4WcquR_w200_h200_c1_t0.jpg","price":1283,"score":48,"starName":"五钻"}
{"address":"丽泽金融商务区凤凰嘴北路与金中都西路交叉口西营街8号院1号楼","brand":"喜来登","business":"北京西站/丽泽商务区","city":"北京","id":2048047291,"location":"39.864026, 116.322505","name":"北京新青海喜来登酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/4DPQMu5sMM7XR1mvcjoqtWngc7TF_w200_h200_c1_t0.jpg","price":723,"score":47,"starName":"五钻"}
{"address":"新和路127-2号","brand":"汉庭","business":"坪山高铁站商圈","city":"深圳","id":2048050570,"location":"22.700753, 114.339089","name":"汉庭酒店（深圳坪山火车站店）","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/2nXN2bWjfoqoTkPwHvLJQPYz17qD_w200_h200_c1_t0.jpg","price":436,"score":47,"starName":"二钻"}
{"address":"观湖街道五和大道327号","brand":"汉庭","business":"观澜","city":"深圳","id":2048671293,"location":"22.684459, 114.07708","name":"汉庭酒店(深圳观澜五和大道店)","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/2JrQi83S9qgDEkXqWpe5iyi44Uh2_w200_h200_c1_t0.jpg","price":234,"score":43,"starName":"二钻"}
{"address":"桂园街道宝安南路1050号嘉宾花园C栋","brand":"汉庭","business":"万象城/京基100","city":"深圳","id":2051661320,"location":"22.540352, 114.112668","name":"汉庭酒店(深圳罗湖口岸万象城二店)","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/34FRP7HLPhvKZP1a6tXu4XrJeiaw_w200_h200_c1_t0.jpg","price":667,"score":47,"starName":"三钻"}
{"address":"新华东街289号2号楼","brand":"希尔顿","business":"果园环岛/通州区","city":"北京","id":2056105938,"location":"39.908805, 116.659748","name":"北京通州北投希尔顿酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/NGKdpec3tZJNUUNWJ5pd67Cp5AY_w200_h200_c1_t0.png","price":1068,"score":48,"starName":"五钻"}
{"address":"申长路630弄1-3 号","brand":"皇冠假日","business":"虹桥机场/国家会展中心","city":"上海","id":2056126831,"location":"31.19036, 121.31535","name":"上海虹桥金臣皇冠假日酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/PvFh4Vzc84xXhm5N41F6AqdAqyJ_w200_h200_c1_t0.jpg","price":2488,"score":48,"starName":"五钻"}
{"address":"深南大道9819号","brand":"皇冠假日","business":"科技园","city":"深圳","id":2056132395,"location":"22.538923, 113.944794","name":"深圳深铁皇冠假日酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/eBLtrED2uJs7yURWfjnWge9dT1P_w200_h200_c1_t0.jpg","price":340,"score":47,"starName":"五钻"}
{"address":"沪南公路7688弄1号","brand":"万豪","business":"南汇/野生动物园","city":"上海","id":2056298828,"location":"31.030053, 121.662943","name":"上海中优城市万豪酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/2gBATEyysyQWmw3wZL863HGdqjaq_w200_h200_c1_t0.jpg","price":1200,"score":45,"starName":"五钻"}
{"address":"粤海街道高新区社区科技南路18号","brand":"万丽","business":"科技园","city":"深圳","id":2058250574,"location":"22.531674, 113.951882","name":"深圳湾万丽酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/2YWUpZsvPVkRiKgdPg95LJxaFmB6_w200_h200_c1_t0.jpg","price":351,"score":47,"starName":"五钻"}
{"address":"妫水北街1号1-14幢","brand":"皇冠假日","business":"延庆休闲度假区","city":"北京","id":2060510277,"location":"40.476483, 115.97481","name":"北京金隅八达岭皇冠假日酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/3Dzq2KxgiQbmb1sbc5iK6xqpVuFr_w200_h200_c1_t0.jpg","price":1026,"score":44,"starName":"五钻"}
{"address":"粤海街道后海社区后海第二统建楼商业裙楼第二层B","brand":"汉庭","business":"海岸城/后海","city":"深圳","id":2060618247,"location":"22.507276, 113.931251","name":"汉庭酒店(深圳海岸城店)","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/TBoXdgEx5Yjc2HobeC3fPWWnSJi_w200_h200_c1_t0.jpg","price":562,"score":49,"starName":"二钻"}
{"address":"展丰路80号","brand":"希尔顿","business":"深圳国际会展中心商圈","city":"深圳","id":2062643512,"location":"22.705335, 113.77794","name":"深圳国际会展中心希尔顿酒店","pic":"https://m.tuniucdn.com/fb3/s1/2n9c/2SHUVXNrN5NsXsTUwcd1yaHKbrGq_w200_h200_c1_t0.jpg","price":285,"score":46,"starName":"五钻"}

使用场景

全文检索查询的基本流程如下：

对用户搜索的内容做分词，得到词条
根据词条去倒排索引库中匹配，得到文档id
根据文档id找到文档，返回给用户

比较常用的场景包括：

商城的输入框搜索
百度输入框搜索

例如京东：

因为是拿着词条去匹配，因此参与搜索的字段也必须是可分词的text类型的字段。

基本语法

常见的全文检索查询包括：

match查询：单字段查询
multi_match查询：多字段查询，任意一个字段符合条件就算符合查询条件

match查询语法如下：

GET /indexName/_search
{
  "query": {
    "match": {
      "FIELD": "TEXT"
    }
  }
}

mulit_match语法如下：

GET /indexName/_search
{
  "query": {
    "multi_match": {
      "query": "TEXT",
      "fields": ["FIELD1", "FIELD12"]
    }
  }
}

示例

match查询示例

multi_match查询示例：

根据brand、name、business字段查询

可以看到，两种查询结果是一样的，为什么？
因为我们将brand、name、business值都利用copy_to复制到了all字段中。因此你根据三个字段搜索，和根据all字段搜索效果当然一样了。
但是，搜索字段越多，对查询性能影响越大，因此建议采用copy_to，然后单字段查询的方式。

总结

match和multi_match的区别是什么？

match：根据一个字段查询
multi_match：根据多个字段查询，参与查询字段越多，查询性能越差

精准查询

精确查询一般是查找keyword、数值、日期、boolean等类型字段。所以不会对搜索条件分词。常见的有：

term：根据词条精确值查询
range：根据值的范围查询

term查询

因为精确查询的字段搜是不分词的字段，因此查询的条件也必须是不分词的词条。查询时，用户输入的内容跟自动值完全匹配时才认为符合条件。如果用户输入的内容过多，反而搜索不到数据。
语法说明：

// term查询
GET /indexName/_search
{
  "query": {
    "term": {
      "FIELD": {
        "value": "VALUE"
      }
    }
  }
}

示例
当我搜索的是精确词条时，能正确查询出结果：
但是，当我搜索的内容不是词条，而是多个词语形成的短语时，反而搜索不到：

range查询

范围查询，一般应用在对数值类型做范围过滤的时候。比如做价格范围过滤。
基本语法：

// range查询
GET /indexName/_search
{
  "query": {
    "range": {
      "FIELD": {
        "gte": 10, // 这里的gte代表大于等于，gt则代表大于
        "lte": 20 // lte代表小于等于，lt则代表小于
      }
    }
  }
}

示例：

总结

精确查询常见的有哪些？

term查询：根据词条精确匹配，一般搜索keyword类型、数值类型、布尔类型、日期类型字段
range查询：根据数值范围查询，可以是数值、日期的范围

地理坐标查询

所谓的地理坐标查询，其实就是根据经纬度查询，官方文档：https://www.elastic.co/guide/en/elasticsearch/reference/current/geo-queries.html
常见的使用场景包括：

携程：搜索我附近的酒店
滴滴：搜索我附近的出租车
微信：搜索我附近的人

附近的酒店：

矩形范围查询

矩形范围查询，也就是geo_bounding_box查询，查询坐标落在某个矩形范围的所有文档：
查询时，需要指定矩形的左上、右下两个点的坐标，然后画出一个矩形，落在该矩形内的都是符合条件的点。
语法如下：

// geo_bounding_box查询
GET /indexName/_search
{
  "query": {
    "geo_bounding_box": {
      "FIELD": {
        "top_left": { // 左上点
          "lat": 31.1,
          "lon": 121.5
        },
        "bottom_right": { // 右下点
          "lat": 30.9,
          "lon": 121.7
        }
      }
    }
  }
}

这种并不符合“附近的人”这样的需求。

附近查询

附近查询，也叫做距离查询（geo_distance）：查询到指定中心点小于某个距离值的所有文档。

换句话来说，在地图上找一个点作为圆心，以指定距离为半径，画一个圆，落在圆内的坐标都算符合条件：
语法说明：

// geo_distance 查询
GET /indexName/_search
{
  "query": {
    "geo_distance": {
      "distance": "15km", // 半径
      "FIELD": "31.21,121.5" // 圆心
    }
  }
}

示例：
我们先搜索陆家嘴附近15km的酒店：
发现共有47家酒店。
然后把半径缩短到3公里：
可以发现，搜索到的酒店数量减少到了5家。

复合查询

复合（compound）查询：复合查询可以将其它简单查询组合起来，实现更复杂的搜索逻辑。常见的有两种：

fuction score：算分函数查询，可以控制文档相关性算分，控制文档排名
bool query：布尔查询，利用逻辑关系组合多个其它的查询，实现复杂搜索

算分函数查询

根据相关度打分是比较合理的需求，但合理的不一定是产品经理需要的。
以百度为例，你搜索的结果中，并不是相关度越高排名越靠前，而是谁掏的钱多排名就越靠前。如图：

要想认为控制相关性算分，就需要利用elasticsearch中的function score 查询了。

语法说明

function score 查询中包含四部分内容：

原始查询条件：query部分，基于这个条件搜索文档，并且基于BM25算法给文档打分，原始算分（query score)
过滤条件：filter部分，符合该条件的文档才会重新算分
算分函数：符合filter条件的文档要根据这个函数做运算，得到的函数算分（function score），有四种函数
- weight：函数结果是常量
- field_value_factor：以文档中的某个字段值作为函数结果
- random_score：以随机数作为函数结果
- script_score：自定义算分函数算法
运算模式：算分函数的结果、原始查询的相关性算分，两者之间的运算方式，包括：
- multiply：相乘
- replace：用function score替换query score
- 其它，例如：sum、avg、max、min

function score的运行流程如下：

1）根据原始条件查询搜索文档，并且计算相关性算分，称为原始算分（query score）
2）根据过滤条件，过滤文档
3）符合过滤条件的文档，基于算分函数运算，得到函数算分（function score）
4）将原始算分（query score）和函数算分（function score）基于运算模式做运算，得到最终结果，作为相关性算分。

因此，其中的关键点是：

过滤条件：决定哪些文档的算分被修改
算分函数：决定函数算分的算法
运算模式：决定最终算分结果

示例

需求：给“如家”这个品牌的酒店排名靠前一些
翻译一下这个需求，转换为之前说的四个要点：

原始条件：不确定，可以任意变化
过滤条件：brand = “如家”
算分函数：可以简单粗暴，直接给固定的算分结果，weight
运算模式：比如求和

因此最终的DSL语句如下：

GET /hotel/_search
{
  "query": {
    "function_score": {
      "query": {  .... }, // 原始查询，可以是任意条件
      "functions": [ // 算分函数
        {
          "filter": { // 满足的条件，品牌必须是如家
            "term": {
              "brand": "如家"
            }
          },
          "weight": 2 // 算分权重为2
        }
      ],
      "boost_mode": "sum" // 加权模式，求和
    }
  }
}

测试，在未添加算分函数时，如家得分如下：

GET /hotel/_search
{
    "query":{
        "function_score":{
            "query": {"match": {"all": "外滩"}},   
            "functions": [
                {
                    "filter": {"term": {"id": "1"}},
                    "weight": 10
                }
            ],
			"boost_mode": "multiply"
        }
    }
}

添加了算分函数后，如家得分就提升了：

Get /hotel/_search
{
    "query": {
        "function_score": {
            "query": {
                "match": {
                    "all": "外滩"
                }
            },
            "functions": [{
                    "filter": {
                        "term": {
                            "brand": "如家"
                        }
                    },
                    "weight": 10
                }
            ],
            "boost_mode": "sum"
        }
    }
}

小结

function score query定义的三要素是什么？

过滤条件：哪些文档要加分
算分函数：如何计算function score
加权方式：function score 与 query score如何运算

布尔查询

布尔查询是一个或多个查询子句的组合，每一个子句就是一个子查询。子查询的组合方式有：

must：必须匹配每个子查询，类似“与”
should：选择性匹配子查询，类似“或”
must_not：必须不匹配，不参与算分，类似“非”
filter：必须匹配，不参与算分

比如在搜索酒店时，除了关键字搜索外，我们还可能根据品牌、价格、城市等字段做过滤：

每一个不同的字段，其查询的条件、方式都不一样，必须是多个不同的查询，而要组合这些查询，就必须用bool查询了。
需要注意的是，搜索时，参与打分的字段越多，查询的性能也越差。因此这种多条件查询时，建议这样做：

搜索框的关键字搜索，是全文检索查询，使用must查询，参与算分
其它过滤条件，采用filter查询。不参与算分

语法示例：

GET /hotel/_search
{
  "query": {
    "bool": {
      "must": [
        {"term": {"city": "上海" }}
      ],
      "should": [
        {"term": {"brand": "皇冠假日" }},
        {"term": {"brand": "华美达" }}
      ],
      "must_not": [
        { "range": { "price": { "lte": 500 } }}
      ],
      "filter": [
        { "range": {"score": { "gte": 45 } }}
      ]
    }
  }
}

示例

需求：搜索名字包含“如家”，价格不高于400，在坐标31.21,121.5周围10km范围内的酒店。
分析：

名称搜索，属于全文检索查询，应该参与算分。放到must中
价格不高于400，用range查询，属于过滤条件，不参与算分。放到must_not中
周围10km范围内，用geo_distance查询，属于过滤条件，不参与算分。放到filter中

GET /hotel/_search
{
    "query": {
        "bool": {
            "must": {
                "match": {
                    "name": "如家"
                }
            },
            "must_not": [
                {
                    "range": {
                        "price": {
                            "gt": 400
                        }
                    }
                }
            ],
            "filter": [
                {
                    "geo_distance": {
                        "distance": "10km",
                        "location": {
                            "lat": 31.21,
                            "lon": 121.5
                        }
                    }
                }
            ]
        }
    }
}

小结

bool查询有几种逻辑关系？

must：必须匹配的条件，可以理解为“与”
should：选择性匹配的条件，可以理解为“或”
must_not：必须不匹配的条件，不参与打分
filter：必须匹配的条件，不参与打分

搜索结果处理

搜索的结果可以按照用户指定的方式去处理或展示。

排序

elasticsearch默认是根据相关度算分（_score）来排序，但是也支持自定义方式对搜索结果排序。可以排序字段类型有：keyword类型、数值类型、地理坐标类型、日期类型等。

普通字段排序

keyword、数值、日期类型排序的语法基本一致。
语法：

GET /indexName/_search
{
  "query": {
    "match_all": {}
  },
  "sort": [
    {
      "FIELD": "desc"  // 排序字段、排序方式ASC、DESC
    }
  ]
}

排序条件是一个数组，也就是可以写多个排序条件。按照声明的顺序，当第一个条件相等时，再按照第二个条件排序，以此类推
示例：
需求描述：酒店数据按照用户评价（score)降序排序，评价相同的按照价格(price)升序排序

GET /hotel/_search
{
    "query": {
        "match_all": {}
    },
    "sort": [
        {
            "score": "desc"
        },
        {
            "price": "asc"
        }
    ]
}

地理坐标排序

地理坐标排序略有不同。
语法说明：

GET /indexName/_search
{
  "query": {
    "match_all": {}
  },
  "sort": [
    {
      "_geo_distance" : {
          "FIELD" : "纬度，经度", // 文档中geo_point类型的字段名、目标坐标点
          "order" : "asc", // 排序方式
          "unit" : "km" // 排序的距离单位
      }
    }
  ]
}

这个查询的含义是：

指定一个坐标，作为目标点
计算每一个文档中，指定字段（必须是geo_point类型）的坐标到目标点的距离是多少
根据距离排序

示例：
需求描述：实现对酒店数据按照到你的位置坐标的距离升序排序
提示：获取你的位置的经纬度的方式：https://lbs.amap.com/demo/jsapi-v2/example/map/click-to-get-lnglat/
假设我的位置是：31.034661，121.612282，寻找我周围距离最近的酒店。

GET /hotel/_search
{
    "query": {
        "match_all": {}
    },
    "sort": [{
            "_geo_distance": {
                "location": {
                    "lat": 31.034661,
                    "lon": 121.612282
                },
                "order": "asc",
                "unit": "km"
            }
        }
    ]
}

分页

elasticsearch 默认情况下只返回top10的数据。而如果要查询更多数据就需要修改分页参数了。elasticsearch中通过修改from、size参数来控制要返回的分页结果：

from：从第几个文档开始
size：总共查询几个文档

类似于mysql中的limit ?, ?

基本的分页

分页的基本语法如下：

GET /hotel/_search
{
  "query": {
    "match_all": {}
  },
  "from": 0, // 分页开始的位置，默认为0
  "size": 10, // 期望获取的文档总数
  "sort": [
    {"price": "asc"}
  ]
}

GET /hotel/_search
{
    "query": {
        "match_all": {}
    },
    "sort": [
        {
            "price": "asc"
        }
    ],
    "from": 0,
    "size": 10
}

// 如果你要查看第二页的数据， 就把 from 改成 10， 
// 公式是 from : (page - 1) * size

深度分页问题

现在，我要查询990~1000的数据，查询逻辑要这么写：

GET /hotel/_search
{
  "query": {
    "match_all": {}
  },
  "from": 990, // 分页开始的位置，默认为0
  "size": 10, // 期望获取的文档总数
  "sort": [
    {"price": "asc"}
  ]
}

这里是查询990开始的数据，也就是第990~第1000条数据。
不过，elasticsearch内部分页时，必须先查询 0~1000条，然后截取其中的990 ~ 1000的这10条：

查询TOP1000，如果es是单点模式，这并无太大影响。
但是elasticsearch将来一定是集群，例如我集群有5个节点，我要查询TOP1000的数据，并不是每个节点查询200条就可以了。
因为节点A的TOP200，在另一个节点可能排到10000名以外了。
因此要想获取整个集群的TOP1000，必须先查询出每个节点的TOP1000，汇总结果后，重新排名，重新截取TOP1000。

那如果我要查询9900~10000的数据呢？是不是要先查询TOP10000呢？那每个节点都要查询10000条？汇总到内存中？
当查询分页深度较大时，汇总数据过多，对内存和CPU会产生非常大的压力，因此elasticsearch会禁止from+ size 超过10000的请求。
针对深度分页，ES提供了两种解决方案，官方文档：

search after：分页时需要排序，原理是从上一次的排序值开始，查询下一页数据。官方推荐使用的方式。
scroll：原理将排序后的文档id形成快照，保存在内存。官方已经不推荐使用。

小结

分页查询的常见实现方案以及优缺点：

from + size：
- 优点：支持随机翻页
- 缺点：深度分页问题，默认查询上限（from + size）是10000
- 场景：百度、京东、谷歌、淘宝这样的随机翻页搜索
after search：
- 优点：没有查询上限（单次查询的size不超过10000）
- 缺点：只能向后逐页查询，不支持随机翻页
- 场景：没有随机翻页需求的搜索，例如手机向下滚动翻页
scroll：
- 优点：没有查询上限（单次查询的size不超过10000）
- 缺点：会有额外内存消耗，并且搜索结果是非实时的
- 场景：海量数据的获取和迁移。从ES7.1开始不推荐，建议用 after search方案。

高亮

高亮原理

什么是高亮显示呢？
我们在百度，京东搜索时，关键字会变成红色，比较醒目，这叫高亮显示：

高亮显示的实现分为两步：

1）给文档中的所有关键字都添加一个标签，例如标签
2）页面给标签编写CSS样式

实现高亮

高亮的语法：

GET /hotel/_search
{
  "query": {
    "match": {
      "FIELD": "TEXT" // 查询条件，高亮一定要使用全文检索查询
    }
  },
  "highlight": {
    "fields": { // 指定要高亮的字段
      "FIELD": {
        "pre_tags": "<em>",  // 用来标记高亮字段的前置标签
        "post_tags": "</em>" // 用来标记高亮字段的后置标签
      }
    }
  }
}

注意：

高亮是对关键字高亮，因此搜索条件必须带有关键字，而不能是范围这样的查询。
默认情况下，高亮的字段，必须与搜索指定的字段一致，否则无法高亮
如果要对非搜索字段高亮，则需要添加一个属性：required_field_match=false

示例：

GET /hotel/_search
{
    "query": {
        "match": {
            "all": "如家"
        }
    },
    "highlight": {
        "fields": {
            "name": {
                "require_field_match": "false"
            }
        }
    }
}

总结

查询的DSL是一个大的JSON对象，包含下列属性：

query：查询条件
from和size：分页条件
sort：排序条件
highlight：高亮条件

示例：

数据聚合

聚合（aggregations ）可以让我们极其方便的实现对数据的统计、分析、运算。例如：

什么品牌的手机最受欢迎？
这些手机的平均价格、最高价格、最低价格？
这些手机每月的销售情况如何？

实现这些统计功能的比数据库的sql要方便的多，而且查询速度非常快，可以实现近实时搜索效果。

聚合的种类

聚合常见的有三类：

**桶（Bucket）**聚合：用来对文档做分组
- TermAggregation：按照文档字段值分组，例如按照品牌值分组、按照国家分组
- Date Histogram：按照日期阶梯分组，例如一周为一组，或者一月为一组
**度量（Metric）**聚合：用以计算一些值，比如：最大值、最小值、平均值等
- Avg：求平均值
- Max：求最大值
- Min：求最小值
- Stats：同时求max、min、avg、sum等
**管道（pipeline）**聚合：其它聚合的结果为基础做聚合

**注意：**参加聚合的字段必须是keyword、日期、数值、布尔类型

DSL实现聚合

现在，我们要统计所有数据中的酒店品牌有几种，其实就是按照品牌对数据分组。此时可以根据酒店品牌的名称做聚合，也就是Bucket聚合。

Bucket聚合语法

语法如下：

GET /hotel/_search
{
  "size": 0,  // 设置size为0，结果中不包含文档，只包含聚合结果
  "aggs": { // 定义聚合
    "brandAgg": { //给聚合起个名字
      "terms": { // 聚合的类型，按照品牌值聚合，所以选择term
        "field": "brand", // 参与聚合的字段
        "size": 20 // 希望获取的聚合结果数量
      }
    }
  }
}

结果如图：

聚合结果排序

默认情况下，Bucket聚合会统计Bucket内的文档数量，记为_count，并且按照_count降序排序。
我们可以指定order属性，自定义聚合的排序方式：

GET /hotel/_search
{
  "size": 0, 
  "aggs": {
    "brandAgg": {
      "terms": {
        "field": "brand",
        "order": {
          "_count": "asc" // 按照_count升序排列
        },
        "size": 20
      }
    }
  }
}

限定聚合范围

默认情况下，Bucket聚合是对索引库的所有文档做聚合，但真实场景下，用户会输入搜索条件，因此聚合必须是对搜索结果聚合。那么聚合必须添加限定条件。
我们可以限定要聚合的文档范围，只要添加query条件即可：

GET /hotel/_search
{
  "query": {
    "range": {
      "price": {
        "lte": 200 // 只对200元以下的文档聚合
      }
    }
  }, 
  "size": 0, 
  "aggs": {
    "brandAgg": {
      "terms": {
        "field": "brand",
        "size": 20
      }
    }
  }
}

这次，聚合得到的品牌明显变少了：

Metric聚合语法

上节课，我们对酒店按照品牌分组，形成了一个个桶。现在我们需要对桶内的酒店做运算，获取每个品牌的用户评分的min、max、avg等值。
这就要用到Metric聚合了，例如stat聚合：就可以获取min、max、avg等结果。
语法如下：

GET /hotel/_search
{
  "size": 0, 
  "aggs": {
    "brandAgg": { 
      "terms": { 
        "field": "brand", 
        "size": 20
      },
      "aggs": { // 是brands聚合的子聚合，也就是分组后对每组分别计算
        "score_stats": { // 聚合名称
          "stats": { // 聚合类型，这里stats可以计算min、max、avg等
            "field": "score" // 聚合字段，这里是score
          }
        }
      }
    }
  }
}

这次的score_stats聚合是在brandAgg的聚合内部嵌套的子聚合。因为我们需要在每个桶分别计算。
另外，我们还可以给聚合结果做个排序，例如按照每个桶的酒店平均分做排序：

小结

aggs代表聚合，与query同级，此时query的作用是？

限定聚合的的文档范围

聚合必须的三要素：

聚合名称
聚合类型
聚合字段

聚合可配置属性有：

size：指定聚合结果数量
order：指定聚合结果排序方式
field：指定聚合字段

标签：name,brand,基础,id,score,elasticsearch,使用,com,price
From： https://blog.csdn.net/qq_27352081/article/details/137120951