ES的基本使用
elasticsearch的基础语法+聚合函数+IK分词器+java的API的高阶调用
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1、简介
elasticSearch【分布式开源搜索与分析引擎，适用于所有类型的数据，包括文本，数字，地理空间，结构化和非结构化数据， 秒级从海量数据从检索出我们所需要的数据，而mysql单表如果达到了百万级数据，检索很慢】
用途：
1、应用程序搜索
2、网站搜索
3、企业搜索
4、日志处理和分析
5、基础设施指标和容器检测
6、应用程序性能检测
7、地理空间数据分析和可视化
8、安全分析和业务分析 mysql【数据的持久化管理curd】
2、基本概念
1、Index（索引），相当于mysql的insert操作，插入（索引）一条数据到数据库；名词形式相当于mysql的database； 2、Type（类型），在索引中，可以定义一个或者多个类型，类似于mysql的table，每一种类型的数据放到一起； 3、Document(文档),保存在某个索引（Index）下,某种类型(Type)的一个数据（Document），文档是Json格式的，Document就像是Mysql中的某个table里边的内容； ES集群 google microsoft megacorp 索引 employee product employee product 类型 {id:1,name:张三} 文档，一条记录 {id:2,name:李四} id被称为属性，==列 ES的概念2：倒排索引；Mysql中保存一条数据，可能是正向索引； 在ES中，会维护一张倒排索引表； 词 记录位置 红海 1,2,3,4,5 行动 1,2,3 特工 5
会有一个相关性得分：3号记录命中了两个单词；
3、基本环境搭建
docker pull elasticsearch
docker pull libana ##可视化检索数据
mkdir -p /mydata/elasticsearch/config #挂载到外部，方便修改
mkdir -p /mydata/elasticsearch/data #挂载到外部，方便修改
echo "http.host:0.0.0.0">> /mydata/elasticsearch/config/elasticsearch.yml #标识ES的机器，可以被远程任何机器访问，写入到yml里边
cat elasticsearch.yml
docker run --name elasticsearch -p 9200:9200 -p 9300:9300 \ #9200是restAPI的时候给9200发送请求，9300是ES在分布式集群下节点之间的通信端口
-e "discovery.type = single-node" \#单节点模式运行
-e ES_JAVA_OPTS = "-Xms64m -Xmx128m" \ #真正上线之后，内存都是32G左右的
-v /mydata/elasticsearch/config/elasticsearch.yml:/usr/share/elasticsearch/config/elasticsearch.yml \ #这是进行挂载配置文件
-v /mydata/elasticsearch/data:/usr/share/elasticsearch/data \ #这是挂载数据
-v /mydata/elasticsearch/plugins:/usr/share/elasticsearch/plugins \ #这是挂载插件目录
-d elasticsearch:latest
#查看镜像版本：
docker image inspect nginx:latest | grep -i version
生成elasticsearch容器[完整，有时行，有时不行，看内存好像，elasticsearch，默认好像占用全部内存，不要脸。]：
docker run --name elasticsearch -p 9200:9200 -p 9300:9300 \
-e "discovery.type=single-node" \
-e ES_JAVA_OPTS="-Xms512m -Xmx1024m" \
-v /mydata/elasticsearch/config/elasticsearch.yml:/usr/share/elasticsearch/elasticsearch.yml \
-v /mydata/elasticsearch/data:/usr/share/elasticsearch/data \
-v /mydata/elasticsearch/plugins:/usr/share/elasticsearch/plugins \
-d elasticsearch:latest
生成之后，容器2秒后死亡，扩大运行内存试试？？【不管用，那就多分配点资源，不设置最小内存了】
docker run --name elasticsearch -p 9200:9200 -p 9300:9300 \
-e "discovery.type=single-node" \
-v /mydata/elasticsearch/config/elasticsearch.yml:/usr/share/elasticsearch/elasticsearch.yml \
-v /mydata/elasticsearch/data:/usr/share/elasticsearch/data \
-v /mydata/elasticsearch/plugins:/usr/share/elasticsearch/plugins \
-d elasticsearch
free -m 查看使用的虚拟机内存
启动Kibana
##docker run --name kibana -e ELASTICSEARCH_HOSTS=192.168.52.130:9200 -p 5601:5601 -d kibana:latest
docker run --name kibana -e ELASTICSEARCH_URL=http://192.168.52.130:9200 -p 5601:5601 -d kibana:latest
http://192.168.52.130:5601/app/kibana#?_g=()
/elastic/elasticsearch/master/docs/src/test/resources/accounts.json
--删除容器：
docker stop elasticsearch
docker rm elasticsearch
查看错误日志：
docker logs elasticsearch
--挂载的文件需要有读写权限：任何用户任何组都有执行权限；
chmod -R 777 /mydata/elasticsearch
执行前：
ll
drwxr-xr-x
执行后：
ll
drwxrwxrwx
elasticsearch的镜像启动成功之后，访问下：
http://192.168.52.130:9200/
{
"name" : "ZeFr4rR",
"cluster_name" : "elasticsearch",
"cluster_uuid" : "Hg9BNYkUQCy650XP26sYkw",
"version" : {
"number" : "5.6.12",
"build_hash" : "cfe3d9f",
"build_date" : "2018-09-10T20:12:43.732Z",
"build_snapshot" : false,
"lucene_version" : "6.6.1"
},
"tagline" : "You Know, for Search"
}相关DSL的语句使用
es查询节点相关信息
http://192.168.52.130:9200/_cat
=^.^=
/_cat/allocation
/_cat/shards
/_cat/shards/{index}
/_cat/master
/_cat/nodes
/_cat/tasks
/_cat/indices
/_cat/indices/{index}
/_cat/segments
/_cat/segments/{index}
/_cat/count
/_cat/count/{index}
/_cat/recovery
/_cat/recovery/{index}
/_cat/health
/_cat/pending_tasks
/_cat/aliases
/_cat/aliases/{alias}
/_cat/thread_pool
/_cat/thread_pool/{thread_pools}
/_cat/plugins
/_cat/fielddata
/_cat/fielddata/{fields}
/_cat/nodeattrs
/_cat/repositories
/_cat/snapshots/{repository}
/_cat/templates
http://192.168.52.130:9200/_cat/nodes
127.0.0.1 12 62 0 0.01 0.07 0.12 mdi * ZeFr4rR 【在集群状态下，哪个节点标了*，说明他是主节点】
http://192.168.52.130:9200/_cat/health --查看es的健康状况
1647341114 10:45:14 elasticsearch green 1 1 0 0 0 0 0 0 - 100.0% 【中间的一些数字是集群分片信息】
http://192.168.52.130:9200/_cat/master --查看主节点
ZeFr4rRZQz-w3ZK80Vuv0Q 127.0.0.1 127.0.0.1 ZeFr4rR
http://192.168.52.130:9200/_cat/indices --查看所有索引 show databases
2、索引一个文档（保存）
保存一个数据，保存在那个索引类型下，指定用哪个唯一索引
在customer索引下的external类型下保存1号数据为
PUT请求方式，必须得带上id
http://192.168.52.130:9200/customer/external/1
body里边的json
{
"name":"pansd"
}
返回信息：
{
"_index": "customer",
"_type": "external",
"_id": "1",
"_version": 1, +1
"result": "created", --updated，
"_shards": { --分片，集群环境下介绍；
"total": 2,
"successful": 1,
"failed": 0
},
"created": true
}
分析返回数据：带_的都是元数据。put请求，第一次是请求时创建操作，以后的请求是更新操作。
如果是post请求方式，不指定id；
{
"_index": "customer",
"_type": "external",
"_id": "AX-NOVshrEj9CP_oy5eV", --自动生成id，多次发送请求，每次都会产生唯一的id；
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"created": true
}
--如果带上了id，就和put一样了。
es的查询：
Get方式： customer/external/1
http://192.168.52.130:9200/customer/external/1
{
"_index": "customer",
"_type": "external",
"_id": "1",
"_version": 1,
"_seq_no":1, -- 此版本的镜像没有，每次更新就会+1，用来做乐观锁。
"_primary_term":1, -- 此版本的镜像没有，主分片重新分配，如果重启，就会发生变化。
"found": true,
"_source": {
"name": "pansd"
}
}
并发更新es的的时候，要带上乐观锁，进行并发控制。或者带上版本号，进行并发控制。
比如：
put请求：http://192.168.52.130:9200/customer/external/1?if_seq_no=1&if_primary_term=1
使用update更新：
post请求方式：http://192.168.52.130:9200/customer/external/1_update
{
"doc":{
"name":"pshdhx"
}
}
1、第一次更新，版本号会改变；若在发送相同的请求，版本号和锁序列号不会改变了。【简言之：update会对比原来的数据】
删除文档索引：
delete customer/external/1
{
"found": true,
"_index": "customer",
"_type": "external",
"_id": "1",
"_version": 2,
"result": "deleted",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
}
}
再搜索：
{
"_index": "customer",
"_type": "external",
"_id": "1",
"found": false
}
delete customer 【删除索引，但是es中是不能单独删除类型的；或者是把索引下所有的数据都给清空了，这样也相当于是删除了类型。】
批量导入API
此时批量操作json格式不对，使用text的换行又不对了，所以得使用kibana了。
1、首先必须post请求
2、customer/external/_bulk
action
body
具体展示为：每两行一个操作；批量插入两行数据；在kibana中的dev tools中添加如下代码；
post /customer/external/_bulk
{"index":{"_id":"1"}}
{"name":"pansd"}
{"index":{"_id":"2"}}
{"name":"pshdhx"}
返回值为：
{
"took": 62,
"errors": false,
"items": [
{
"index": {
"_index": "customer",
"_type": "external",
"_id": "1",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"created": true,
"status": 201
}
},
{
"index": {
"_index": "customer",
"_type": "external",
"_id": "2",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"created": true,
"status": 201
}
}
]
}复杂DSL语句
复杂操作：
post /_bulk
{"delete":{"_index":"website","_type":"blog","_id":"123"}}
{"create":{"_index":"website","_type":"blog","_id":"123"}}
{"title":"my first blog"}
{"index":{"_index":"website","_type":"blog"}}
{"title":"my second blog"}
{"update":{"_index":"website","_type":"blog","_id":"123"}}
{"doc":{"title":"my updated blog post"}}
返回了：
{
"took": 24,
"errors": false,
"items": [
{
"delete": {
"found": true,
"_index": "website",
"_type": "blog",
"_id": "123",
"_version": 6,
"result": "deleted",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
},
{
"create": {
"_index": "website",
"_type": "blog",
"_id": "123",
"_version": 7,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"created": true,
"status": 201
}
},
{
"index": {
"_index": "website",
"_type": "blog",
"_id": "AX-OHvjg4kOgKKwsJNF9",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"created": true,
"status": 201
}
},
{
"update": {
"_index": "website",
"_type": "blog",
"_id": "123",
"_version": 8,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
}
]
}
批量新增的数据地址：https://github.com/elastic/elasticsearch/blob/5.0/docs/src/test/resources/accounts.json
post /bank/account/_bulk
.....
新增成功后：使用高级检索：
docker update ... --restart=always
https://www.elastic.co/guide/index.html 参照官方文档：
https://www.elastic.co/guide/en/elastic-stack-get-started/7.5/index.html
https://www.elastic.co/guide/en/elasticsearch/reference/7.5/getting-started-search.html
GET bank/_search?q=*&sort=account_number:asc
该响应还提供有关搜索请求的以下信息：
took– Elasticsearch 运行查询需要多长时间，以毫秒为单位
timed_out– 搜索请求是否超时
_shards– 搜索了多少分片，以及多少分片成功、失败或被跳过的细分。
max_score– 找到的最相关文档的分数
hits.total.value- 找到了多少匹配的文档
hits.sort- 文档的排序位置（不按相关性分数排序时）
hits._score- 文档的相关性分数（使用时不适用match_all）
total": 1000,但是实际上hits里边只有10条，这是分页的结果。
##match_all的查询；全文检索
GET /bank/_search
{
"query": { "match_all": {} },
"sort": [
{ "account_number":{
"order": "desc"
}
}
],
"from": 0,
"size": 20,
"_source": ["balance","firstname"]
}
##match查询；按条件模糊查询
GET /bank/_search
{
"query": {
"match": {
"address": "mill lane" ##凡是有mill或者是lane的，都返回
}
}
}
##只返回这样的mill lane；短语查询；
GET /bank/_search
{
"query": { "match_phrase": { "address": "mill lane" } }
}
must和must_not；满足should的得分高；
GET /bank/_search
{
"query": {
"bool": {
"must": [
{ "match": { "age": "40" } }
],
"must_not": [
{ "match": { "state": "ID" } }
],
"should": [
{
"match": {
"address": "MILL"
}
}
]
}
}
}
##filter不会计算相关性得分。must会计算相关性得分。
GET /bank/_search
{
"query": {
"bool": {
"must": { "match_all": {} },
"filter": {
"range": {
"balance": {
"gte": 20000,
"lte": 30000
}
}
}
}
}
}
##多字段匹配
get bank/_search
{
"query":{
"multi_match": {
"query": "mill",
"fields": ["state","address"]
}
}
}
使用term检索精确字段的值；与match正好相反（match适用于全文检索）
get bank/_search
{
"query":{
"term": {
"age": {
"age": "20"
}
}
}
}
##查询精确值 这是精确匹配
get bank/_search
{
"query":{
"match": {
"address.keyword": "MILL"
}
}
}聚合函数
众所周知：elasticsearch是一个搜索与分析的引擎，下面是它的分析的用法。
Analyze results with aggregations
搜索address中包含mill的所有人的年龄分布以及平均年龄；
get bank/_search
{
"query":{
"match":{
"address":"mill"
}
},
"aggs":{
"ageAgg":{
"terms": {
"field": "age",
"size": 10
}
},
"ageAvg":{
"avg": {
"field": "age"
}
},
"balanceAvg":{
"avg": {
"field": "balance"
}
}
},
##不显示hits的结果，只显示个数。
"size":0
}
"aggregations": {
"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
}
]
}
}
##按照年龄进行聚合，并且请求这些年龄的这些人的平均薪资
get bank/_search
{
"query":{
"match_all": {}
},
"aggs":{
"ageAgg":{
"terms": {
"field": "age",
"size": 10
},
"aggs":{
"balanceAvg":{
"avg": {
"field": "balance"
}
}
}
}
}
}
返回值：
"aggregations": {
"ageAgg": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 463,
"buckets": [
{
"key": 31,
"doc_count": 61,
"balanceAvg": {
"value": 28312.918032786885
}
},
{
"key": 39,
"doc_count": 60,
"balanceAvg": {
"value": 25269.583333333332
}
},
##查出所有年龄分布，并且这些年龄段中男性的平均薪资和女性的平均薪资，还有总体的平均薪资
get bank/_search
{
"query":{
"match_all": {}
},
"aggs":{
"ageAgg":{
"terms": {
"field": "age",
"size": 10
},
"aggs": {
"genderAgg": {
"terms": {
"field": "gender.keyword",
"size": 10
},
"aggs": {
"balanceAgg": {
"avg": {
"field": "balance"
}
}
}
},
"balanceAvgAll":{
"avg": {
"field": "balance"
}
}
}
}
}
}
返回值：
"buckets": [ 31岁的人 有 61个； 35个男的，26个女的； 男的的平均薪资是29565.628571428573 女性的平均薪资是26626.576923076922
{
"key": 31,
"doc_count": 61,
"genderAgg": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "M",
"doc_count": 35,
"balanceAgg": {
"value": 29565.628571428573
}
},
{
"key": "F",
"doc_count": 26,
"balanceAgg": {
"value": 26626.576923076922
}
}
]
},
"balanceAvgAll": {
"value": 28312.918032786885
}
},
mapping映射
elasticsearch的mapping方式：7.x之后，去除了type；预计8.x取消type的支持。为了防止不同的type的字段名称相同。
get bank/_mapping
关于ES中的数据类型：https://www.elastic.co/guide/en/elasticsearch/reference/7.5/mapping-types.html
string
text and keyword
Numeric
long, integer, short, byte, double, float, half_float, scaled_float
Date
date
Date nanoseconds
date_nanos
Boolean
boolean
Binary
binary
Range
integer_range, float_range, long_range, double_range, date_range
还有复杂数据类型。
关于映射：很恶心，不同的版本不一样，看对应版本的文档都对应不起来。
get bank/_mapping
PUT my_index
{
"mappings": {
"user": {
"_all": {
"enabled": false
},
"properties": {
"title": {
"type": "text"
},
"name": {
"type": "text"
},
"age": {
"type": "integer"
}
}
}
}
}
PUT twitter
{
"mappings": {
"user": {
"properties": {
"name": { "type": "text" },
"user_name": { "type": "keyword" },
"email": { "type": "keyword" }
}
},
"tweet": {
"properties": {
"content": { "type": "text" },
"user_name": { "type": "keyword" },
"tweeted_at": { "type": "date" }
}
}
}
}
PUT twitter/user/kimchy
{
"name": "Shay Banon",
"user_name": "kimchy",
"email": "shay@kimchy.com"
}
PUT twitter/tweet/1
{
"user_name": "kimchy",
"tweeted_at": "2017-10-24T09:00:00Z",
"content": "Types are going away"
}
GET twitter/tweet/_search
{
"query": {
"match": {
"user_name": "kimchy"
}
}
}
PUT twitter
{
"mappings": {
"doc": {
"properties": {
"type": { "type": "keyword" },
"name": { "type": "text" },
"user_name": { "type": "keyword" },
"email": { "type": "keyword" },
"content": { "type": "text" },
"tweeted_at": { "type": "date" }
}
}
}
}ES的映射修改和数据迁移
##创建一个索引，并指定映射规则
PUT /my_index
{
"mappings": {
"properties": {
"age":{"type": "integer"},
"email":{"type": "keyword"},
"name":{"type": "text"}
}
}
}
##修改索引下的映射规则，【新增字段】
PUT /my_index/_mapping
{
"properties":{
"employee-id":{"type":"keyword","index":false}
}
}
GET /my_index/_mapping
##对于已经存在的映射字段，我们是不能更新的，只能添加；因为如果修改映射，那么可能存在已有的数据；我们
可以创建一个新的索引，重新reindex原来的数据。【数据迁移】
##创建新的索引,来改变老的索引的映射类型
PUT /newBank
{
"mappings": {
"properties": {
}
}
}
##数据迁移
POST _reindex
{
"source": {
"index": "bank",
"type": "account"
},
"dest": {
"index": "newBank"
}
}
4、ES的分词器
关于ES的分词：
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token流。
例如：whitespace tokenizer遇到空格字符时分割文本。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term（词条）的顺序或者是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的start和end的字符偏移量。（用于高亮显示搜索的内容）
ES提供了很多内置的分词器，可以用来构建custom analyzers（自定义分词器）
4.1、安装ik分词器：
ES的分词器：7.4.2
链接：https://pan.baidu.com/s/1DpWUZEFicNiYOJDCSDidVA
提取码：pshd
--来自百度网盘超级会员V2的分享
POST _analyze
{
"analyzer": "whitespace",
"text": "The quick brown fox."
}
安装下中文分词器：ik；github到plugins；
进入容器内部：
docker exec -it elasticsearch /bin/bash
pwd
ls
exit退出容器
先下载wget ：用yum下载下载器；
yum install wget
设置root登录：
vi /etc/ssh/sshd_config
修改：passwordAuthentication yes/no
重启服务：service sshd restart
1、必须安装ES对应版本的IK分词器；
2、放入的plugins目录下；
3、重启容器；
4、测试分词：
POST _analyze
{
"analyzer": "ik_smart", ## ik_max_word
"text": "尚硅谷电商项目"
}
返回值：
{
"tokens": [
{
"token": "尚",
"start_offset": 0,
"end_offset": 1,
"type": "CN_CHAR",
"position": 0
},
{
"token": "硅谷",
"start_offset": 1,
"end_offset": 3,
"type": "CN_WORD",
"position": 1
},
{
"token": "电",
"start_offset": 3,
"end_offset": 4,
"type": "CN_CHAR",
"position": 2
},
{
"token": "商",
"start_offset": 4,
"end_offset": 5,
"type": "CN_CHAR",
"position": 3
},
{
"token": "项目",
"start_offset": 5,
"end_offset": 7,
"type": "CN_WORD",
"position": 4
}
]
}
有些词语分词器不识别，需要创建自定义分词：
1、先设置网卡：
添加GATEWAY=xxxxx.1
DNS1=114.114.114.114
DNS2=8.8.8.8
使用了match之后，会有一个max_score最大得分；查询数字是精确查询；查询字符串是模糊查询【全文检索】；
关于自定义分词器：
4.2、安装nginx
安装nginx，把词库的地址放到nginx里边；
docker container cp nginx:/etc/nginx .
mv nginx conf #文件夹改名
mv conf nginx/ #把conf挪移到nginx文件夹里边
docker stop nginx;
docker rm nginx;
docker run -p 80:80 --name nginx \
-v /mydata/nginx/conf/html:/usr/share/nginx/html \
-v /mydata/nginx/conf/logs:/var/log/nginx \
-v /mydata/nginx/conf:/etc/nginx \
-d nginx:1.10
docker run -p80:80 --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 ##【正确】
在nginx里边自定义fenci.txt
配置ik分词器的词库地址http://192.168.52.130/es/fenci.txt
5、 java代码交互elasticsearch
5.1、pom.xml
<elasticserach.version>7.4.2</elasticserach.version>
<dependency>
<groupId>org.elasticsearch.client</groupId>
<artifactId>elasticsearch-rest-high-level-client</artifactId>
<version>7.4.2</version>
</dependency>
5.2、配置文件configuration
package com.pshdhx.elasticsearch.config;
import org.apache.http.HttpHost;
import org.elasticsearch.client.RequestOptions;
import org.elasticsearch.client.RestClient;
import org.elasticsearch.client.RestClientBuilder;
import org.elasticsearch.client.RestHighLevelClient;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
/**
* @author pshdhx
* @date 2022-03-28 15:09
*/
@Configuration
public class ElasticSearchConfiguration {
public static final RequestOptions COMMON_OPTIONS;
static {
RequestOptions.Builder builder = RequestOptions.DEFAULT.toBuilder();
//addHeader();
//setHttpAsyncResponseCounsumerFactory
COMMON_OPTIONS = builder.build();
}
@Bean
public RestHighLevelClient EsRestClient(){
RestClientBuilder restClientBuilder = RestClient.builder(new HttpHost("82.xx.xx.xxxx", 9200, "http"));
RestHighLevelClient client = new RestHighLevelClient(restClientBuilder);
return client;
}
}
5.3、测试文件
package com.pshdhx.elasticsearch;
import com.alibaba.fastjson.JSON;
import com.pshdhx.elasticsearch.config.ElasticSearchConfig;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.ToString;
import org.elasticsearch.action.index.IndexRequest;
import org.elasticsearch.action.search.SearchRequest;
import org.elasticsearch.action.search.SearchResponse;
import org.elasticsearch.client.RestHighLevelClient;
import org.elasticsearch.common.xcontent.XContentType;
import org.elasticsearch.index.query.QueryBuilder;
import org.elasticsearch.index.query.QueryBuilders;
import org.elasticsearch.search.SearchHit;
import org.elasticsearch.search.SearchHits;
import org.elasticsearch.search.aggregations.Aggregation;
import org.elasticsearch.search.aggregations.AggregationBuilder;
import org.elasticsearch.search.aggregations.AggregationBuilders;
import org.elasticsearch.search.aggregations.Aggregations;
import org.elasticsearch.search.aggregations.bucket.terms.Terms;
import org.elasticsearch.search.aggregations.metrics.Avg;
import org.elasticsearch.search.builder.SearchSourceBuilder;
import org.junit.jupiter.api.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import javax.naming.directory.SearchResult;
import java.io.IOException;
import java.util.Map;
@SpringBootTest
class GuilimallEsApplicationTests {
@Autowired
private RestHighLevelClient restHighLevelClient;
@Test
void testSearchEs() throws IOException {
//1、创建检索请求
SearchRequest searchRequest = new SearchRequest();
//指定索引
searchRequest.indices("bank");
//指定DSL检索条件
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
sourceBuilder.query(QueryBuilders.matchQuery("address","mill"));
//测试聚合函数
sourceBuilder.aggregation(AggregationBuilders.terms("ageAgg").field("age").size(10));
sourceBuilder.aggregation(AggregationBuilders.avg("balanceAvg").field("balance"));
System.out.println(sourceBuilder.toString());
searchRequest.source(sourceBuilder);
//2、执行检索
SearchResponse response = restHighLevelClient.search(searchRequest, ElasticSearchConfig.COMMON_OPTIONS);
System.out.println(response.toString());
Map map = JSON.parseObject(response.toString(), Map.class);
SearchHits hits = response.getHits();
SearchHit[] hits1 = hits.getHits();
for (SearchHit hit : hits1) {
String index = hit.getIndex();
//Map<String, Object> sourceAsMap = hit.getSourceAsMap();
String sourceAsString = hit.getSourceAsString();
Account account = JSON.parseObject(sourceAsString, Account.class);
System.out.println("Account:"+account.toString());
}
//获取分析信息
Aggregations aggregations = response.getAggregations();
Terms ageAgg1 = aggregations.get("ageAgg");
for (Terms.Bucket bucket : ageAgg1.getBuckets()) {
String keyAsString = bucket.getKeyAsString();
System.out.println("年龄"+keyAsString+"===》"+bucket.getDocCount());
}
Avg balanceAvg1 = aggregations.get("balanceAvg");
System.out.println("平均薪资:"+balanceAvg1.getValue());
}
@Test
void contextLoads() {
System.out.println("111");
System.out.println(restHighLevelClient);
}
@Test
public void indexData() throws IOException {
IndexRequest indexRequest = new IndexRequest("users");
indexRequest.id("1");
// indexRequest.source("userName","zhangsan","age",18,"gender","男");
User user = new User("pshdhx",18,"man");
String jsonString = JSON.toJSONString(user);
IndexRequest index = indexRequest.source(jsonString, XContentType.JSON);
//执行操作
restHighLevelClient.index(indexRequest, ElasticSearchConfig.COMMON_OPTIONS);
System.out.println(index);
}
@Data
@AllArgsConstructor
class User{
private String userName;
private Integer age;
private String gender;
}
@Data
@ToString
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;
}
}
操作结果
{"query":{"match":{"address":{"query":"mill","operator":"OR","prefix_length":0,"max_expansions":50,"fuzzy_transpositions":true,"lenient":false,"zero_terms_query":"NONE","auto_generate_synonyms_phrase_query":true,"boost":1.0}}},"aggregations":{"ageAgg":{"terms":{"field":"age","size":10,"min_doc_count":1,"shard_min_doc_count":0,"show_term_doc_count_error":false,"order":[{"_count":"desc"},{"_key":"asc"}]}},"balanceAvg":{"avg":{"field":"balance"}}}}
{"took":1,"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":"_doc","_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":"_doc","_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":"_doc","_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":"_doc","_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"}}]},"aggregations":{"lterms#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}]},"avg#balanceAvg":{"value":25208.0}}}
Account:GuilimallEsApplicationTests.Account(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)
Account:GuilimallEsApplicationTests.Account(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)
Account:GuilimallEsApplicationTests.Account(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)
Account:GuilimallEsApplicationTests.Account(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)
年龄38===》2
年龄28===》1
年龄32===》1
平均薪资:25208.0
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