elasticsearch查询之keyword字段的查询相关度评分控制
一、数据情况
purchase记录每个用户的购买信息；
PUT purchase
{
"mappings":{
"properties":{
"id":{
"type":"keyword"
},
"name":{
"type":"text"
},
"goods":{
"properties":{
"id":{
"type":"keyword"
},
"name":{
"type":"text"
}
}
}
}
}
}
index 三个document
PUT purchase/_doc/1
{
"id":1,
"name":"sam",
"goods":[
{"id":"g1","name":"ipad"},
{"id":"g2","name":"iphone"}
]
}
PUT purchase/_doc/2
{
"id":2,
"name":"coco",
"goods":[
{"id":"g1","name":"ipad"},
{"id":"g2","name":"iphone"},
{"id":"g3","name":"ipod"}
]
}
PUT purchase/_doc/3
{
"id":3,
"name":"jim",
"goods":[
{"id":"g1","name":"ipad"},
{"id":"g2","name":"iphone"},
{"id":"g3","name":"ipod"},
{"id":"g4","name":"TV"}
]
}
查看索引数据情况
POST purchase/_search
{
"query": {
"match_all": {}
}
}
{
"took":331,
"timed_out":false,
"_shards":{
"total":1,
"successful":1,
"skipped":0,
"failed":0
},
"hits":{
"total":{
"value":3,
"relation":"eq"
},
"max_score":1,
"hits":[
{
"_index":"purchase",
"_id":"1",
"_score":1,
"_source":{
"id":1,
"name":"sam",
"goods":[
{
"id":"g1",
"name":"ipad"
},
{
"id":"g2",
"name":"iphone"
}
]
}
},
{
"_index":"purchase",
"_id":"2",
"_score":1,
"_source":{
"id":2,
"name":"coco",
"goods":[
{
"id":"g1",
"name":"ipad"
},
{
"id":"g2",
"name":"iphone"
},
{
"id":"g3",
"name":"ipod"
}
]
}
},
{
"_index":"purchase",
"_id":"3",
"_score":1,
"_source":{
"id":3,
"name":"jim",
"goods":[
{
"id":"g1",
"name":"ipad"
},
{
"id":"g2",
"name":"iphone"
},
{
"id":"g3",
"name":"ipod"
},
{
"id":"g4",
"name":"TV"
}
]
}
}
]
}
}
二、查询需求
我们需要查询购买过某种商品的顾客，一般我们可以通过ui的业务逻辑得到需要筛选的一些商品的id，由于id字段是一个不需要分词的keyword字段，所以我们会直接使用term级别的查询；
POST purchase/_search
{
"query": {
"terms": {
"goods.id": [
"g2",
"g3",
"g4"
]
}
}
}
我们可以看到查询结果中的三条记录的权重打分都是1；正常情况下购买商品越多的客户，相对来说价值更大即命中的权重得分越大；
{
"took":0,
"timed_out":false,
"_shards":{
"total":1,
"successful":1,
"skipped":0,
"failed":0
},
"hits":{
"total":{
"value":3,
"relation":"eq"
},
"max_score":1,
"hits":[
{
"_index":"purchase",
"_id":"1",
"_score":1,
"_source":{
"id":1,
"name":"sam",
"goods":[
{
"id":"g1",
"name":"ipad"
},
{
"id":"g2",
"name":"iphone"
}
]
}
},
{
"_index":"purchase",
"_id":"2",
"_score":1,
"_source":{
"id":2,
"name":"coco",
"goods":[
{
"id":"g1",
"name":"ipad"
},
{
"id":"g2",
"name":"iphone"
},
{
"id":"g3",
"name":"ipod"
}
]
}
},
{
"_index":"purchase",
"_id":"3",
"_score":1,
"_source":{
"id":3,
"name":"jim",
"goods":[
{
"id":"g1",
"name":"ipad"
},
{
"id":"g2",
"name":"iphone"
},
{
"id":"g3",
"name":"ipod"
},
{
"id":"g4",
"name":"TV"
}
]
}
}
]
}
}
三、terms查询分析
我们使用_explain分析一下terms查询怎么打分的；
POST purchase/_explain/3
{
"query": {
"terms": {
"goods.id": [
"g2",
"g3",
"g4"
]
}
}
}
我们可以看到elasticsearch最终使用ConstantScore查询重写的terms查询，此查询默认权重打分为1；
{
"_index" : "purchase",
"_id" : "3",
"matched" : true,
"explanation" : {
"value" : 1.0,
"description" : "ConstantScore(goods.id:g2 goods.id:g3 goods.id:g4)",
"details" : [ ]
}
}
terms提供的查询参数十分有限，其中涉及权重的只有boost，但是这只是针对整个terms查询，而不是内部的子查询；
POST purchase/_explain/3
{
"query": {
"terms": {
"goods.id": [
"g2",
"g3",
"g4"
],
"boost":2
}
}
}
{
"_index" : "purchase",
"_id" : "3",
"matched" : true,
"explanation" : {
"value" : 2.0,
"description" : "ConstantScore(goods.id:g2 goods.id:g3 goods.id:g4)^2.0",
"details" : [ ]
}
}
四、构建子查询打分
match是elasticsearch提供的一个跟terms类似的查询，由于goods.id的type是keyword，所以需要给match指定一个查询时的analyzer，才能保证输入的几个id分开作为不同的查询；
POST purchase/_search
{
"query": {
"match": {
"goods.id": {
"query": "g2 g3 g4",
"analyzer":"standard"
}
}
}
}
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : 2.178501,
"hits" : [
{
"_index" : "purchase",
"_id" : "3",
"_score" : 2.178501,
"_source" : {
"id" : 3,
"name" : "jim",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
},
{
"id" : "g3",
"name" : "ipod"
},
{
"id" : "g4",
"name" : "TV"
}
]
}
},
{
"_index" : "purchase",
"_id" : "2",
"_score" : 0.8298607,
"_source" : {
"id" : 2,
"name" : "coco",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
},
{
"id" : "g3",
"name" : "ipod"
}
]
}
},
{
"_index" : "purchase",
"_id" : "1",
"_score" : 0.18360566,
"_source" : {
"id" : 1,
"name" : "sam",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
}
]
}
}
]
}
}
通过查看文档3的打分情况，我们可以看到elasticsearch先针对每个关键字计算打分，然后将三项打分的和作为最终的打分；在这里我们也可以看到elasticsearch内部会自动将match查询rewrite为三个子查询；
POST purchase/_explain/3
{
"query": {
"match": {
"goods.id": {
"query": "g2 g3 g4",
"analyzer":"standard"
}
}
}
}
{
"_index" : "purchase",
"_id" : "3",
"matched" : true,
"explanation" : {
"value" : 2.178501,
"description" : "sum of:",
"details" : [
{
"value" : 0.18360566,
"description" : "weight(goods.id:g2 in 2) [PerFieldSimilarity], result of:",
"details" : []
},
{
"value" : 0.646255,
"description" : "weight(goods.id:g3 in 2) [PerFieldSimilarity], result of:",
"details" : []
},
{
"value" : 1.3486402,
"description" : "weight(goods.id:g4 in 2) [PerFieldSimilarity], result of:",
"details" : []
}
]
}
}
我们也可以通过bool查询，使用它的should在查询之前手动组建多个子查询；
POST purchase/_search
{
"query": {
"bool": {
"should": [
{"term": {"goods.id": "g2"}},
{"term": {"goods.id": "g3"}},
{"term": {"goods.id": "g4"}}
],
"minimum_should_match": 1
}
}
}
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : 2.178501,
"hits" : [
{
"_index" : "purchase",
"_id" : "3",
"_score" : 2.178501,
"_source" : {
"id" : 3,
"name" : "jim",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
},
{
"id" : "g3",
"name" : "ipod"
},
{
"id" : "g4",
"name" : "TV"
}
]
}
},
{
"_index" : "purchase",
"_id" : "2",
"_score" : 0.8298607,
"_source" : {
"id" : 2,
"name" : "coco",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
},
{
"id" : "g3",
"name" : "ipod"
}
]
}
},
{
"_index" : "purchase",
"_id" : "1",
"_score" : 0.18360566,
"_source" : {
"id" : 1,
"name" : "sam",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
}
]
}
}
]
}
}
在bool查询中，通过查看文档3的打分情况，我们可以看到elasticsearch也是先针对每个关键字计算打分，然后将三项打分的和作为最终的打分；
POST purchase/_explain/3
{
"query": {
"bool": {
"should": [
{"term": {"goods.id": "g2"}},
{"term": {"goods.id": "g3"}},
{"term": {"goods.id": "g4"}}
],
"minimum_should_match": 1
}
}
}
{
"_index" : "purchase",
"_id" : "3",
"matched" : true,
"explanation" : {
"value" : 2.178501,
"description" : "sum of:",
"details" : [
{
"value" : 0.18360566,
"description" : "weight(goods.id:g2 in 2) [PerFieldSimilarity], result of:",
"details" : []
},
{
"value" : 0.646255,
"description" : "weight(goods.id:g3 in 2) [PerFieldSimilarity], result of:",
"details" : []
},
{
"value" : 1.3486402,
"description" : "weight(goods.id:g4 in 2) [PerFieldSimilarity], result of:",
"details" : []
}
]
}
}
五、控制子查询的打分
不管是elasticsearch自动组建子查询，还是我们自己手动构建子查询，elasticsearch都会针对每个查询做相关性的打分计算，这对于一般的语义化关键字搜索是没有问题的；
我们这里的搜索条件goods.id一般是没有任何语义的，不同的值打分应该是一样的；这样我们只能使用bool+constant_score+term来手动构建查询语句；
POST purchase/_search
{
"query": {
"bool": {
"should": [
{"constant_score": {"filter": {"term": {"goods.id": "g2"}}}},
{"constant_score": {"filter": {"term": {"goods.id": "g3"}}}},
{"constant_score": {"filter": {"term": {"goods.id": "g4"}}}}
],
"minimum_should_match": 1
}
}
}
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : 3.0,
"hits" : [
{
"_index" : "purchase",
"_id" : "3",
"_score" : 3.0,
"_source" : {
"id" : 3,
"name" : "jim",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
},
{
"id" : "g3",
"name" : "ipod"
},
{
"id" : "g4",
"name" : "TV"
}
]
}
},
{
"_index" : "purchase",
"_id" : "2",
"_score" : 2.0,
"_source" : {
"id" : 2,
"name" : "coco",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
},
{
"id" : "g3",
"name" : "ipod"
}
]
}
},
{
"_index" : "purchase",
"_id" : "1",
"_score" : 1.0,
"_source" : {
"id" : 1,
"name" : "sam",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
}
]
}
}
]
}
}
我们看下文档3的打分情况，每一个命中项的打分都是固定的1，最终的打分命中项的和；
POST purchase/_explain/3
{
"query": {
"bool": {
"should": [
{"constant_score": {"filter": {"term": {"goods.id": "g2"}}}},
{"constant_score": {"filter": {"term": {"goods.id": "g3"}}}},
{"constant_score": {"filter": {"term": {"goods.id": "g4"}}}}
],
"minimum_should_match": 1
}
}
}
{
"_index" : "purchase",
"_id" : "3",
"matched" : true,
"explanation" : {
"value" : 3.0,
"description" : "sum of:",
"details" : [
{
"value" : 1.0,
"description" : "ConstantScore(goods.id:g2)",
"details" : [ ]
},
{
"value" : 1.0,
"description" : "ConstantScore(goods.id:g3)",
"details" : [ ]
},
{
"value" : 1.0,
"description" : "ConstantScore(goods.id:g4)",
"details" : [ ]
}
]
}
}
elasticsearch查询之keyword字段的查询相关度评分控制的更多相关文章
- Django---Django的ORM的一对多操作(外键操作),ORM的多对多操作(关系管理对象),ORM的分组聚合,ORM的F字段查询和Q字段条件查询,Django的事务操作,额外(Django的终端打印SQL语句,脚本调试)
Django---Django的ORM的一对多操作(外键操作),ORM的多对多操作(关系管理对象),ORM的分组聚合,ORM的F字段查询和Q字段条件查询,Django的事务操作,额外(Django的终 ...
- Elasticsearch由浅入深（十）搜索引擎：相关度评分 TF&IDF算法、doc value正排索引、解密query、fetch phrase原理、Bouncing Results问题、基于scoll技术滚动搜索大量数据
相关度评分 TF&IDF算法 Elasticsearch的相关度评分(relevance score)算法采用的是term frequency/inverse document frequen ...
- 创建ASP.NET Core MVC应用程序(5)-添加查询功能 & 新字段
创建ASP.NET Core MVC应用程序(5)-添加查询功能 & 新字段 添加查询功能 本文将实现通过Name查询用户信息. 首先更新GetAll方法以启用查询: public async ...
- Django ORM queryset object 解释(子查询和join连表查询的结果)
#下面两种是基于QuerySet查询 也就是说SQL中用的jion连表的方式查询books = models.UserInfo.objects.all() print(type(books)) --- ...
- ElasticSearch 学习记录之ES查询添加排序字段和使用missing或existing字段查询
ES添加排序 在默认的情况下,ES 是根据文档的得分score来进行文档额排序的.但是自己可以根据自己的针对一些字段进行排序.就像下面的查询脚本一样.下面的这个查询是根据productid这个值进行排 ...
- Elasticsearch 结构化搜索、keyword、Term查询
前言 Elasticsearch 中的结构化搜索,即面向数值.日期.时间.布尔等类型数据的搜索,这些数据类型格式精确,通常使用基于词项的term精确匹配或者prefix前缀匹配.本文还将新版本的&qu ...
- [Elasticsearch] 多字段搜索 (三) - multi_match查询和多数字段 <译>
multi_match查询 multi_match查询提供了一个简便的方法用来对多个字段执行相同的查询. NOTE 存在几种类型的multi_match查询,其中的3种正好和在“了解你的数据”一节中提 ...
- ElasticSearch 6.2 Mapping参数说明及text类型字段聚合查询配置
背景: 由于本人使用的是6.0以上的版本es,在使用发现很多中文博客对于mapping参数的说明已过时.ES6.0以后有很多参数变化. 现我根据官网总结mapping最新的参数,希望能对大家有用处. ...
- [Elasticsearch] 多字段搜索 (三) - multi_match查询和多数字段
multi_match查询 multi_match查询提供了一个简便的方法用来对多个字段执行相同的查询. NOTE 存在几种类型的multi_match查询,其中的3种正好和在"了解你的数据 ...
随机推荐
- Hadoop安装学习（第二天）
学习任务: 1.对VMnet8进行设置 2.配置主机名,对host文件进行编辑 3.将Hadoop文件以及jdk通过Xshell7传输到Linux系统 4.设置免密登录
- C++从静态类型到单例模式
目录 1. 概述 2. 详论 2.1. 静态类型 2.1.1. 静态方法成员 2.1.2. 静态数据成员 2.2. 单例模式 2.2.1. 实现 2.2.2. 问题 3. 参考 1. 概述 很多的知识 ...
- Git合并上的问题
关于Git合并上问题的处理 在前几天对某个游戏章节的bug修改完成,主程让我把dev_7的内容合并到dev_8上.虽然很少使用Fork,但是还是硬着头皮说行. 合并前,先将分支切换到dev_8,选择d ...
- mysql 超时配置
日志报错: No operations allowed after connection closed.; nested exception is com.mysql.jdbc.exceptions. ...
- django框架5
内容概要 模板语法之过滤器(类似于内置函数) 模板语法之标签(类似于流程控制) 自定义过滤器.标签.inclusion_tag 模板的导入 模板的继承 注释语法补充 前期数据准备(测试环境搭建) al ...
- python基础学习9
python基础学习 内容概要 字符编码的简介 字符编码的发展史 字符编码的实际应用 文件操作简介 文件读写模式 文件操作模式 文件操作方法 内容详情 字符编码的简介 # 字符编码主要研究的对象是文本 ...
- Docker-Compose实现Mysql主从
1. 简介 通过使用docker-compose 搭建一个主从数据库,本示例为了解耦 将两个server拆分到了两个compose文件中,当然也可以放到一个compose文件中 演示mysql版本:5 ...
- Spring框架 - Spring和Spring框架组成
Spring框架 - Spring和Spring框架组成 Spring是什么?它是怎么诞生的?有哪些主要的组件和核心功能呢? 本文通过这几个问题帮助你构筑Spring和Spring Framework ...
- distroless 镜像介绍及 基于cbl-mariner的.NET distroless 镜像的容器
1.概述 容器改变了我们看待技术基础设施的方式.这是我们运行应用程序方式的一次巨大飞跃.容器编排和云服务一起为我们提供了一种近乎无限规模的无缝扩展能力. 根据定义,容器应该包含 应用程序 及其 运行时 ...
- 下载安装myslq-----win
在百度上搜索MySQL官网,进入下载页面-->选择社区版(Community)-->选择MySQL Community Server后,点击DOWNLOAD按钮-->Generall ...