目录
查询
创建
删除请求
1. 删除索引
数据类型
一、创建索引
1.1 创建book_test索引
1.2 查看索引映射
1.3 添加文档记录
1.4 查询文档
1.6 删除文档中某一条数据
二、查询语法
2.1 term&terms查询
2.2 match查询属于高层查询，他会根据你查询的字段类型不一样，采用不同的查询方式，
3.1 分词效果分析
4.1 其他查询
4.1.1 根据单个id查询
4.1.2 # 查询多个id返回结果
4.2.1 prefix查询
4.3 fuzzy查询 模糊查询，我们输入字符的大概，ES就可以根据输入的内容大概去匹配结果。但是查询结果不太稳定
4.4 wildcard 查询
4.5 range范围查询
4.6 正则查询，通过你编写的正则表达式去匹配内容
4.7 scroll 深分页
4.8 delete-by-query 根据查询删除
4.9 复合查询，
4.9 boosting查询
4.10 filter查询
4.11 高亮查询
4.12 聚合查询，多种多样的聚合
查询
GET请求 ，参数只能在路径上
POST请求，也是 查询，参数可以放在请求体中，也可以更新文档，指定type=update
创建
PUT请求
创建索引，需要在请求体中指定分片信息，索引信息
http://ip:port/index/type/_mapping 创建索引时，指定索引文档的属性
删除请求
DELETE
http://ip:port/index 删除整个文档，==删除库
1. 删除索引
DELETE /user_index
数据类型
https://www.elastic.co/guide/en/elasticsearch/reference/current/mapping-types.html
feild
String
text：分词，可以放一段话
keyword: 不会被分词 ，比如地区--北京，上海
时间类型
date :指定格式
数值类型
布尔类型
Boolean
二进制类型
范围类型
经纬度
"type": "geo_point"
一、创建索引
1.1 创建book_test索引
PUT /book_test
{
"settings": {
"number_of_replicas": 1,
"number_of_shards": 1
},
"mappings": {
"noval":{ # 执行文档类型 在es8以上没有这个了。需要删掉，否则会出错
"properties":{ # 指定文档属性
"name":{ # 指定属性名
"type":"text",
"index":true,
"analyzer": "ik_smart",
"store": true
},
"author":{
"type":"keyword"
},
"count":{
"type":"long"
},
"on_sale":{
"type":"date",
"format": "yyyy-MM-dd HH:mm:ss||yyyy-MM-dd||epoch_millis"
},
"desc":{
"type":"text",
"analyzer":"ik_max_word"
}
}
}
}
}
1.1 创建成功响应结果
{
"acknowledged" : true,
"shards_acknowledged" : true,
"index" : "book_test"
}
1.2 查看索引映射
GET /book_test/_mapping
1.2 响应结果
{
"book_test" : {
"mappings" : {
"properties" : {
"author" : {
"type" : "keyword"
},
"count" : {
"type" : "long"
},
"desc" : {
"type" : "text",
"analyzer" : "ik_max_word"
},
"name" : {
"type" : "text",
"store" : true,
"analyzer" : "ik_smart"
},
"on_sale" : {
"type" : "date",
"format" : "yyyy-MM-dd HH:mm:ss||yyyy-MM-dd||epoch_millis"
}
}
}
}
}
1.3 添加文档记录
POST /book_test/_doc/2 # 这是指定id，没有/2自动生成id。带有id多次操作是修改
{
"name":"西游记",
"author":"张三2",
"count":"1565",
"on_sale":"2022-08-30"
,"desc":"了接口直接裂开房价高了几十个"
}
响应 带有id去更新操作
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "2",
"_version" : 2,
"result" : "updated",
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 2,
"_primary_term" : 1
}
1.4 查询文档
GET /book_test/_search
响应
{
"took" : 0, # 查询耗时
"timed_out" : false, # 是否超时，这里没超时
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "23dxtoMBUPIMnS01cm85",
"_score" : 1.0,
"_source" : {
"name" : "我的小说",
"author" : "张三",
"count" : "125",
"on_sale" : "2022-09-30",
"desc" : "家乐鸡粉了垃圾的垃圾费附件覅额计入还让他了客户是"
}
},
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "2",
"_score" : 1.0,
"_source" : {
"name" : "西游记",
"author" : "张三2",
"count" : "1565",
"on_sale" : "2022-08-30",
"desc" : "了接口直接裂开房价高了几十个"
}
}
]
}
}
1.5 修改某个字段的值，根据id查询结果，修改一条
POST /book_test/_update_by_query
{
"script": {
"source": "ctx._source.name=params.name",
"params": {
"name":"修改西游记"
},
"lang": "painless"
}
, "query": {
"term": {
"_id": 2
}
}
}
修改某个字段的值，根据字段查询结果，修改多条
POST /book_test/_update_by_query
{
"script": {
"source": "ctx._source.name=params.name",
"params": {
"name":"修改西游记2"
},
"lang": "painless"
}
, "query": {
"term": {
"author" : "张三2"
}
}
}
文档方式修改单个字段
POST /book_test/_doc/2/_update
{
"doc":{
"count":"1222"
}
}
1.6 删除文档中某一条数据
删除id=_update的数据
DELETE /book_test/_doc/_update
响应结果
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "_update",
"_version" : 2,
"result" : "deleted",
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 11,
"_primary_term" : 1
}
二、查询语法
2.1 term&terms查询
虽然查询的词不会分词，但是搜索的时候，是用词，到分词库中匹配，匹配到就返回响应的数据，比如：一条数据的长文本里有奋斗的时代，分词后会产生奋斗、时代。我们用奋斗term查询的时候就会匹配到这条数据返回。
2.1.1 term查询
term 的查询代表的是完全匹配，搜索之前不会对你搜索的关键字进行分词，对你的关键字去文档分词库中去匹配内容。
查询语法
POST /book_test/_search
{
"from": 0,
"size": 20,
"query": {
"term": {
"author": {
"value": "李四"
}
}
}
}
响应结果
{
"took" : 402, # 耗时
"timed_out" : false, # 是否超时
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 1.3862942,
"hits" : [
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "4",
"_score" : 1.3862942,
"_source" : {
"name" : "西游记2",
"author" : "李四",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "发根深蒂固"
}
}
]
}
}
2.1.2 terms 也不会分词，针对某个字段，多个值进行匹配
查询语法
POST /book_test/_search
{
"from": 0,
"size": 20,
"query": {
"terms": {
"author": [
"张三",
"李四"
]
}
}
}
响应结果
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "23dxtoMBUPIMnS01cm85",
"_score" : 1.0,
"_source" : {
"name" : "我的小说",
"author" : "张三",
"count" : "125",
"on_sale" : "2022-09-30",
"desc" : "家乐鸡粉了垃圾的垃圾费附件覅额计入还让他了客户是"
}
},
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "4",
"_score" : 1.0,
"_source" : {
"name" : "西游记2",
"author" : "李四",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "发根深蒂固"
}
}
]
}
}
2.2 match查询属于高层查询，他会根据你查询的字段类型不一样，采用不同的查询方式，
查询日期或者是数据类型，他会将你基于字符串查询的内推转换为日期或者数值
如果查询的内容是一个不能被分词的内容（keyword），match查询不会对你指定的查询关键字进行分词
如果查询的内容是一个可以被分词的内容（text），match会将你指定的查询内容根据一定的方式去分词，去分词库中匹配指定的内容
总之，match查询，实际底层就是多个term查询，将多个term查询的结果给封装到一起，返回。
ES默认只查询10条数据，要想查询更多，需要指定size。
2.2.1 查询语法 分词匹配
POST /book_test/_search
{
"query": {
"match": {
"name": "西游记"
}
}
}
响应
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : 0.48120394,
"hits" : [
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "4",
"_score" : 0.48120394,
"_source" : {
"name" : "西游记2",
"author" : "李四",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "发根深蒂固"
}
},
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "3",
"_score" : 0.40841687,
"_source" : {
"author" : "张三2",
"name" : "修改西游记2",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "fsgdfgafad"
}
},
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "2",
"_score" : 0.40841687,
"_source" : {
"author" : "张三2",
"name" : "修改西游记2",
"count" : "1222",
"on_sale" : "2022-08-30",
"desc" : "了接口直接裂开房价高了几十个"
}
}
]
}
}
2.2.2 布尔match查询
查询一个字段，可以匹配多个字，关系可以是or 或者 and
POST /book_test/_search
{
"query": {
"match": {
"desc": {
"query": "京东 大家",
"operator": "or"
}
}
}
}
响应结果，评分越高，匹配度越高
{
"took" : 275,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 3.179408,
"hits" : [
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "5",
"_score" : 3.179408,
"_source" : {
"name" : "大话西游",
"author" : "李大四",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "京东卡拉拉LDA； 大家都发"
}
},
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "6",
"_score" : 1.5683775,
"_source" : {
"name" : "大话济公",
"author" : "小四",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "大家都发财"
}
}
]
}
}
2.3 multi_match查询
match针对一个feild做检索，multi_match针对多个feild进行检索，多个feild对应一个text。
3.1 分词效果分析
POST _analyze
{
"analyzer": "ik_max_word",
"text": ["笔记本电脑"]
}
4.1 其他查询
4.1.1 根据单个id查询
GET /book_test/_doc/2
GET /book_test/_search?q=_id:3.
4.1.2 # 查询多个id返回结果
GET /user_index/_doc/_mget
{
"ids":[1,2]
}
POST /book_test/_search
{
"query": {
"ids": {
"values": ["3","2"]
}
}
}
4.2.1 prefix查询
4.2.1.1 前缀查询，下面author类型是keyword，如果用match或者term匹配不到，用前缀查询就可以匹配到
POST /book_test/_search
{
"query": {
"prefix": {
"author": {
"value": "李"
}
}
}
}
前缀查询响应
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "4",
"_score" : 1.0,
"_source" : {
"name" : "西游记2",
"author" : "李四",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "发根深蒂固"
}
},
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "5",
"_score" : 1.0,
"_source" : {
"name" : "大话西游",
"author" : "李大四",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "京东卡拉拉LDA； 大家都发"
}
}
]
}
}
4.2.1.2 前缀匹配text类型的字段查询，匹配的是分词库--比如name
POST /book_test/_search
{
"query": {
"prefix": {
"name": {
"value": "西游"
}
}
}
}
分词前缀响应
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "3",
"_score" : 1.0,
"_source" : {
"author" : "张三2",
"name" : "修改西游记2",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "fsgdfgafad"
}
},
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "2",
"_score" : 1.0,
"_source" : {
"author" : "张三2",
"name" : "修改西游记2",
"count" : "1222",
"on_sale" : "2022-08-30",
"desc" : "了接口直接裂开房价高了几十个"
}
},
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "4",
"_score" : 1.0,
"_source" : {
"name" : "西游记2",
"author" : "李四",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "发根深蒂固"
}
}
]
}
}
4.3 fuzzy查询 模糊查询，我们输入字符的大概，ES就可以根据输入的内容大概去匹配结果。但是查询结果不太稳定
4.3.1 模糊查询也是---去分词库查
POST /book_test/_search
{
"query": {
"fuzzy": { # 模糊查询
"desc": { # 模糊字段
"value": "大家",
"prefix_length": 1 # 前边几个字不可以错的
}
}
}
}
模糊响应
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 1.691406,
"hits" : [
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "6",
"_score" : 1.691406,
"_source" : {
"name" : "大话济公",
"author" : "小四",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "大家都发财"
}
},
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "5",
"_score" : 1.386699,
"_source" : {
"name" : "大话西游",
"author" : "李大四",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "京东卡拉拉LDA； 大家都发"
}
}
]
}
}
4.4 wildcard 查询
通配符查询，和mysql中的like是一样，可以在查询时，在字符串中指定通配符*和占位符？
POST /book_test/_search
{
"query": {
"wildcard": {
"desc": {
"value": "*f*"
}
}
}
}
响应
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "3",
"_score" : 1.0,
"_source" : {
"author" : "张三2",
"name" : "修改西游记2",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "fsgdfgafad"
}
}
]
}
}
第二个测试
POST /book_test/_search
{
"query": {
"wildcard": {
"author": {
"value": "李??"
}
}
}
}
4.5 range范围查询
范围查询，只针对数值类型，对某一个feild进行大于或者小于的范围指定
大于gt； 小于lt
POST /book_test/_search
{
"query": {
"range": {
"count": {
"gte": 10,
"lte": 200
}
}
}
}
4.6 正则查询，通过你编写的正则表达式去匹配内容
ps：prefix，fuzzy，wildcard，regexp查询效率相对比较低，要求效率比较高时，避免去使用。
POST /book_test/_search
{
"query": {
"regexp": {
"name": "*[0-9]{1}"
}
}
}
4.7 scroll 深分页
ES对from+size是有限制的，from和size二者纸盒不能超过1万
原理：
from+size 的ES查询数据的方式
1、 先将用户指定的关键字进行分词
2、 将词汇去分词库中进行检索，得到多个文档的id
3、 去各个分片中去拉取指定的数据 # 这个过程有点慢的
4、 将数据分局score进行排序 # 这个慢
5、 根据from的值，将查询到的数据舍弃一部分
6、 返回结果
scroll 的ES查询数据的方式
1、 先将用户指定的关键字进行分词
2、 将词汇去分词库中进行检索，得到多个文档的id
3、 将文档的id存放在一个ES的上下文中
4、 根据你指定的size去ES中检索指定的数据，拿完数据的文档id，会从上下文移除。
5、 如果需要下一页的数据，直接去ES的上下文中，找后续内容。
6、 返回结果
ps：不适合做实时查询，因为是从内存中查的。
查询结果没有评分了，按照id排序或者是自己定义排序
POST /book_test/_search?scroll=1m # 指定scroll查询的id在内存中存1分钟
{
"query": {
"match": {
"name": "西游记"
}
}
, "size": 2 # 查询条数
, "sort": [ # 自定义排序
{
"count": {
"order": "desc"
}
}
]
}
深分页响应
{
"_scroll_id" : "FGluY2x1ZGVfY29udGV4dF91dWlkDXF1ZXJ5QW5kRmV0Y2gBFlZ4ZGplLVZ4VGFlcVdWT1ljQmstTHcAAAAAAIMYSRZDMWpuSWFZa1JFQ2ZReGRRRHdzcm53",
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : null,
"hits" : [
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "3",
"_score" : null,
"_source" : {
"author" : "张三2",
"name" : "修改西游记2",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "fsgdfgafad"
},
"sort" : [
1565
]
},
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "4",
"_score" : null,
"_source" : {
"name" : "西游记2",
"author" : "李四",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "发根深蒂固"
},
"sort" : [
1565
]
}
]
}
}
下一次scroll查询
POST /_search/scroll
{
"scroll_id":"FGluY2x1ZGVfY29udGV4dF91dWlkDXF1ZXJ5QW5kRmV0Y2gBFlZ4ZGplLVZ4VGFlcVdWT1ljQmstTHcAAAAAAIMZghZDMWpuSWFZa1JFQ2ZReGRRRHdzcm53"
,"scroll":"1m"
}
删除scroll
DELETE /_search/scroll/FGluY2x1ZGVfY29udGV4dF91dWlkDXF1ZXJ5QW5kRmV0Y2gBFlZ4ZGplLVZ4VGFlcVdWT1ljQmstTHcAAAAAAIMZghZDMWpuSWFZa1JFQ2ZReGRRRHdzcm53
4.8 delete-by-query 根据查询删除
如果删除文档数量是大部分文档，不建议用这种方式，这种方式，比较耗费性能，一条条删除的
POST /book_test/_delete_by_query
{
"query": {
"match": {
"name": "西游记"
}
}
}
4.9 复合查询，
复合过滤器，将你的多个查询条件，以一定的逻辑组合在一起
must ：所有的条件，用must组合在一起，表示and意思
must_not : 将must_not中的条件，全部都不能匹配，表示not的意思
should ： 所有的条件，用should组合在一起，表示or的意思
POST /book_test/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"name": "西游记"
}
},
{
"range": {
"on_sale": {
"gte": "2022-08-31",
"lte": "2022-08-31"
}
}
}
],
"should": [
{
"term": {
"author": {
"value": "李四"
}
}
}
],
"must_not": [
{
"range": {
"count": {
"gte": 10,
"lte": 20
}
}
}
]
}
}
}
响应结果
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 3.5179136,
"hits" : [
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "4",
"_score" : 3.5179136,
"_source" : {
"name" : "西游记2",
"author" : "李四",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "发根深蒂固"
}
},
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "3",
"_score" : 1.6099696,
"_source" : {
"author" : "张三2",
"name" : "修改西游记2",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "fsgdfgafad"
}
}
]
}
}
4.9 boosting查询
boosting查询可以帮助我们去影响查询后的score，
positive：只有匹配上positive的查询的内容，才会被放到返回的结果中
negative ： 如果匹配上和positive并且也匹配上了negative，就可以降低这样的文档score。
negative_boost ： 指定系数，必须小于1.0
关键查询时，分数是如何计算的：
搜索的关键字在文档中出现的频次越高，分数就越高
指定的文档内容越短，分数就越高
我们在搜索时，指定的关键字也会分词，这个被分词的内容，被分词的个数越多，分数越高
POST /book_test/_search
{
"query": {
"boosting": {
"positive": {
"match": {
"name": "西游记"
}
},
"negative": {
"match": {
"author": "李四"
}
},
"negative_boost": 0.5
}
}
}
响应
{
"took" : 6,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : 0.60996956,
"hits" : [
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "3",
"_score" : 0.60996956,
"_source" : {
"author" : "张三2",
"name" : "修改西游记2",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "fsgdfgafad"
}
},
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "2",
"_score" : 0.60996956,
"_source" : {
"author" : "张三2",
"name" : "修改西游记2",
"count" : "1222",
"on_sale" : "2022-08-30",
"desc" : "了接口直接裂开房价高了几十个"
}
},
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "4",
"_score" : 0.36307707,
"_source" : {
"name" : "西游记2",
"author" : "李四",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "发根深蒂固"
}
}
]
}
}
4.10 filter查询
query查询，根据你的查询条件，去计算文档的匹配度，得到一个分数，并且根据分数进行排序，不会做缓存的。
filter ，根据你的查询条件去查询文档，不去计算分数，不做排序，而且filter会对经常被过滤的数据进行缓存，查询效率相对query高
POST /book_test/_search
{
"query": {
"bool": {
"filter": [
{
"term": { # 第一个过滤条件
"author": "李四"
}
},
{
"range": { # 第二个过滤条件
"count": {
"gte": 10,
"lte": 20000
}
}
}
]
}
}
}
响应结果没有分数
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 0.0,
"hits" : [
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "4",
"_score" : 0.0,
"_source" : {
"name" : "西游记2",
"author" : "李四",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "发根深蒂固"
}
}
]
}
}
4.11 高亮查询
高亮查询就是你用户输入的关键字，以一定的特殊样式展示给用户，让用户知道为什么这个结果被检索出来。
高亮展示的数据，本身就是稳定中的一个feild，单独将feild以highlight的形式返回给你。
ES提供了一个highlight属性和query同级别的。
fragment_size：指定高亮数据展示多少个字符回来。
pre_tags：指定前缀标签<font color = "red">
post_tags : 指定后缀标签</font>
fields：指定哪个feild以高亮形式返回。
POST /book_test/_search
{
"query": {
"bool": {
"filter": [
{
"term": {
"desc": "大家"
}
},
{
"range": {
"count": {
"gte": 10,
"lte": 20000
}
}
}
]
}
},
"highlight": {
"fields": {
"desc": {}
},
"pre_tags": "<font color = 'red'>",
"post_tags": "</font>"
, "fragment_size":10
}
}
高亮响应
{
"took" : 86,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 0.0,
"hits" : [
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "5",
"_score" : 0.0,
"_source" : {
"name" : "大话西游",
"author" : "李大四",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "京东卡拉拉LDA； 大家都发"
},
"highlight" : {
"desc" : [
"京东卡拉拉LDA； <font color = 'red'>大家</font>都发"
]
}
},
{
"_index" : "book_test",
"_type" : "_doc",
"_id" : "6",
"_score" : 0.0,
"_source" : {
"name" : "大话济公",
"author" : "小四",
"count" : "1565",
"on_sale" : "2022-08-31",
"desc" : "大家都发财"
},
"highlight" : {
"desc" : [
"<font color = 'red'>大家</font>都发财"
]
}
}
]
}
}
文章来源：https://www.toymoban.com/news/detail-407689.html
4.12 聚合查询，多种多样的聚合
ES的聚合查询和MySQL的聚合查询类似
聚合查询的语法
POST /book_test/_search
{
"aggs":{
"名字(count_agg)":{
"聚合类型：cardinality , avg agg_type":{
"属性":"值"
}
}
}
}
4.12.1 去重计数查询
去重计数，即cardinality，第一步先将返回的文档中的一个指定的feild进行去重，统计一共多少条。 去重的字段不能是text。会报错，可以是keyword、long等
POST /mall_index_spu/_search
{
"aggs": {
"aggs": {
"cardinality": {
"field": "teamId"
}
}
}
}
响应最后
{
"aggregations" : {
"aggs" : {
"value" : 4
}
}
4.12.2 范围统计
统计一定范围内出现的文档，比如，针对某一个feild的值在0-100,100-200之间
范围统计可以针对普通的数值，针对时间类型，针对ip类型都可以做相应的统计
range，date_range,ip_range
POST /mall_index_spu/_search
{
"aggs": {
"range_count": {
"range": {
"field": "saleNum",
"ranges": [
{
"to": 10
},
{
"from": 10, # from 是包含的
"to": 50
},
{
"from": 50
}
]
}
}
}
}
4.12.3 时间范围统计
POST /book_test/_search
{
"aggs": {
"range_count": {
"date_range": {
"field": "on_sale",
"format": "yyyy-MM",
"ranges": [
{
"from": "2021-10",
"to": "2023-10"
}
]
}
}
}
}
时间范围统计结果
{
"aggregations" : {
"range_count" : {
"buckets" : [
{
"key" : "2021-10-2023-10",
"from" : 1.6330464E12,
"from_as_string" : "2021-10",
"to" : 1.6961184E12,
"to_as_string" : "2023-10",
"doc_count" : 7
}
]
}
}
}
4.12.4 extended_stats统计聚合查询
他可以帮你查询指定feild的最大值，最小值，平均值，平方和
POST /book_test/_search
{
"aggs": {
"range_count": {
"extended_stats": {
"field": "count"
}
}
}
}
响应结果
"aggregations" : {
"range_count" : {
"count" : 7,
"min" : 125.0,
"max" : 1565.0,
"avg" : 1310.2857142857142,
"sum" : 9172.0,
"sum_of_squares" : 1.3755034E7,
"variance" : 248156.20408163272,
"variance_population" : 248156.20408163272,
"variance_sampling" : 289515.57142857154,
"std_deviation" : 498.15279190388236,
"std_deviation_population" : 498.15279190388236,
"std_deviation_sampling" : 538.0665120861654,
"std_deviation_bounds" : {
"upper" : 2306.591298093479,
"lower" : 313.9801304779495,
"upper_population" : 2306.591298093479,
"lower_population" : 313.9801304779495,
"upper_sampling" : 2386.418738458045,
"lower_sampling" : 234.15269011338341
}
}
}
4.12.5 ES 的地图检索方式 经纬度
geo_distance: 直线距离检索方式
geo_bounding_box: 以两个点确定一个矩形，获取在矩形内的全部数据
geo_polygon: 以多个点，确定一个多边形，获取多边形内的全部数据
文章来源地址https://www.toymoban.com/news/detail-407689.html
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