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
- Sentinel介绍与使用 收藏起来
点赞再看,养成习惯,微信搜索[牧小农]关注我获取更多资讯,风里雨里,小农等你,很高兴能够成为你的朋友. 项目源码地址:公众号回复 sentinel,即可免费获取源码 前言 在家休息的的时候,突然小勇打 ...
- 第6组 Beta冲刺 总结
目录 1. 基本情况 2. 思考与总结 2.1. 设想和目标 2. 计划 3. 资源 4. 变更管理 5. 设计/实现 6. 测试/发布 7. 团队的角色,管理,合作 8. 总结 3. 敏捷开发 1. ...
- 【ASP.NET Core】配置应用程序地址的N多种方法
下面又到了老周误人子弟的时间,今天要误大伙的话题是:找找有多少种方法可以设置 ASP.NET Core 应用的地址,即 URL. 精彩马上开始! 1.UseUrls 方法 这是一个扩展方法,参数是可变 ...
- [杂项]从子域名接管到Subtaker
子域名接管安全性分析及落地化 能说只是为了学Go嘛?33333 Github项目直通车 简介 子域名接管,主要原因归结于失效dns记录未删除. 譬如,一条指向test.sec.com的CNAME记录未 ...
- django框架6
内容概要 神奇的双下划线查询 外键字段的创建 外键字段操作 多表查询 基于对象的跨表查询 基于双下划线的跨表查询 双下线查询扩展 如何查看SQL语句 内容详情 神奇的双下划线查询 1.查询年龄大于20 ...
- 即时通讯IM，是时代进步的逆流？看看JNPF怎么说
JNPF快速开发平台所包含的第四个重要的开发框架是即时通讯沟通工具.即时沟通工具的目的是让各大企事业单位在各种业务工作流程环境下实现实时无缝协同办公,打破信息数据孤岛,形成高效的层级流转审批和各流程环 ...
- Jmeter基础入门应用举例
举例当然应该有接口下面以常用的百度搜索接口为例: 1.接口地址: http://www.baidu.com/s?ie=utf-8&wd=jmeter性能测试 2.请求参数 ie:编码方式,默认 ...
- 六、LVM和从磁盘配额
一.LVM概述 Logical Volume Manager,逻辑卷管理 优点:能够保证在现有数据不变的情况下,动态调整磁盘容量,从而提高磁盘管理的灵活性 /boot分区用于存放引导文件,不能基于LV ...
- VR技术赋能五大领域，不止高级，更高效！
除了VR游戏.VR影视作品,究竟还有哪些产业领域会应用到VR技术并为生活带来改变呢?今天就帮大家好好梳理一下~ VR赋能交通,不只是高级 最近在网上看到了VR考驾照的新闻,网友都赞叹,现在学车都这么高 ...
- SpringBoot + JWT + Redis 开源知识社区系统
「Java学习+面试指南」一份涵盖大部分 Java 程序员所需要掌握的核心知识.准备 Java 面试,首选 JavaGuide!:https://javaguide.cn/ 你好,我是 Guide!这 ...