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+# ES-高级DSL查询
+
+[TOC]
+
+# 1.DSL查询文档
+
+elasticsearch的查询依然是基于JSON风格的DSL来实现的。
+
+下方所有示例所采用索引库结构：
+
+```apl
+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"
+      },
+      "score":{
+        "type": "integer"
+      },
+      "brand":{
+        "type": "keyword",
+        "copy_to": "all"
+      },
+      "city":{
+        "type": "keyword",
+        "copy_to": "all"
+      },
+      "starName":{
+        "type": "keyword"
+      },
+      "business":{
+        "type": "keyword"
+      },
+      "location":{
+        "type": "geo_point"
+      },
+      "pic":{
+        "type": "keyword",
+        "index": false
+      },
+      "all":{
+        "type": "text",
+        "analyzer": "ik_max_word"
+      }
+    }
+  }
+}
+```
+
+## 1.1.DSL查询分类
+
+Elasticsearch提供了基于JSON的DSL（[Domain Specific Language](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html)）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：
+
+```json
+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
+
+```json
+// 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。
+
+## 1.2.全文检索查询
+
+### 1.2.1.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：
+
+![image-20210721165326938](assets/image-20210721165326938.png)
+
+
+
+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。
+
+### 1.2.2.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：
+
+```json
+GET /indexName/_search
+{
+  "query": {
+    "match": {
+      "FIELD": "TEXT"
+    }
+  }
+}
+```
+
+mulit_match语法如下：
+
+```json
+GET /indexName/_search
+{
+  "query": {
+    "multi_match": {
+      "query": "TEXT",
+      "fields": ["FIELD1", " FIELD12"]
+    }
+  }
+}
+```
+
+### 1.2.3.示例
+
+match查询示例：
+
+![image-20210721170455419](assets/image-20210721170455419.png)
+
+
+
+multi_match查询示例：
+
+![image-20210721170720691](assets/image-20210721170720691.png)
+
+
+
+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。
+
+
+
+### 1.2.4.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
+
+
+
+## 1.3.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
+
+
+
+### 1.3.1.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：
+
+```json
+// 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：
+
+![image-20210721171655308](assets/image-20210721171655308.png)
+
+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：
+
+![image-20210721171838378](assets/image-20210721171838378.png)
+
+
+
+### 1.3.2.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：
+
+```json
+// 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：
+
+![image-20210721172307172](assets/image-20210721172307172.png)
+
+### 1.3.3.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
+
+## 1.4.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：
+
+![image-20210721172645103](assets/image-20210721172645103.png) 
+
+附近的车：
+
+![image-20210721172654880](assets/image-20210721172654880.png) 
+
+
+
+### 1.4.1.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：
+
+![DKV9HZbVS6](assets/DKV9HZbVS6.gif)
+
+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：
+
+```json
+// 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，所以我们就不做了。
+
+### 1.4.2.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：
+
+![vZrdKAh19C](assets/vZrdKAh19C.gif)
+
+语法说明：
+
+```json
+// 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：
+
+![image-20210721175443234](assets/image-20210721175443234.png)
+
+发现共有47家酒店。
+
+## 1.5.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
+
+### 1.5.1.相关性算分
+
+当我们利用match查询时，文档结果会根据与搜索词条的关联度打分（_score），返回结果时按照分值降序排列。
+
+例如，我们搜索 "虹桥如家"，结果如下：
+
+```json
+[
+  {
+    "_score" : 17.850193,
+    "_source" : {
+      "name" : "虹桥如家酒店真不错",
+    }
+  },
+  {
+    "_score" : 12.259849,
+    "_source" : {
+      "name" : "外滩如家酒店真不错",
+    }
+  },
+  {
+    "_score" : 11.91091,
+    "_source" : {
+      "name" : "迪士尼如家酒店真不错",
+    }
+  }
+]
+```
+
+在elasticsearch中，早期使用的打分算法是TF-IDF算法，公式如下：
+
+![image-20210721190152134](assets/image-20210721190152134.png)
+
+在后来的5.1版本升级中，elasticsearch将算法改进为BM25算法，公式如下：
+
+![image-20210721190416214](assets/image-20210721190416214.png)
+
+
+
+TF-IDF算法有一各缺陷，就是词条频率越高，文档得分也会越高，单个词条对文档影响较大。而BM25则会让单个词条的算分有一个上限，曲线更加平滑：
+
+![image-20210721190907320](assets/image-20210721190907320.png)
+
+
+
+小结：elasticsearch会根据词条和文档的相关度做打分，算法由两种：
+
+- TF-IDF算法
+- BM25算法，elasticsearch5.1版本后采用的算法
+
+### 1.5.2.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：
+
+![image-20210721191144560](assets/image-20210721191144560.png)
+
+
+
+要想认为控制相关性算分，就需要利用elasticsearch中的function score 查询了。
+
+#### 1）语法说明
+
+![image-20210721191544750](assets/image-20210721191544750.png)
+
+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
+
+#### 2）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：
+
+```json
+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：
+
+![image-20210721193152520](assets/image-20210721193152520.png)
+
+添加了算分函数后，如家得分就提升了：
+
+![image-20210721193458182](assets/image-20210721193458182.png)
+
+#### 3）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
+
+### 1.5.3.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：
+
+![image-20210721193822848](assets/image-20210721193822848.png)
+
+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
+
+#### 1）语法示例：
+
+```json
+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 } }}
+      ]
+    }
+  }
+}
+```
+
+#### 2）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
+
+
+
+![image-20210721194744183](assets/image-20210721194744183.png)
+
+#### 3）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
+
+# 2.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。
+
+## 2.1.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](https://www.elastic.co/guide/en/elasticsearch/reference/current/sort-search-results.html)。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。
+
+### 2.1.1.普通字段排序
+
+keyword、数值、日期类型排序的语法基本一致。
+
+**语法**：
+
+```json
+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
+
+![image-20210721195728306](assets/image-20210721195728306.png)
+
+
+
+### 2.1.2.地理坐标排序
+
+地理坐标排序略有不同。
+
+**语法说明**：
+
+```json
+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。
+
+![image-20210721200214690](assets/image-20210721200214690.png)
+
+## 2.2.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 ?, ?`
+
+### 2.2.1.基本的分页
+
+分页的基本语法如下：
+
+```json
+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"}
+  ]
+}
+```
+
+### 2.2.2.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：
+
+```json
+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：
+
+![image-20210721200643029](assets/image-20210721200643029.png)
+
+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。
+
+![image-20210721201003229](assets/image-20210721201003229.png)
+
+
+
+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](https://www.elastic.co/guide/en/elasticsearch/reference/current/paginate-search-results.html)：
+
+- 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。
+
+### 2.2.3.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。
+
+## 2.3.高亮
+
+### 2.3.1.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：
+
+![image-20210721202705030](assets/image-20210721202705030.png)
+
+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`<em>`标签
+- 2）页面给`<em>`标签编写CSS样式
+
+### 2.3.2.实现高亮
+
+**高亮的语法**：
+
+```json
+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**：
+
+![image-20210721203349633](assets/image-20210721203349633.png)
+
+
+
+## 2.4.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：
+
+![image-20210721203657850](assets/image-20210721203657850.png)
+
+# 3.示例汇总
+
+```apl
+#查询全部
+GET /hotel/_search
+{
+  "query": {
+    "match_all": {}
+  }
+}
+
+#match
+GET /hotel/_search
+{
+  "query": {
+    "match": {
+      "all": "上海"
+    }
+  }
+}
+
+#multi_match
+GET /hotel/_search
+{
+  "query": {
+    "multi_match": {
+      "query": "北京",
+      "fields": ["name","city"]
+    }
+  }
+}
+#term
+GET /hotel/_search
+{
+  "query": {
+    "term": {
+      "city": {
+        "value": "上海"
+      }
+    }
+  }
+}
+#terms
+GET /hotel/_search
+{
+  "query": {
+    "terms": {
+      "brand": [
+        "速8",
+        "如家"
+      ]
+    }
+  }
+}
+#range
+GET /hotel/_search
+{
+  "query": {
+    "range": {
+      "price": {
+        "gte": 300,
+        "lte": 500
+      }
+    }
+  }
+}
+#geo_bounding_box
+GET /hotel/_search
+{
+  "query": {
+    "geo_bounding_box":{
+      "location":{
+        "top_left":{
+          "lat":31.1,
+          "lon":121.5
+        },
+        "bottom_right":{
+          "lat":30.9,
+          "lon":121.7
+        }
+      }
+    }
+  }
+}
+#geo_distance
+GET /hotel/_search
+{
+  "query": {
+    "geo_distance":{
+      "distance":"15km",
+      "location": "31.21,121.5"
+    }
+  }
+}
+GET /hotel/_search
+{
+  "query": {
+    "geo_distance":{
+      "distance":"15km",
+      "location": {
+        "lat":"31.21",
+        "lon":"121.5"
+      }
+    }
+  }
+}
+#function_score
+GET /hotel/_search
+{
+  "query": {
+    "function_score": {
+      "query": {"term": {"city": {"value": "北京"}}},
+      "functions": [
+        {
+          "filter": {"term": {"id": "394617"}},
+          "weight": 10
+        }
+      ],
+      "boost_mode": "multiply" 
+    }
+  }
+}
+#bool
+GET /hotel/_search
+{
+  "query": {
+    "bool": {
+      "must": [
+        {
+          "match": {
+            "name": "如家"
+          }
+        }
+      ],
+      "should": [
+        {
+          "term": {
+            "city": {
+              "value": "深圳"
+            }
+          }
+        },
+        {
+          "match": {
+            "all": "城"
+          }
+        }
+      ],
+      "must_not": [
+        {
+          "term": {
+            "city": {
+              "value": "北京"
+            }
+          }
+        }
+      ],
+      "filter": [
+        {
+          "range": {
+            "score": {
+              "gte": 40,
+              "lte": 50
+            }
+          }
+        }
+      ]
+    }
+  }
+}
+#sort
+GET /hotel/_search
+{
+  "query": {"match_all": {}}, 
+  "sort": [
+    {
+      "score": {
+        "order": "desc"
+      }
+    },
+    {
+      "price": "asc"
+    }
+  ]
+}
+
+#sort _geo_distance
+GET /hotel/_search
+{
+  "query": {"match_all": {}},
+  "sort": [
+    {
+      "_geo_distance": {
+        "location": {
+          "lat": 40.082017,
+          "lon": 116.411252
+        }, 
+        "order": "asc",
+        "unit": "km"
+      }
+    }
+  ]
+}
+GET /hotel/_search
+{
+  "query": {"match_all": {}},
+  "sort": [
+    {
+      "_geo_distance": {
+        "location": "40.082017,116.411252",
+        "order": "asc",
+        "unit": "km"
+      }
+    }
+  ]
+}
+#分页
+GET /hotel/_search
+{
+  "query": {"match_all": {}},
+  "from": 0,
+  "size": 2,
+  "sort": [
+    {
+      "price": {
+        "order": "desc"
+      }
+    }
+  ]
+}
+#高亮
+GET /hotel/_search
+{
+  "query": {
+    "match": {
+      "all": "如家"
+    }
+  },
+  "highlight": {
+    "fields": {
+      "name": {
+        "pre_tags": "<em>",
+        "post_tags": "</em>",
+        "require_field_match": "false"
+      }
+    }
+  }
+}
+```
+
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diff --git a/elk/elasticsearch/基础ES-DSL语句.md b/elk/elasticsearch/基础ES-DSL语句.md
index 1e2454a..4c24bcc 100644
--- a/elk/elasticsearch/基础ES-DSL语句.md
+++ b/elk/elasticsearch/基础ES-DSL语句.md
@@ -1,6 +1,8 @@
 # 基础索引库、文档ES-DSL语句
 
-## 基础概念
+[TOC]
+
+# 基础概念
 
 我们把mysql与elasticsearch的概念做一下对比：
 
@@ -26,9 +28,9 @@
 - 对查询性能要求较高的搜索需求，使用elasticsearch实现
 - 两者再基于某种方式，实现数据的同步，保证一致性
 
-## 基础语法
+# 基础语法
 
-### 索引基础语法
+## 索引基础语法
 
 - 创建索引库：PUT /索引库名
 - 查询索引库：GET /索引库名
@@ -37,7 +39,7 @@
   - PUT /索引库名/_mapping 
   - POST /索引库名/_mapping
 
-### 文档基础语法
+## 文档基础语法
 
 - 创建文档：POST /{索引库名}/_doc/文档id   { json文档 }
 - 查询文档：GET /{索引库名}/_doc/文档id
@@ -46,7 +48,7 @@
   - 全量修改 (如果索引库中不存在则新增)：PUT /{索引库名}/_doc/文档id { json文档 }
   - 局部修改(如果索引库中不存在则修改失败)：POST /{索引库名}/_update/文档id { "doc": {字段}}
 
-## 示例
+# 示例
 
 ```apl
 #分词器