Elasticsearch使用系列-ES增删查改基本操作+ik分词
Elasticsearch使用系列-基本查询和聚合查询+sql插件
Elasticsearch使用系列-.NET6对接Elasticsearch
Elasticsearch使用系列-Docker搭建Elasticsearch集群
一、基本查询
1.And查询must
GET user2/_search { "query": { "bool":{ "must": [ { "match": { "name": "张三" } }, { "match": { "hobby": "钓鱼" } } ] } }, "_source": ["name","age","hobby"], "sort": [ { "age": {"order": "desc"}}, { "name2": {"order": "asc"}} ], "from": 0, "size": 20 }
- bool ：And查询属于bool查询，must里面带And的查询条件。
- _source：要查询的字段
- sort：对查询结果排序
- from：分页查询，跳过多少条
- size：分页查询，一页查多少条
2.or查询should
GET user2/_search { "query": { "bool":{ "should": [ { "match": { "name": "张三" } }, { "match": { "hobby": "钓鱼" } } ] } } }
- bool：or查询属于bool查询
- should：里面放or的查询条件
3.排除查询 must_not
#查询名字不等于张三 GET user2/_search { "query": { "bool":{ "must_not": [ { "match": { "name": "张三" } } ] } } }
4.过滤查询filter
#查询名字等于张三，年龄大于等于10小于等于20 GET user2/_search { "query": { "bool":{ "must": [ { "match": { "name": "张三" } } ], "filter": [ {"range": { "age": { "gte": 10, "lte": 20 } }} ] } } }
- filter：过滤条件，先过滤数据再查询结果
- range：范围查询，和term,match是同类的查询。
- gte：大于等于
- gt：大于
- lte：小于等于
- lt：小于
5.同字段多值查询
GET user2/_search { "query": { "terms": { "name2": ["张三","李四"] } } }
#text类型的多值查询，空格隔开 GET user2/_search { "query": { "match": { "name": "张三 李四" } } }
6.高亮查询highlight
高亮查询，就是平时搜索东西时，搜索结果会把你的关键词匹配到的显示颜色，像下图一样。
高亮展示的数据，本身就是文档中的一个field，单独将field以highlight的形式返回给你。
ES提供了一个highlight属性，和query同级别的。
- pre_tag：指定前缀标签，如
<font color="red"> - post_tags：指定后缀标签，如
</font> - fields：指定那个字段为高亮字段
查出来后，显示hobby字段的地方，就直接用高亮的hobby展示就行了。
二、聚合查询
bucket：分组后统计，类似于Mysql中的group by
metric：对分组统计的结果，计算最大值，最小值，平均值等，类似于Mysql中的max()，min()，avg()函数的值。
1.准备数据
创建索引
PUT employee { "mappings": { "properties": { "id": { "type": "integer" }, "name": { "type": "keyword" }, "job": { "type": "keyword" }, "age": { "type": "integer" }, "gender": { "type": "keyword" } } } }
批量插入数据
PUT employee/_bulk {"index": {"_id": 1}} {"id": 1, "name": "Bob", "job": "java", "age": 21, "sal": 8000, "gender": "male"} {"index": {"_id": 2}} {"id": 2, "name": "Rod", "job": "html", "age": 31, "sal": 18000, "gender": "female"} {"index": {"_id": 3}} {"id": 3, "name": "Gaving", "job": "java", "age": 24, "sal": 12000, "gender": "male"} {"index": {"_id": 4}} {"id": 4, "name": "King", "job": "dba", "age": 26, "sal": 15000, "gender": "female"} {"index": {"_id": 5}} {"id": 5, "name": "Jonhson", "job": "dba", "age": 29, "sal": 16000, "gender": "male"} {"index": {"_id": 6}} {"id": 6, "name": "Douge", "job": "java", "age": 41, "sal": 20000, "gender": "female"} {"index": {"_id": 7}} {"id": 7, "name": "cutting", "job": "dba", "age": 27, "sal": 7000, "gender": "male"} {"index": {"_id": 8}} {"id": 8, "name": "Bona", "job": "html", "age": 22, "sal": 14000, "gender": "female"} {"index": {"_id": 9}} {"id": 9, "name": "Shyon", "job": "dba", "age": 20, "sal": 19000, "gender": "female"} {"index": {"_id": 10}} {"id": 10, "name": "James", "job": "html", "age": 18, "sal": 22000, "gender": "male"} {"index": {"_id": 11}} {"id": 11, "name": "Golsling", "job": "java", "age": 32, "sal": 23000, "gender": "female"} {"index": {"_id": 12}} {"id": 12, "name": "Lily", "job": "java", "age": 24, "sal": 2000, "gender": "male"} {"index": {"_id": 13}} {"id": 13, "name": "Jack", "job": "html", "age": 23, "sal": 3000, "gender": "female"} {"index": {"_id": 14}} {"id": 14, "name": "Rose", "job": "java", "age": 36, "sal": 6000, "gender": "female"} {"index": {"_id": 15}} {"id": 15, "name": "Will", "job": "dba", "age": 38, "sal": 4500, "gender": "male"} {"index": {"_id": 16}} {"id": 16, "name": "smith", "job": "java", "age": 32, "sal": 23000, "gender": "male"}
2.分组统计
查询员工各种语言数量，相当于group by
#查询员工各种语言数量 GET employee/_search { "size": 0, "aggs": { "languge_count": { "terms": { "field": "job" } } } }
- size：0表示只要统计后的结果，原始数据不展现，如果是大于0，则会返回多少条原始数据
- aggs:固定语法
- languge_count：自定义的分组名称，可以随便写
- terms：按什么字段进行分组
- field：具体的字段名称
3.平均值，最大值，最小值，求和统计
GET employee/_search { "size": 0, "aggs": { "language_count": { "terms": { "field": "job" }, "aggs":{ "age_avg":{ "avg":{ "field": "age" } } } } } }
- aggs：固定写法
- age_avg：自定义统计名称，随便写
- avg：平均值，其他有 max：最大值，min：最小值，sum：求和
- fileld：要计算的字段
4.分段统计
#按年龄区间分段统计 GET employee/_search { "size": 0, "aggs": { "language_count": { "histogram": { "field": "age", "interval": 10 }, "aggs":{ "age_avg":{ "sum":{ "field": "age" } } } } } }
- histogram：分段统计
- interval：分段间隔
5.日期分段统计
#按月份统计生日人数 GET employee/_search { "size": 0, "aggs": { "language_count": { "date_histogram": { "field": "borthday", "interval": "month", "format": "yyyy-MM-dd", "min_doc_count": 0, "extended_bounds": { "min": "1970-10-01", "max": "2022-12-31" } } } } }
- date_histogram：日期分段统计函数
- field：聚合分组的字段，类型需要为date
- interval：按什么时间聚合，interval字段支持多种关键字：year, quarter(季度), month, week, day, hour, minute, second，
- format：返回值格式化
- min_doc_count：0分组后没数据的也显示，最小有多少条才显示
- extended_bounds：强制规定最小值和最大值界限，ES默认把有数据的最小值开始做开始界限
6.同时统计多个集合
#分别统计年龄和性别 GET employee/_search { "size": 0, "aggs": { "language_count": { "histogram": { "field": "age", "interval": 10 }, "aggs":{ "age_avg":{ "sum":{ "field": "age" } } } }, "gender_count":{ "terms": { "field": "gender" } } } }
三、sql插件
1.插件安装
上面的查询语句为DSL查询，sql插件可以编写sql语句，然后自动解析为DSL语句查询
sql插件github地址：https://github.com/NLPchina/elasticsearch-sql
下载的对应es的版本。
解压后放到 plugins 文件夹并改名为sql，然后重启es
2.sql语句查询
2.1普通查询
GET /_sql?format=txt { "query": "select * from employee where job='java'" }
2.2其他查询写法
#普通查询 SELECT * FROM bank WHERE age >30 AND gender = 'm'
#聚合查询（分组统计） select COUNT(*),SUM(age),MIN(age) as m, MAX(age),AVG(age) FROM bank GROUP BY gender ORDER BY SUM(age), m DESC
#删除 DELETE FROM bank WHERE age >30 AND gender = 'm'
更多的查询看sql插件的github地址最下面的说明