语雀文档库: https://www.yuque.com/imoyt/zssuuf/gcbz67

一、简介

mysql用作持久化存储,ES用作检索

基本概念:index库>type表>document文档

  1. index索引

​ 动词:相当于mysql的insert

​ 名词:相当于mysql的db

  1. Type类型

​ 在index中,可以定义一个或多个类型

​ 类似于mysql的table,每一种类型的数据放在一起

  1. Document文档

​ 保存在某个index下,某种type的一个数据document,文档是json格式的,document就像是mysql中的某个table里面的内容。每一行对应的列叫属性

为什么ES搜索快?倒排索引

保存的记录

  • 红海行动
  • 探索红海行动
  • 红海特别行动
  • 红海记录片
  • 特工红海特别探索

将内容分词就记录到索引中

记录
红海1,2,3,4,5
行动1,2,3
探索2,5
特别3,5
纪录片4,
特工5

检索:

1)、红海特工行动?查出后计算相关性得分:3号记录命中了2次,且3号本身才有3个单词,2/3,所以3号最匹配
2)、红海行动?

关系型类型数据库中两个数据表示式独立的,即使他们里面有相同名称的列也不影响使用,但ES中不是这样的。Elasticsearch是基于Lucene开发的搜索引擎,而ES中不同type下名称相同的Lucene中的处理方式是一样的。

  • 两个不同的type下的两个user_name,在ES同一个索引下其实被认为是同一个filed,你必须在两个不同的type中定义相同的filed映射。否则,不同type中的相同字段名称就会处理中出现冲突的情况,导致Lucene处理效率下降。

  • 去掉type就是为了提交ES处理数据的效率。

  • ElasticSearch 7.x URL中的type参数为可选。比如,索引一个文档不再要求提供文档类型。

  • ElasticSearch 8.x 不再支持URL中的type参数

    解决:将索引从多个类型迁移到单类型。每种类型文档一个独立索引

二、ElasticSearch 安装

请参考这篇博客:CentOS 7 安装 Elasticsearch

三、初步检索

1、检索es信息

(1)GET /_cat/nodes:查看所有节点

如:http://192.168.56.10:9200/_cat/nodes

可以直接浏览器输入上面的url,也可以在kibana中输入 GET /_cat/nodes

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127.0.0.1 13 96 4 0.00 0.05 0.17 dilm * 566f1291aedb

566f1291aedb 代表节点

* 代表是主节点

(2)GET /_cat/health: 查看es健康状况

如:http://192.168.56.10:9200/_cat/health

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1623940778 14:39:38 elasticsearch green 1 1 3 3 0 0 0 0 - 100.0%

注意: green 表示健康值正常

(3)GET /_cat/master: 查看主节点

如:http://192.168.56.10:9200/_cat/master

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JBYr_91ySkSi1cHVNzkgaw 127.0.0.1 127.0.0.1 566f1291aedb

主节点唯一编号
虚拟机地址

(4)GET /_cat/indicies : 查看所有的索引,等价于mysql数据库的show database

如:http://192.168.56.10:9200/_cat/indicies

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green  open .kibana_task_manager_1   DhtDmKrsRDOUHPJm1EFVqQ 1 0 2 3 40.8kb 40.8kb
green open .apm-agent-configuration vxzRbo9sQ1SvMtGkx6aAHQ 1 0 0 0 230b 230b
green open .kibana_1 rdJ5pejQSKWjKxRtx-EIkQ 1 0 5 1 18.2kb 18.2kb

这3个索引是kibana创建的

2、新增文档

保存一个数据,保存在哪个索引的那个类型下(哪张数据表哪张表下),保存时用唯一标识指定

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# 在customer索引下的external类型下保存1号数据库
PUT customer/external/1

# POSTMAN输入
http://192.168.56.10:9200/customer/external/1

{
"name":"John Doe"
}

==PUT和POST区别==

  • POST 新增。如果不指定id,会自动生成id.指定id就会修改这个数据,并新增版本号;
    • 可以不指定id,不指定id时永远为创建
    • 指定不存在的id为创建
    • 指定存在的id为更新,而版本号会根据内容变而觉得版本号递增与否
  • PUT可以新增也可以修改。PUT必须指定id; 由于PUT需要指定id,我们一般用来修改操作,不指定id会报错。
    • 必须指定id
    • 版本号总会增加

区分技巧:put 和 java 里的map.put 一样必须指定 key-value。 而post 相当于mysql insert

seq_no 和 version 的区别:

  • 每个文档的版本号_version起始值都为1 每次对当前文档成功操作后都加1
  • 而序列号 _seq_no则可以看做事索引插入数据插入数据时为0,每对索引内数据操作成功一次 sqlNO 加1,并且文档会记录时第几次操作便它成为现在的情况的。

可以参考:https://www.cnblogs.com/Taeso/p/13363136.html

测试

image-20210617230719119

创建数据成功后,显示201 created表示插入记录成功。

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返回数据:
带有下划线开头的,称为元数据,反映了当前的基本信息
{
"_index": "customer", 表明该数据在那个数据库下;
"_type": "external", 表明该数据在那个类型下;
"_id": "1", 表明被保存数据的id;
"_version": 1, 被保存数据的版本
"result": "created", 这里是创建了一条数据,如果重新put一条数据,则该状态会变为updated,并且版本号也会发生变化。
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"_seq_no": 0,
"_primary_term": 1
}

下面选用POST方式:

添加数据的时候,不指定ID,会自动的生成id,并且类型是新增:

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{
"_index": "customer",
"_type": "external",
"_id": "5MIjvncBKdY1wAQm-wNZ",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"_seq_no": 11,
"_primary_term": 6
}

再次使用POST插入数据,不指定ID,仍然是新增的:

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{
"_index": "customer",
"_type": "external",
"_id": "5cIkvncBKdY1wAQmcQNk",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"_seq_no": 12,
"_primary_term": 6
}

添加数据的时候,指定ID,会使用该id,并且类型是新增:

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{
"_index": "customer",
"_type": "external",
"_id": "2",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"_seq_no": 13,
"_primary_term": 6
}

再次使用POST插入数据,指定同样的ID,类型为updated

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{
"_index": "customer",
"_type": "external",
"_id": "2",
"_version": 2,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"_seq_no": 14,
"_primary_term": 6
}

3、查看文档

GET /customer/external/1

http://192.168.56.10:9200/customer/external/1

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{
"_index": "customer",
"_type": "external",
"_id": "1",
"_version": 10,
"_seq_no": 18,// 并发控制字段,每次更新都会+1,用来做乐观锁
"_primary_term": 6,//同上,主分片重新分配,如果重启,就会变化
"found": true,
"_source": {
"name": "John Doe"
}
}

乐观锁用法:通过“if_seq_no=1&if_primary_term=1”,当序列号匹配的时候,才进行修改,否则不修改

实例:将id=1的数据更新为name=1,然后再次更新为name=2,起始1_seq_no=18,_primary_term=6

(1)将name更新为1

PUT http://192.168.56.10:9200/customer/external/1?if_seq_no=6&if_primary_term=2

image-20210618211751569

返回结果:

image-20210618211900290

再次查询:

image-20210618212044783

2)将name更新为2,更新过程中使用seq_no=6

PUT http://192.168.56.10:9200/customer/external/1?if_seq_no=6&if_primary_term=2

结果为:

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{
"error": {
"root_cause": [
{
"type": "version_conflict_engine_exception",
"reason": "[1]: version conflict, required seqNo [6], primary term [2]. current document has seqNo [7] and primary term [2]",
"index_uuid": "Tdur0O2yQOm75NF-JaLPpw",
"shard": "0",
"index": "customer"
}
],
"type": "version_conflict_engine_exception",
"reason": "[1]: version conflict, required seqNo [6], primary term [2]. current document has seqNo [7] and primary term [2]",
"index_uuid": "Tdur0O2yQOm75NF-JaLPpw",
"shard": "0",
"index": "customer"
},
"status": 409
}

(3)查询新的数据

GET http://192.168.56.10:9200/customer/external/1

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{
"_index": "customer",
"_type": "external",
"_id": "1",
"_version": 6,
"_seq_no": 7,
"_primary_term": 2,
"found": true,
"_source": {
"name": 2
}
}

能够看到_seq_no变为7

(4)再次更新,更新成功

PUT http://192.168.56.10:9200/customer/external/1?if_seq_no=7&if_primary_term=2

4、更新文档 _update

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POST customer/externel/1/_update
{
"doc":{
"name":"111"
}
}
或者
POST customer/externel/1
{
"doc":{
"name":"222"
}
}
或者
PUT customer/externel/1
{
"doc":{
"name":"222"
}
}

不同:带有update情况下

  • ==POST操作会对比源文档数据==,如果相同不会有什么操作,文档version不会增加。
  • PUT操作总会重新保存并增加version版本

POST时带_update对比元数据如果一样就不进行任何操作

看场景:

  • 对于大并发更新,不带update
  • 对于大并发查询偶尔更新,带update;对比更新,重新计算分配规则

(1)POST更新文档,带有_update

http://192.168.56.10:9200/customer/external/1/_update

image-20210618215519208

image-20210618215656180

如果再次执行更新,则不执行任何操作,序列号也不发生变化

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返回
{
"_index": "customer",
"_type": "external",
"_id": "1",
"_version": 10,
"result": "noop", // 无操作
"_shards": {
"total": 0,
"successful": 0,
"failed": 0
},
"_seq_no": 11,
"_primary_term": 2
}

POST更新方式,会对比原来的数据,和原来的相同,则不执行任何操作(version和_seq_no)都不变。****

(2)POST更新文档,不带_update

在更新过程中,重复执行更新操作,数据也能够更新成功,不会和原来的数据进行对比。

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{
"_index": "customer",
"_type": "external",
"_id": "1",
"_version": 13,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"_seq_no": 21,
"_primary_term": 6
}

5、删除文档或索引

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DELETE customer/external/1
DELETE customer

: elasticsearch并没有提供删除类型的操作,值提供了删除索引和文档的操作

实例:删除id=1的数据,删除后继续查询

DELETE http://192.168.56.10:9200/customer/external/1

image-20210618220643602

再次执行DELETE http://192.168.56.10:9200/customer/external/1

image-20210618220715096

GET http://192.168.56.10:9200/customer/external/1

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{
"_index": "customer",
"_type": "external",
"_id": "1",
"found": false
}

删除索引

实例:删除整个costomer索引数据

删除前,所有的索引http://192.168.56.10:9200/_cat/indices

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green  open .kibana_task_manager_1   wJwVPfDPTbWhGFrpnFq6ag 1 0 2 0 12.5kb 12.5kb
green open .apm-agent-configuration 9HdINrLnTjOhHSHjtFHaiQ 1 0 0 0 283b 283b
green open .kibana_1 JjDT586BTdayZm-jpeB2vg 1 0 8 0 28.6kb 28.6kb
yellow open customer Tdur0O2yQOm75NF-JaLPpw 1 1 3 1 9.9kb 9.9kb

删除“ customer ”索引

DELTE http://192.168.56.10:9200/customer

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响应
{
"acknowledged": true
}

删除后,所有的索引http://192.168.56.10:9200/_cat/indices

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green open .kibana_task_manager_1   wJwVPfDPTbWhGFrpnFq6ag 1 0 2 0 12.5kb 12.5kb
green open .apm-agent-configuration 9HdINrLnTjOhHSHjtFHaiQ 1 0 0 0 283b 283b
green open .kibana_1 JjDT586BTdayZm-jpeB2vg 1 0 8 0 28.6kb 28.6kb

6、ES的批量操作——bulk

匹配导入数据

POST http://192.168.56.10:9200/customer/external/_bulk

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两行为一个整体
{"index":{"_id":"1"}}
{"name":"a"}
{"index":{"_id":"2"}}
{"name":"b"}
注意格式json和text均不可,要去kibana里Dev Tools
image-20210618221652900

语法格式:

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{action:{metadata}}\n
{request body }\n

{action:{metadata}}\n
{request body }\n

​ 这里的批量操作,当发生某一条执行失败时,其他的数据仍然能够接着执行,也就是说彼此之间是独立的。

​ bulk api以此按顺序执行的所有的action(动作)。如果一个单个的动作因任何原因失败,它将继续处理它后面剩余的动作。当 bulk api返回时,它将提供每个动作的状态(与发送的顺序相同),所以您可以检查是否一个指定的动作是否失败。

实例1: 执行多条数据

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POST /customer/external/_bulk
{"index":{"_id":"1"}}
{"name":"John Doe"}
{"index":{"_id":"2"}}
{"name":"John Doe"}

执行结果:

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#! Deprecation: [types removal] Specifying types in bulk requests is deprecated.
{
"took" : 285,
"errors" : false,
"items" : [
{
"index" : {
"_index" : "customer",
"_type" : "external",
"_id" : "1",
"_version" : 1,
"result" : "created",
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 0,
"_primary_term" : 1,
"status" : 201
}
},
{
"index" : {
"_index" : "customer",
"_type" : "external",
"_id" : "2",
"_version" : 1,
"result" : "created",
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 1,
"_primary_term" : 1,
"status" : 201
}
}
]
}

实例2:对于整个索引执行批量操作

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POST /_bulk
{"delete":{"_index":"website","_type":"blog","_id":"123"}}
{"create":{"_index":"website","_type":"blog","_id":"123"}}
{"title":"my first blog post"}
{"index":{"_index":"website","_type":"blog"}}
{"title":"my second blog post"}
{"update":{"_index":"website","_type":"blog","_id":"123"}}
{"doc":{"title":"my updated blog post"}}

运行结果:

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#! Deprecation: [types removal] Specifying types in bulk requests is deprecated.
{
"took" : 370,
"errors" : false,
"items" : [
{
"delete" : {
"_index" : "website",
"_type" : "blog",
"_id" : "123",
"_version" : 1,
"result" : "not_found", // 没有该记录
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 0,
"_primary_term" : 1,
"status" : 404
}
},
{
"create" : {
"_index" : "website",
"_type" : "blog",
"_id" : "123",
"_version" : 2,
"result" : "created", // 创建
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 1,
"_primary_term" : 1,
"status" : 201
}
},
{
"index" : { // 保存
"_index" : "website",
"_type" : "blog",
"_id" : "F2KBH3oBzwta_wrj-92p",
"_version" : 1,
"result" : "created",
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 2,
"_primary_term" : 1,
"status" : 201
}
},
{
"update" : { // 更新
"_index" : "website",
"_type" : "blog",
"_id" : "123",
"_version" : 3,
"result" : "updated",
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 3,
"_primary_term" : 1,
"status" : 200
}
}
]
}

7、样本测试数据

准备了一份顾客银行账户信息的虚构的JSON文档样本。每个文档都有下列的schema(模式)。

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{
"account_number": 1,
"balance": 39225,
"firstname": "Amber",
"lastname": "Duke",
"age": 32,
"gender": "M",
"address": "880 Holmes Lane",
"employer": "Pyrami",
"email": "amberduke@pyrami.com",
"city": "Brogan",
"state": "IL"
}

https://github.com/elastic/elasticsearch/blob/master/docs/src/test/resources/accounts.json , 导入测试数据

考虑目前的版本已经没有accounts.json,我们的版本是7.4.2 ,可以在github中把你现在使用的版本下载下来。

image-20210618225111364

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POST bank/account/_bulk
上面的数据

image-20210618225153228

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http://192.168.56.10:9200/_cat/indices
刚导入了1000
yellow open bank 99m64ElxRuiH46wV7RjXZA 1 1 1000 0 427.8kb 427.8kb

四、进阶语法

1、search检索文档

ES支持两种基本方式检索:

  • 通过REST request url 发送检索信息 (uri + 检索参数);
  • 通过REST request body 来发送它们(uri+请求体)

信息检索

API: https://www.elastic.co/guide/en/elasticsearch/reference/7.x/getting-started-search.html

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GET bank/_search?q=*&sort=account_number:asc

说明:
q=* # 查询所有
sort # 排序字段
asc # 升序

检索bank下所有的信息,包括type和docs
GET bank/_search

返回的结果:

image-20210622223943079

  • took - 花费多少ms搜索
  • timed_out - 是否超时
  • _shards - 多少分片被搜索了,以及多少成功/失败的搜索分片
  • max_score - 文档相关性最高得分
  • hits.total.value - 多少匹配文档被找到
  • hits.sort - 结果的排序key(列),没有的话按照score排序
  • hits._score -相关的得分 (not applicable when using match_all)
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GET bank/_search?q=*&sort=account_number:asc

检索了1000条数据,但是根据相关性算法,只返回10

uri + 请求体进行检索

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GET bank/_search
{
"query":{"match_all": {}},
"sort":[
{"account_number": "asc"} ,
{"balance": "desc"}
]
}

​ POSTMAN中get不能携带请求体,我们变为post也是一样的,我们post一个json风格的查询的请求体到 _search需要了解,一旦搜索的结果被返回,es就完成了这次请求,不能切不会维护任何服务端的资源或者结果的cursor游标。

2、DSL领域特定语言

如何写复杂查询

​ Elasticsearch提供一个可以执行查询的json风格的DSL(domain-specific language领域特定语言)。这个被称为Query DSL,该查询语言非常全面。

2.1 基本语法格式

一个查询语句的典型结构

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如果针对于某个字段,那么它的结构如下:
{
QUERY_NAME: { # 使用的功能
FIELD_NAME: { # 功能参数
ARGUMENT:VALUE,
ARGUMENT:VALUE,...
}
}
}
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GET bank/_search
{
"query":{ # 查询的字段
"match_all":{}
},
"from":0, # 从第几条文档开始查
"size":5,
"_source":["balance"],
"sort":[
{
"account_number":{ # 返回结果按哪个列排序
"order":"desc" # 降序
}
}
]
}

_source为要返回的字段

query定义如何查询:

  1. match_all查询类型【代表查询所有的所有】,es中可以在query中的组合非常重的查询类型完成复杂查询;
  2. 除了query参数之外,我们也可以传递其他参数可改变查询结果,如sort、size;
  3. from + size 限定,完成分页功能;
  4. sort排序,多字段排序,会在前序字段相等时后内部排序,否则以前序为准;

2.2 返回部分字段

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GET bank/_search
{
"query": {
"match_all": {}
},
"from": 0,
"size": 5,
"sort": [
{
"account_number": {
"order": "desc"
}
}
],
"_source": ["balance","firstname"]

}

查询结果:

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{
"took" : 18,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1000,
"relation" : "eq"
},
"max_score" : null,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "999",
"_score" : null,
"_source" : {
"firstname" : "Dorothy",
"balance" : 6087
},
"sort" : [
999
]
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "998",
"_score" : null,
"_source" : {
"firstname" : "Letha",
"balance" : 16869
},
"sort" : [
998
]
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "997",
"_score" : null,
"_source" : {
"firstname" : "Combs",
"balance" : 25311
},
"sort" : [
997
]
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "996",
"_score" : null,
"_source" : {
"firstname" : "Andrews",
"balance" : 17541
},
"sort" : [
996
]
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "995",
"_score" : null,
"_source" : {
"firstname" : "Phelps",
"balance" : 21153
},
"sort" : [
995
]
}
]
}
}

2.3 match比配查询

基本查询(非字符串),“account_number”: 20 可加可不加“ ”,不加就是精确匹配

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GET bank/_search
{
"query":{
"match": {
"account_number": "20"
}
}
}

match返回account-number=20数据

查询结果:

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{
"took" : 11,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "20",
"_score" : 1.0,
"_source" : {
"account_number" : 20,
"balance" : 16418,
"firstname" : "Elinor",
"lastname" : "Ratliff",
"age" : 36,
"gender" : "M",
"address" : "282 Kings Place",
"employer" : "Scentric",
"email" : "elinorratliff@scentric.com",
"city" : "Ribera",
"state" : "WA"
}
}
]
}
}

字符串,全文检索“ ” 模糊查询

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GET bank/_search
{
"query":{
"match": {
"address": "kings"
}
}
}

全文检索,最终会按照评分进行排序,会对检索条件进行匹配。

查询结果:

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{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 5.9908285,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "20",
"_score" : 5.9908285,
"_source" : {
"account_number" : 20,
"balance" : 16418,
"firstname" : "Elinor",
"lastname" : "Ratliff",
"age" : 36,
"gender" : "M",
"address" : "282 Kings Place",
"employer" : "Scentric",
"email" : "elinorratliff@scentric.com",
"city" : "Ribera",
"state" : "WA"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "722",
"_score" : 5.9908285,
"_source" : {
"account_number" : 722,
"balance" : 27256,
"firstname" : "Roberts",
"lastname" : "Beasley",
"age" : 34,
"gender" : "F",
"address" : "305 Kings Hwy",
"employer" : "Quintity",
"email" : "robertsbeasley@quintity.com",
"city" : "Hayden",
"state" : "PA"
}
}
]
}
}

2.4 match_phrase [短语匹配]

将需要匹配的值当成一个整个单词(不分词)进行检索

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GET bank/_search
{
"query" :{
"match_phrase": {
"address": "mill road"
}
}
}

查出address中包含mill_road的所有记录,并给出相关性得分

查询结果:

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{
"took" : 10,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 8.926605,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 8.926605,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "forbeswallace@pheast.com",
"city" : "Lopezo",
"state" : "AK"
}
}
]
}
}

注意:

match_phrase和match的区别,观察如下的实例:

  • match_phrase是做短语匹配
  • match是分词匹配,例如990 Mill匹配含有990或者Mill的结果
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GET bank/_search
{
"query":{
"match_phrase": {
"address": "990 Mill"
}
}
}

查询结果:

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{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 10.806405,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 10.806405,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "forbeswallace@pheast.com",
"city" : "Lopezo",
"state" : "AK"
}
}
]
}
}

使用match的keyword

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GET bank/_search
{
"query": {
"match": {
"address.keyword": "990 Mill"
}
}
}

查询结果,一条也未匹配到

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{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 0,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
}
}

修改匹配数据为“990 Mill Road”

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GET bank/_search
{
"query": {
"match": {
"address.keyword": "990 Mill Road"
}
}
}

查询出一条数据

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{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 6.5032897,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 6.5032897,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "forbeswallace@pheast.com",
"city" : "Lopezo",
"state" : "AK"
}
}
]
}
}

文本字段的匹配,使用keyword,匹配的条件就是显示字段的全部值,要进行精确匹配的。

match_phrase是做短语匹配,只要文本中包含匹配条件即包含这个短语,就能匹配到。

2.5 multi_math【多字段匹配】

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GET bank/_search
{
"query":{
"multi_match": {
"query": "mill",
"fields": [
"state" ,
"address"
]
}
}
}

state或者address中包含mill,并且在查询过程中,会对于查询条件分词

查询结果:

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{
"took" : 3,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 4,
"relation" : "eq"
},
"max_score" : 5.4032025,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 5.4032025,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "forbeswallace@pheast.com",
"city" : "Lopezo",
"state" : "AK"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "136",
"_score" : 5.4032025,
"_source" : {
"account_number" : 136,
"balance" : 45801,
"firstname" : "Winnie",
"lastname" : "Holland",
"age" : 38,
"gender" : "M",
"address" : "198 Mill Lane",
"employer" : "Neteria",
"email" : "winnieholland@neteria.com",
"city" : "Urie",
"state" : "IL"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "345",
"_score" : 5.4032025,
"_source" : {
"account_number" : 345,
"balance" : 9812,
"firstname" : "Parker",
"lastname" : "Hines",
"age" : 38,
"gender" : "M",
"address" : "715 Mill Avenue",
"employer" : "Baluba",
"email" : "parkerhines@baluba.com",
"city" : "Blackgum",
"state" : "KY"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "472",
"_score" : 5.4032025,
"_source" : {
"account_number" : 472,
"balance" : 25571,
"firstname" : "Lee",
"lastname" : "Long",
"age" : 32,
"gender" : "F",
"address" : "288 Mill Street",
"employer" : "Comverges",
"email" : "leelong@comverges.com",
"city" : "Movico",
"state" : "MT"
}
}
]
}
}

2.6 bool用来做复合查询

复合语句可以合并,任何其他查询语句,包括符合语句。这也就意味着,复合语句之家可以相互嵌套,可以表达非常复杂的逻辑。

must: 必须达到must所列举的所有条件

实例:查询gender=m,并且address=mill的数据

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GET bank/_search
{
"query":{
"bool":{
"must":[
{"match":{"address": "mill"}},
{"match": {"gender": "M"}}
]
}
}
}

查询结果:

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{
"took" : 5,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : 6.0824604,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 6.0824604,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "forbeswallace@pheast.com",
"city" : "Lopezo",
"state" : "AK"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "136",
"_score" : 6.0824604,
"_source" : {
"account_number" : 136,
"balance" : 45801,
"firstname" : "Winnie",
"lastname" : "Holland",
"age" : 38,
"gender" : "M",
"address" : "198 Mill Lane",
"employer" : "Neteria",
"email" : "winnieholland@neteria.com",
"city" : "Urie",
"state" : "IL"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "345",
"_score" : 6.0824604,
"_source" : {
"account_number" : 345,
"balance" : 9812,
"firstname" : "Parker",
"lastname" : "Hines",
"age" : 38,
"gender" : "M",
"address" : "715 Mill Avenue",
"employer" : "Baluba",
"email" : "parkerhines@baluba.com",
"city" : "Blackgum",
"state" : "KY"
}
}
]
}
}

must_not,必须不匹配must_not所列举的所有条件。

实例:查询gender=m,并且address=mill的数据,但是age不等于38的

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GET bank/_search
{
"query":{
"bool":{
"must":[
{
"match":
{
"gender": "M"
}
},
{
"match": {
"address": "mill"
}
}
],
"must_not":[
{
"match":{
"age":"38"
}
}
]
}
}
}

查询结果:

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{
"took" : 4,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 6.0824604,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 6.0824604,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "forbeswallace@pheast.com",
"city" : "Lopezo",
"state" : "AK"
}
}
]
}
}

should,应该满足should多列举的条件

如果达到增加相关文档的评分,并不会改变查询的结果。如果query中只有should且只有一种匹配规则,那么should的条件就会被作为默认条件二区改变查询结果。

实例:匹配lastName应该属于Wallace的数据

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GET bank/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"gender": "M"
}
},
{
"match": {
"address": "mill"
}
}
],
"must_not": [
{
"match": {
"age": "18"
}
}
],
"should": [
{
"match": {
"lastname": "Wallace"
}
}
]
}
}
}

查询结果:

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{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : 12.585751,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 12.585751,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "forbeswallace@pheast.com",
"city" : "Lopezo",
"state" : "AK"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "136",
"_score" : 6.0824604,
"_source" : {
"account_number" : 136,
"balance" : 45801,
"firstname" : "Winnie",
"lastname" : "Holland",
"age" : 38,
"gender" : "M",
"address" : "198 Mill Lane",
"employer" : "Neteria",
"email" : "winnieholland@neteria.com",
"city" : "Urie",
"state" : "IL"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "345",
"_score" : 6.0824604,
"_source" : {
"account_number" : 345,
"balance" : 9812,
"firstname" : "Parker",
"lastname" : "Hines",
"age" : 38,
"gender" : "M",
"address" : "715 Mill Avenue",
"employer" : "Baluba",
"email" : "parkerhines@baluba.com",
"city" : "Blackgum",
"state" : "KY"
}
}
]
}
}

能够看到相关度越高,得分越高。

2.7 Filter【结果过滤】

并不是所有的查询都需要产生分数,特别是哪些仅用于filtering过滤的文档,为了不计算分数,elasticsearch会自动检查场景并且优化查询的执行。

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GET bank/_search
{
"query":{
"bool":{
"must":[
{"match":{"address":"mill"}}
],
"filter":{
"range":{
"balance":{
"gte":"10000",
"lte":"20000"
}
}
}
}
}
}

这里先是查询所有匹配address=mill的文档,然后再根据10000<=balance<=20000进行过滤查询结果

查询结果:

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{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 5.4032025,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 5.4032025,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "forbeswallace@pheast.com",
"city" : "Lopezo",
"state" : "AK"
}
}
]
}
}

Each must, should, and must_not element in a Boolean query is referred to as a query clause. How well a document meets the criteria in each must or should clause contributes to the document’s relevance score. The higher the score, the better the document matches your search criteria. By default, Elasticsearch returns documents ranked by these relevance scores.

在boolean查询中,must, shouldmust_not 元素都被称为查询子句 。 文档是否符合每个“must”或“should”子句中的标准,决定了文档的“相关性得分”。 得分越高,文档越符合您的搜索条件。 默认情况下,Elasticsearch返回根据这些相关性得分排序的文档。

The criteria in a must_not clause is treated as a filter. It affects whether or not the document is included in the results, but does not contribute to how documents are scored. You can also explicitly specify arbitrary filters to include or exclude documents based on structured data.

“must_not”子句中的条件被视为“过滤器”。 它影响文档是否包含在结果中, 但不影响文档的评分方式。 还可以显式地指定任意过滤器来包含或排除基于结构化数据的文档。

filter在使用过程中,并不会计算相关得分_score:

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GET bank/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"address": "mill"
}
}
],
"filter": {
"range": {
"balance": {
"gte": "10000",
"lte": "20000"
}
}
}
}
}
}
//gte:>= lte:<=

查询结果:

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{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 213,
"relation" : "eq"
},
"max_score" : 0.0,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "20",
"_score" : 0.0,
"_source" : {
"account_number" : 20,
"balance" : 16418,
"firstname" : "Elinor",
"lastname" : "Ratliff",
"age" : 36,
"gender" : "M",
"address" : "282 Kings Place",
"employer" : "Scentric",
"email" : "elinorratliff@scentric.com",
"city" : "Ribera",
"state" : "WA"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "37",
"_score" : 0.0,
"_source" : {
"account_number" : 37,
"balance" : 18612,
"firstname" : "Mcgee",
"lastname" : "Mooney",
"age" : 39,
"gender" : "M",
"address" : "826 Fillmore Place",
"employer" : "Reversus",
"email" : "mcgeemooney@reversus.com",
"city" : "Tooleville",
"state" : "OK"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "51",
"_score" : 0.0,
"_source" : {
"account_number" : 51,
"balance" : 14097,
"firstname" : "Burton",
"lastname" : "Meyers",
"age" : 31,
"gender" : "F",
"address" : "334 River Street",
"employer" : "Bezal",
"email" : "burtonmeyers@bezal.com",
"city" : "Jacksonburg",
"state" : "MO"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "56",
"_score" : 0.0,
"_source" : {
"account_number" : 56,
"balance" : 14992,
"firstname" : "Josie",
"lastname" : "Nelson",
"age" : 32,
"gender" : "M",
"address" : "857 Tabor Court",
"employer" : "Emtrac",
"email" : "josienelson@emtrac.com",
"city" : "Sunnyside",
"state" : "UT"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "121",
"_score" : 0.0,
"_source" : {
"account_number" : 121,
"balance" : 19594,
"firstname" : "Acevedo",
"lastname" : "Dorsey",
"age" : 32,
"gender" : "M",
"address" : "479 Nova Court",
"employer" : "Netropic",
"email" : "acevedodorsey@netropic.com",
"city" : "Islandia",
"state" : "CT"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "176",
"_score" : 0.0,
"_source" : {
"account_number" : 176,
"balance" : 18607,
"firstname" : "Kemp",
"lastname" : "Walters",
"age" : 28,
"gender" : "F",
"address" : "906 Howard Avenue",
"employer" : "Eyewax",
"email" : "kempwalters@eyewax.com",
"city" : "Why",
"state" : "KY"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "183",
"_score" : 0.0,
"_source" : {
"account_number" : 183,
"balance" : 14223,
"firstname" : "Hudson",
"lastname" : "English",
"age" : 26,
"gender" : "F",
"address" : "823 Herkimer Place",
"employer" : "Xinware",
"email" : "hudsonenglish@xinware.com",
"city" : "Robbins",
"state" : "ND"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "222",
"_score" : 0.0,
"_source" : {
"account_number" : 222,
"balance" : 14764,
"firstname" : "Rachelle",
"lastname" : "Rice",
"age" : 36,
"gender" : "M",
"address" : "333 Narrows Avenue",
"employer" : "Enaut",
"email" : "rachellerice@enaut.com",
"city" : "Wright",
"state" : "AZ"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "227",
"_score" : 0.0,
"_source" : {
"account_number" : 227,
"balance" : 19780,
"firstname" : "Coleman",
"lastname" : "Berg",
"age" : 22,
"gender" : "M",
"address" : "776 Little Street",
"employer" : "Exoteric",
"email" : "colemanberg@exoteric.com",
"city" : "Eagleville",
"state" : "WV"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "272",
"_score" : 0.0,
"_source" : {
"account_number" : 272,
"balance" : 19253,
"firstname" : "Lilly",
"lastname" : "Morgan",
"age" : 25,
"gender" : "F",
"address" : "689 Fleet Street",
"employer" : "Biolive",
"email" : "lillymorgan@biolive.com",
"city" : "Sunbury",
"state" : "OH"
}
}
]
}
}

能看到所有文档的 “_score” : 0.0。

2.8 term

和match一样。匹配某个属性值。全文检索字段用match,其他非text字段匹配用term.

Avoid using the term query for text fields.

避免对文本字段使用“term”查询

By default, Elasticsearch changes the values of text fields as part of analysis. This can make finding exact matches for text field values difficult.

默认情况下,Elasticsearch作为analysis的一部分更改’ text ‘字段的值。这使得为“text”字段值寻找精确匹配变得困难。

To search text field values, use the match.

要搜索“text”字段值,请使用匹配。

https://www.elastic.co/guide/en/elasticsearch/reference/7.6/query-dsl-term-query.html

使用term匹配查询

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GET bank/_search
{
"query":{
"term": {
"age":"28"
}
}
}

如果是text则查不到

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GET bank/_search
{
"query": {
"term": {
"gender" : "F"
}
}
}

image-20210626183135965

一条也没有匹配到

而更换为match匹配时,能够匹配到32个文档1

image-20210626183227857

也就是说,全文检索字段用match,其他非text字段匹配用term

2.9 Aggregation(执行聚合)

聚合提供了从数据中分组和提取数据的能力。最简单的聚合方法大致等于SQL Group by和SQL聚合函数,在elasticsearch中,执行搜索返回this(命中结果),并且同时返回聚合结果,把以响应中的所有this(命中结果)分隔开的能力。这是非常强大且有效的,你可以执行查询和多个聚合,并且在一次使用中得到各自的(任何一个的)返回结果,使用一次简化的API避免网络往返。

size: 0 不显示搜索数据

aggs: 执行聚合

语法如下:

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"aggs":{
"aggs_name这次聚合的名字,方便展示在结果集中":{
"AGG_TYPE聚合的类型(avg,term,terms)":{}
}
},

搜索address中包含mill的所有人的年龄分布以及平均年龄,但不显示这些人的详情

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GET bank/_search
{
"query":{
"match": {
"address": "Mill"
}
},
"aggs":{
"ageAgg":{
"terms": {
"field": "age",
"size": 10
}
},
"aggAvg":{
"avg": {
"field": "age"
}
},
"balanceAvg":{
"avg":{
"field": "balance"
}
}
},
"size":0
}
//ageAgg:聚合名字 terms:聚合类型 "field": "age":按照age字段聚合 size:10:取出前十种age
//avg:平均值聚合类型
//不显示这些人的详情,只看聚合结果

查询结果:

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{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 4,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"aggAvg" : {
"value" : 34.0
},
"ageAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 38,
"doc_count" : 2
},
{
"key" : 28,
"doc_count" : 1
},
{
"key" : 32,
"doc_count" : 1
}
]
},
"balanceAvg" : {
"value" : 25208.0
}
}
}

复杂:按照年龄聚合,并且求这些年龄段的这些人的平均薪资

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GET bank/_search
{
"query":{
"match_all": {}
},
"aggs":{
"ageAgg":{
"terms": {
"field": "age",
"size": 100
},
"aggs":{
"ageAvg":{
"avg": {
"field": "balance"
}
}
}
}
}
}

输出结果:

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{
"took" : 44,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1000,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "1",
"_score" : 1.0,
"_source" : {
"account_number" : 1,
"balance" : 39225,
"firstname" : "Amber",
"lastname" : "Duke",
"age" : 32,
"gender" : "M",
"address" : "880 Holmes Lane",
"employer" : "Pyrami",
"email" : "amberduke@pyrami.com",
"city" : "Brogan",
"state" : "IL"
}
},
.....省略
]
},
"aggregations" : {
"ageAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 31,
"doc_count" : 61,
"ageAvg" : {
"value" : 28312.918032786885
}
},
{
"key" : 39,
"doc_count" : 60,
"ageAvg" : {
"value" : 25269.583333333332
}
},
....省略

]
}
}
}

查出所有年龄分布,并且这些年龄段中M的平均薪资和F的平均薪资以及这个年龄段的总体平均薪资

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GET bank/_search
{
"query":{
"match_all": {}
},
"aggs":{
"ageAgg":{
"terms": {
"field": "age",
"size": 10
},
"aggs":{
"genderAgg":{
"terms":{
"field": "gender.keyword"
},
"aggs": {
"balanceAvg":{
"avg":{
"field": "balance"
}
}
}
},
"ageBalanceAvg":{
"avg": {
"field": "balance"
}
}
}
}
},
"size":0
}

查询结果:

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{
"took" : 119,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1000,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"ageAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 31,
"doc_count" : 61,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "M",
"doc_count" : 35,
"balanceAvg" : {
"value" : 29565.628571428573
}
},
{
"key" : "F",
"doc_count" : 26,
"balanceAvg" : {
"value" : 26626.576923076922
}
}
]
},
"ageBalanceAvg" : {
"value" : 28312.918032786885
}
}
]
.......//省略其他
}
}
}

五、Mapping

1、字段类型

image-20210626223158965

2、映射

**Mapping(映射)**:是用来定义文档(document),以及它所包含的属性(field)是如何存储和索引的。比如:使用mapping来定义:

  • 哪些字符串属性应该被看作全文本属性(full text fields);
  • 哪些属性包含数字,日期或地理位置
  • 文档中的所有属性是否都能被索引(_all 配置)
  • 日期格式
  • 自定义映射规则来执行动态添加属性。

查看mapping信息

GET bank/_mapping

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{
"bank" : {
"mappings" : {
"properties" : {
"account_number" : {
"type" : "long"
},
"address" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"age" : {
"type" : "long"
},
"balance" : {
"type" : "long"
},
"city" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"email" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"employer" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"firstname" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"gender" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"lastname" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"state" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
}
}
}
}
}

修改mapping信息

官方文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/current/mapping.html

自动猜测的映射类型

image-20210626225344047

3、新版本改变

ElasticSearch7-去掉type概念

  1. 关系型数据库中两个数据表示是独立的,即使他们里面有相同名称的列也不影响使用,但ES中不是这样的。elasticsearch是基于Lucene开发的搜索引擎,而ES中不同type下名称相同的filed最终在Lucene中的处理方式是一样的。

    • 两个不同type下的两个user_name,在ES同一个索引下其实被认为是同一个filed,你必须在两个不同的type中定义相同的filed映射。否则,不同type中的相同字段名称就会在处理中出现冲突的情况,导致Lucene处理效率下降。
    • 去掉type就是为了提高ES处理数据的效率。
  2. Elasticsearch 7.x URL中的type参数为可选。比如,索引一个文档不再要求提供文档类型。

  3. Elasticsearch 8.x 不再支持URL中的type参数。

  4. 解决:
    将索引从多类型迁移到单类型,每种类型文档一个独立索引

    将已存在的索引下的类型数据,全部迁移到指定位置即可。详见数据迁移

官方:

Elasticsearch 7.x

  • Specifying types in requests is deprecated. For instance, indexing a document no longer requires a document type. The new index APIs are PUT {index}/_doc/{id} in case of explicit ids and POST {index}/_doc for auto-generated ids. Note that in 7.0, _doc is a permanent part of the path, and represents the endpoint name rather than the document type.
  • The include_type_name parameter in the index creation, index template, and mapping APIs will default to false. Setting the parameter at all will result in a deprecation warning.
  • The _default_ mapping type is removed.

Elasticsearch 8.x

  • Specifying types in requests is no longer supported.
  • The include_type_name parameter is removed.

3.1 创建索引

创建索引并指定属性的映射规则(相当于新建表并制定字段和字段类型

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PUT /my_index
{
"mappings":{
"properties":{
"age":{
"type":"integer"
},
"email":{
"type":"keyword"
},
"name":{
"type":"text"
}
}
}
}

输出:

image-20210626230845738

3.2 查看映射

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GET /my_index

输出结果:

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// "index": false 是否被索引即能检索到,默认是true
{
"my_index" : {
"aliases" : { },
"mappings" : {
"properties" : {
"age" : {
"type" : "integer"
},
"email" : {
"type" : "keyword"
},
"name" : {
"type" : "text"
}
}
},
"settings" : {
"index" : {
"creation_date" : "1624720095622",
"number_of_shards" : "1",
"number_of_replicas" : "1",
"uuid" : "jGHXQpc6RpeUwJNAHu7AgQ",
"version" : {
"created" : "7040299"
},
"provided_name" : "my_index"
}
}
}
}

3.3 添加新的字段映射

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PUT /my_index/_mapping
{
"properties":{
"employee-id":{
"type":"keyword",
"index": false
}
}
}

这里的 “index”: false, 表明新增字段不能被检索,只是一个冗余字段。

3.4 更新字段

对于已经存在的字段映射,我们不能更新。更新必须创建新的索引,进行数据迁移。

3.5 数据迁移

先创建new_twitter的正确映射。然后使用如下方式进行数据迁移。

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POST reindex [固定写法]
{
"source":{
"index":"twitter"
},
"dest":{
"index":"new_twitters"
}
}

更多详情见: https://www.elastic.co/guide/en/elasticsearch/reference/7.6/docs-reindex.html

GET /bank/_search

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{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1000,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "1",
"_score" : 1.0,
"_source" : {
"account_number" : 1,
"balance" : 39225,
"firstname" : "Amber",
"lastname" : "Duke",
"age" : 32,
"gender" : "M",
"address" : "880 Holmes Lane",
"employer" : "Pyrami",
"email" : "amberduke@pyrami.com",
"city" : "Brogan",
"state" : "IL"
}
}
.......

想将年龄修改为integer

GET /bank/_search

image-20210626232553134

  • 创建newbank索引
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PUT /newbank
{
"mappings": {
"properties": {
"account_number": {
"type": "long"
},
"address": {
"type": "text"
},
"age": {
"type": "integer"
},
"balance": {
"type": "long"
},
"city": {
"type": "keyword"
},
"email": {
"type": "keyword"
},
"employer": {
"type": "keyword"
},
"firstname": {
"type": "text"
},
"gender": {
"type": "keyword"
},
"lastname": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"state": {
"type": "keyword"
}
}
}
}
  • 查看“newbank”的映射:

GET /newbank/_mapping

能够看到age的映射类型被修改为了integer

image-20210626232949187

  • 将bank中的数据迁移到newbank中
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POST _reindex
{
"source":{
"index":"bank",
"type":"account"
},
"dest":{
"index":"newbank"
}
}

运行输出:

image-20210626233550951

查看newbank中的数据

image-20210626233650289

六、分词

一个tokenizer(分词器)接收一个字符流,将之分割为独立的tokens词元,通常是独立的单词),然后输出tokens流。

例如:whitespace tokenizer遇到空白字符时分割文本。它会将文本"Quick brown fox!"分割为[Quick,brown,fox!]

该tokenizer(分词器)还负责记录各个terms(词条)的顺序或position位置(用于phrase短语和word proximity词近邻查询),以及term(词条)所代表的原始word(单词)的start(起始)和end(结束)的character offsets(字符串偏移量)(用于高亮显示搜索的内容)。

elasticsearch提供了很多内置的分词器(标准分词器),可以用来构建custom analyzers(自定义分词器)。

关于分词器https://www.elastic.co/guide/en/elasticsearch/reference/7.6/analysis.html

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POST _analyze
{
"analyzer": "standard",
"text": "The 2 Brown-Foxes bone"
}

测试结果:

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{
"tokens" : [
{
"token" : "the",
"start_offset" : 0,
"end_offset" : 3,
"type" : "<ALPHANUM>",
"position" : 0
},
{
"token" : "2",
"start_offset" : 4,
"end_offset" : 5,
"type" : "<NUM>",
"position" : 1
},
{
"token" : "brown",
"start_offset" : 6,
"end_offset" : 11,
"type" : "<ALPHANUM>",
"position" : 2
},
{
"token" : "foxes",
"start_offset" : 12,
"end_offset" : 17,
"type" : "<ALPHANUM>",
"position" : 3
},
{
"token" : "bone",
"start_offset" : 18,
"end_offset" : 22,
"type" : "<ALPHANUM>",
"position" : 4
}
]
}

对于中文,我们需要安装额外的分词器

1、安装 ik 分词器

所有的语言分词,默认使用的都是”Standard Analyzer”,但是这部分分词器针对于中文的分词,并不友好。为此需要安装中文的分词器。

注意: 不能使用默认的elasticsearch-plugin install xxx.zip 进行安装

https://github.com/medcl/elasticsearch-analysis-ik/releases

在前面安装的elasticsearch时,我们已经将elasticsearch容器的“/usr/share/elasticsearch/plugins”目录,映射到宿主机的“ /mydata/elasticsearch/plugins”目录下,所以比较方便的做法就是下载“/elasticsearch-analysis-ik-7.4.2.zip”文件,然后解压到该文件夹下即可。安装完毕后,需要重启elasticsearch容器。

如果不嫌麻烦,还可以采用如下的方式。

  1. 查看elasticsearch版本号:
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[root@localhost ~]#  curl http://localhost:9200
{
"name" : "ce20fcb8d039",
"cluster_name" : "elasticsearch",
"cluster_uuid" : "hle-F0zaRHKN-H1Qnj4tZA",
"version" : {
"number" : "7.4.2",
"build_flavor" : "default",
"build_type" : "docker",
"build_hash" : "2f90bbf7b93631e52bafb59b3b049cb44ec25e96",
"build_date" : "2019-10-28T20:40:44.881551Z",
"build_snapshot" : false,
"lucene_version" : "8.2.0",
"minimum_wire_compatibility_version" : "6.8.0",
"minimum_index_compatibility_version" : "6.0.0-beta1"
},
"tagline" : "You Know, for Search"
}
  1. 进入es内部plugs目录
  • docker exec -it 容器 id /bin/bash
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[vagrant@localhost ~]$ sudo docker exec -it elasticsearch /bin/bash

[root@66718a266132 elasticsearch]# pwd
/usr/share/elasticsearch
[root@66718a266132 elasticsearch]# pwd
/usr/share/elasticsearch
[root@66718a266132 elasticsearch]# yum install wget
#下载ik7.4.2
[root@66718a266132 elasticsearch]# wget https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v7.4.2/elasticsearch-analysis-ik-7.4.2.zip
  • unzip 下载的文件
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[root@66718a266132 elasticsearch]# unzip elasticsearch-analysis-ik-7.4.2.zip -d ik

#移动到plugins目录下
[root@66718a266132 elasticsearch]# mv ik plugins/
chmod -R 777 plugins/ik

docker restart elasticsearch
  • rm -rf *.zip
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[root@66718a266132 elasticsearch]# rm -rf elasticsearch-analysis-ik-7.6.2.zip 

2、测试分词器

使用默认分词器

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GET _analyze
{
"analyzer": "standard",
"text":"我是中国人"
}

请观察执行结果:

image-20210627104644371

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GET _analyze
{
"analyzer": "ik_smart",
"text":"我是中国人"
}

输出结果:

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{
"tokens" : [
{
"token" : "我",
"start_offset" : 0,
"end_offset" : 1,
"type" : "CN_CHAR",
"position" : 0
},
{
"token" : "是",
"start_offset" : 1,
"end_offset" : 2,
"type" : "CN_CHAR",
"position" : 1
},
{
"token" : "中国人",
"start_offset" : 2,
"end_offset" : 5,
"type" : "CN_WORD",
"position" : 2
}
]
}
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GET _analyze
{
"analyzer": "ik_max_word",
"text":"我是中国人"
}

输出结果:

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{
"tokens" : [
{
"token" : "我",
"start_offset" : 0,
"end_offset" : 1,
"type" : "CN_CHAR",
"position" : 0
},
{
"token" : "是",
"start_offset" : 1,
"end_offset" : 2,
"type" : "CN_CHAR",
"position" : 1
},
{
"token" : "中国人",
"start_offset" : 2,
"end_offset" : 5,
"type" : "CN_WORD",
"position" : 2
},
{
"token" : "中国",
"start_offset" : 2,
"end_offset" : 4,
"type" : "CN_WORD",
"position" : 3
},
{
"token" : "国人",
"start_offset" : 3,
"end_offset" : 5,
"type" : "CN_WORD",
"position" : 4
}
]
}

3、自定义词库

比如我们要把尚硅谷算作一个词

  • 修改/usr/share/elasticsearch/plugins/ik/config中的IKAnalyzer.cfg.xml
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<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE properties SYSTEM "http://java.sun.com/dtd/properties.dtd">
<properties>
<comment>IK Analyzer 扩展配置</comment>
<!--用户可以在这里配置自己的扩展字典 -->
<entry key="ext_dict"></entry>
<!--用户可以在这里配置自己的扩展停止词字典-->
<entry key="ext_stopwords"></entry>
<!--用户可以在这里配置远程扩展字典 -->
<entry key="remote_ext_dict">http://192.168.56.10/es/fenci.txt</entry>
<!--用户可以在这里配置远程扩展停止词字典-->
<!-- <entry key="remote_ext_stopwords">words_location</entry> -->
</properties>

修改完成后,需要重启elasticsearch容器,否则修改不生效。docker restart elasticsearch

更新完成后,es只会对于新增的数据用更新分词。历史数据是不会重新分词的。如果想要历史数据重新分词,需要执行:

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POST my_index/_update_by_query?conflicts=proceed

安装Nginx 请查看 补充

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>mkdir /mydata/nginx/html/es
>cd /mydata/nginx/html/es
>vim fenci.txt
>输入尚硅谷

测试http://192.168.56.10/es/fenci.txt

往fenci.txt 文件添加内容:

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echo "樱桃萨其马,带你甜蜜入夏" > /mydata/nginx/html/fenci.txt 

测试效果:

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GET _analyze
{
"analyzer": "ik_max_word",
"text":"樱桃萨其马,带你甜蜜入夏"
}
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{
"tokens" : [
{
"token" : "樱桃",
"start_offset" : 0,
"end_offset" : 2,
"type" : "CN_WORD",
"position" : 0
},
{
"token" : "萨其马",
"start_offset" : 2,
"end_offset" : 5,
"type" : "CN_WORD",
"position" : 1
},
{
"token" : "带你",
"start_offset" : 6,
"end_offset" : 8,
"type" : "CN_WORD",
"position" : 2
},
{
"token" : "甜蜜",
"start_offset" : 8,
"end_offset" : 10,
"type" : "CN_WORD",
"position" : 3
},
{
"token" : "入夏",
"start_offset" : 10,
"end_offset" : 12,
"type" : "CN_WORD",
"position" : 4
}
]
}

七、elasticsearch-Rest-Client

java操作es有两种方式

1、9300: TCP

  • spring-data-elasticsearch:transport-api.jar;
    • springboot版本不同,ransport-api.jar不同,不能适配es版本
    • 7.x已经不建议使用,8以后就要废弃

2、9200: HTTP

有诸多包

  • jestClient: 非官方,更新慢;
  • RestTemplate:模拟HTTP请求,ES很多操作需要自己封装,麻烦;
  • HttpClient:同上;
  • Elasticsearch-Rest-Client:官方RestClient,封装了ES操作,API层次分明,上手简单;

最终选择Elasticsearch-Rest-Client(elasticsearch-rest-high-level-client)

https://www.elastic.co/guide/en/elasticsearch/client/java-rest/current/java-rest-high.html

八、SpringBoot整合ElasticSearch

创建项目gulimall-search

image-20210627114816175

选择依赖web,但不要在里面选择es

image-20210627114902769

1、导入依赖

这里的版本要和所按照的ELK版本匹配。

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<dependency>
<groupId>org.elasticsearch.client</groupId>
<artifactId>elasticsearch-rest-high-level-client</artifactId>
<version>7.4.2</version>
</dependency>

在spring-boot-dependencies中所依赖的ES版本位6.8.5,要改掉

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<properties>
<java.version>1.8</java.version>
<elasticsearch.version>7.4.2</elasticsearch.version>
</properties>

请求测试项,比如es添加了安全访问规则,访问es需要添加一个安全头,就可以通过requestOptions设置

官方建议把requestOptions创建成单实例

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@Configuration
public class GulimallElasticSearchConfig {
public static final RequestOptions COMMON_OPTIONS;
static {
RequestOptions requestOptions;
RequestOptions.Builder builder = RequestOptions.DEFAULT.toBuilder();

COMMON_OPTIONS = builder.build();
}

@Bean
public RestHighLevelClient esRestClient(){
RestClientBuilder builder = null;
// 可以指定多个es
builder = RestClient.builder(new HttpHost("192.168.56.10",9200,"http"));

RestHighLevelClient client = new RestHighLevelClient(builder);
return client;

}
}

2、测试

2.1 保存数据

https://www.elastic.co/guide/en/elasticsearch/client/java-rest/current/java-rest-high-document-index.html

保存方式分为同步和异步,异步方式多了个listener回调

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@Test
public void indexData() throws IOException {

// 设置索引
IndexRequest indexRequest = new IndexRequest ("users");
indexRequest.id("1");

User user = new User();
user.setUserName("张三");
user.setAge(20);
user.setGender("男");
String jsonString = JSON.toJSONString(user);

//设置要保存的内容,指定数据和类型
indexRequest.source(jsonString, XContentType.JSON);

//执行创建索引和保存数据
IndexResponse index = client.index(indexRequest, GulimallElasticSearchConfig.COMMON_OPTIONS);

System.out.println(index);

}

2.2 获取数据

https://www.elastic.co/guide/en/elasticsearch/client/java-rest/current/java-rest-high-search.html

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@Test
public void find() throws IOException {
// 1 创建检索请求
SearchRequest searchRequest = new SearchRequest();
searchRequest.indices("bank");
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
// 构造检索条件
// sourceBuilder.query();
// sourceBuilder.from();
// sourceBuilder.size();
// sourceBuilder.aggregation();
sourceBuilder.query(QueryBuilders.matchQuery("address","mill"));
System.out.println(sourceBuilder.toString());

searchRequest.source(sourceBuilder);

// 2 执行检索
SearchResponse response = client.search(searchRequest, GuliESConfig.COMMON_OPTIONS);
// 3 分析响应结果
System.out.println(response.toString());
}
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{
"took": 6,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 4,
"relation": "eq"
},
"max_score": 5.4032025,
"hits": [
{
"_index": "bank",
"_type": "account",
"_id": "970",
"_score": 5.4032025,
"_source": {
"account_number": 970,
"balance": 19648,
"firstname": "Forbes",
"lastname": "Wallace",
"age": 28,
"gender": "M",
"address": "990 Mill Road",
"employer": "Pheast",
"email": "forbeswallace@pheast.com",
"city": "Lopezo",
"state": "AK"
}
},
{
"_index": "bank",
"_type": "account",
"_id": "136",
"_score": 5.4032025,
"_source": {
"account_number": 136,
"balance": 45801,
"firstname": "Winnie",
"lastname": "Holland",
"age": 38,
"gender": "M",
"address": "198 Mill Lane",
"employer": "Neteria",
"email": "winnieholland@neteria.com",
"city": "Urie",
"state": "IL"
}
},
{
"_index": "bank",
"_type": "account",
"_id": "345",
"_score": 5.4032025,
"_source": {
"account_number": 345,
"balance": 9812,
"firstname": "Parker",
"lastname": "Hines",
"age": 38,
"gender": "M",
"address": "715 Mill Avenue",
"employer": "Baluba",
"email": "parkerhines@baluba.com",
"city": "Blackgum",
"state": "KY"
}
},
{
"_index": "bank",
"_type": "account",
"_id": "472",
"_score": 5.4032025,
"_source": {
"account_number": 472,
"balance": 25571,
"firstname": "Lee",
"lastname": "Long",
"age": 32,
"gender": "F",
"address": "288 Mill Street",
"employer": "Comverges",
"email": "leelong@comverges.com",
"city": "Movico",
"state": "MT"
}
}
]
}
}{
"took": 6,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 4,
"relation": "eq"
},
"max_score": 5.4032025,
"hits": [
{
"_index": "bank",
"_type": "account",
"_id": "970",
"_score": 5.4032025,
"_source": {
"account_number": 970,
"balance": 19648,
"firstname": "Forbes",
"lastname": "Wallace",
"age": 28,
"gender": "M",
"address": "990 Mill Road",
"employer": "Pheast",
"email": "forbeswallace@pheast.com",
"city": "Lopezo",
"state": "AK"
}
},
{
"_index": "bank",
"_type": "account",
"_id": "136",
"_score": 5.4032025,
"_source": {
"account_number": 136,
"balance": 45801,
"firstname": "Winnie",
"lastname": "Holland",
"age": 38,
"gender": "M",
"address": "198 Mill Lane",
"employer": "Neteria",
"email": "winnieholland@neteria.com",
"city": "Urie",
"state": "IL"
}
},
{
"_index": "bank",
"_type": "account",
"_id": "345",
"_score": 5.4032025,
"_source": {
"account_number": 345,
"balance": 9812,
"firstname": "Parker",
"lastname": "Hines",
"age": 38,
"gender": "M",
"address": "715 Mill Avenue",
"employer": "Baluba",
"email": "parkerhines@baluba.com",
"city": "Blackgum",
"state": "KY"
}
},
{
"_index": "bank",
"_type": "account",
"_id": "472",
"_score": 5.4032025,
"_source": {
"account_number": 472,
"balance": 25571,
"firstname": "Lee",
"lastname": "Long",
"age": 32,
"gender": "F",
"address": "288 Mill Street",
"employer": "Comverges",
"email": "leelong@comverges.com",
"city": "Movico",
"state": "MT"
}
}
]
}
}
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@Test
public void find() throws IOException {
// 1 创建检索请求
SearchRequest searchRequest = new SearchRequest();
searchRequest.indices("bank");
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
// 构造检索条件
// sourceBuilder.query();
// sourceBuilder.from();
// sourceBuilder.size();
// sourceBuilder.aggregation();
sourceBuilder.query(QueryBuilders.matchQuery("address","mill"));
//AggregationBuilders工具类构建AggregationBuilder
// 构建第一个聚合条件:按照年龄的值分布
TermsAggregationBuilder agg1 = AggregationBuilders.terms("ageAgg").field("age").size(10);// 聚合名称
// 参数为AggregationBuilder
sourceBuilder.aggregation(agg1);
// 构建第二个聚合条件:平均薪资
AvgAggregationBuilder agg2 = AggregationBuilders.avg("balanceAvg").field("balance");
sourceBuilder.aggregation(agg2);

System.out.println("检索条件"+sourceBuilder.toString());

searchRequest.source(sourceBuilder);

// 2 执行检索
SearchResponse response = client.search(searchRequest, GuliESConfig.COMMON_OPTIONS);
// 3 分析响应结果
System.out.println(response.toString());
}

转换bean

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@Data
static class Account {
private int account_number;
private int balance;
private String firstname;
private String lastname;
private int age;
private String gender;
private String address;
private String employer;
private String email;
private String city;
private String state;

@Override
public String toString() {
return "Account{" +
"account_number=" + account_number +
", balance=" + balance +
", firstname='" + firstname + '\'' +
", lastname='" + lastname + '\'' +
", age=" + age +
", gender='" + gender + '\'' +
", address='" + address + '\'' +
", employer='" + employer + '\'' +
", email='" + email + '\'' +
", city='" + city + '\'' +
", state='" + state + '\'' +
'}';
}
}
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// 3.1 获取java bean
SearchHits hits = response.getHits();
SearchHit[] hits1 = hits.getHits();
for (SearchHit hit : hits1) {
hit.getId();
hit.getIndex();
String sourceAsString = hit.getSourceAsString();
Account account = JSON.parseObject(sourceAsString, Account.class);
System.out.println(account);

}
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Account(accountNumber=970, balance=19648, firstname=Forbes, lastname=Wallace, age=28, gender=M, address=990 Mill Road, employer=Pheast, email=forbeswallace@pheast.com, city=Lopezo, state=AK)
Account(accountNumber=136, balance=45801, firstname=Winnie, lastname=Holland, age=38, gender=M, address=198 Mill Lane, employer=Neteria, email=winnieholland@neteria.com, city=Urie, state=IL)
Account(accountNumber=345, balance=9812, firstname=Parker, lastname=Hines, age=38, gender=M, address=715 Mill Avenue, employer=Baluba, email=parkerhines@baluba.com, city=Blackgum, state=KY)
Account(accountNumber=472, balance=25571, firstname=Lee, lastname=Long, age=32, gender=F, address=288 Mill Street, employer=Comverges, email=leelong@comverges.com, city=Movico, state=MT)

Buckets分析信息

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// 3.2 获取检索到的分析信息
Aggregations aggregations = response.getAggregations();
Terms ageAgg = aggregations.get("ageAgg");
for (Terms.Bucket bucket : ageAgg.getBuckets()) {
System.out.println("年龄:" + bucket.getKeyAsString() + "--人数: " + bucket.getDocCount());
}
Avg balanceAvg = aggregations.get("balanceAvg");
System.err.println("薪资平均值:"+balanceAvg.getValue());

搜索address中包含mill的所有人的年龄分布以及平均年龄,平均薪资

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GET bank/_search
{
"query": {
"match": {
"address": "Mill"
}
},
"aggs": {
"ageAgg": {
"terms": {
"field": "age",
"size": 10
}
},
"ageAvg": {
"avg": {
"field": "age"
}
},
"balanceAvg": {
"avg": {
"field": "balance"
}
}
}
}

补充:安装Nginx

  • 随便启动一个nginx实例,只是为了复制出配置
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docker run -p80:80 --name nginx -d nginx:1.10   
  • 将容器内的配置文件拷贝到当前目录 (别忘了后面的点 )
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docker container cp nginx:/etc/nginx .
  • 关闭nginx容器和删除
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docker stop nginx
docker rm nginx
  • 查看mydata目录下的nginx复制文件
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cd /mydata/nginx
ll

image-20210627112135785

  • 在mydata目录下把nginx文件夹名修改为conf
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mv nginx conf

image-20210627112412712

  • 在mydata目录下再次新建nginx目录,把conf目录移动进去
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[root@localhost mydata]# mkdir nginx
[root@localhost mydata]# mv conf nginx/
[root@localhost mydata]# ls
elasticsearch mysql nginx portainer redis
  • 创建新的 nginx
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docker run -p 80:80 --name nginx \
-v /mydata/nginx/html:/usr/share/nginx/html \
-v /mydata/nginx/logs:/var/log/nginx \
-v /mydata/nginx/conf:/etc/nginx \
-d nginx:1.10

image-20210627112844296

  • 创建“/mydata/nginx/html/index.html”文件,测试是否能够正常访问
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echo '<h2>hello nginx!</h2>' >index.html

访问:http://nginx所在主机的IP:80/index.html

image-20210627113513696