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电商项目高级篇-02 elasticsearch-下
4.2、返回指定字段
返回单个字段
GET bank/_search { "query": { "match_all": {} } , "sort": [ { "balance": { "order": "desc" } } ] , "from": 5 , "size": 5 , "_source": "balance" }- 1
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返回多个字段：GET bank/_search { "query": { "match_all": {} } , "sort": [ { "balance": { "order": "desc" } } ] , "from": 5 , "size": 5 , "_source": ["balance","age"] }- 1
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4.3、match查询
精确查询（对非字符串）
GET bank/_search { "query": { "match": { "account_number": "20" } } }- 1
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模糊查询（对字符串）全文检索GET bank/_search { "query": { "match": { "address": "kings" } } }- 1
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###根据评分倒排索引
GET bank/_search { "query": { "match": { "address": "mill road" } } }- 1
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得益于倒排索引机制。会分词匹配。查询出结果
4.4、短语匹配：match_phrase(用于短语匹配。包含匹配)
GET bank/_search { "query": { "match_phrase": { "address": "mill road" } } }- 1
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###字段.keyword相当于精确匹配GET bank/_search { "query": { "match": { "address.keyword": "mill road" } } }- 1
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4.5、多字段匹配：multi_match
GET bank/_search { "query": { "multi_match": { "query": "mill", "fields": ["state","address"] } } }- 1
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state或者address中包含mill的
4.6、bool复合查询
GET bank/_search { "query": { "bool": { "must": [ { "match": { "address": "mill" } }, { "match": { "gender": "M" } } ], "should": [ { "match": { "address": "lane" } } ], "must_not": [ { "match": { "email": "baluba.com" } } ] } } }- 1
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查询一定是地址包含mill和性别是M的，一定不是email包含baluba.com的，可以是address包含lane的结果集
4.7、filter过滤查询（不贡献相关性得分）
GET bank/_search { "query": { "bool": { "filter": { "range": { "balance": { "gte": 10000, "lte": 20000 } } } } } }- 1
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GET bank/_search { "query": { "bool": { "must": [ { "match": { "address": "mill" } } ], "filter": { "range": { "balance": { "gte": 10000, "lte": 20000 } } } } } }- 1
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4.8、term（非文本检索）
match 可适用于文本检索。term适用于非文本检索
GET bank/_search { "query": { "bool": { "must": [ {"term": { "age": { "value": "28" } }}, {"match": { "address": "990 Mill Road" }} ] } } }- 1
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4.9、执行聚合
需求一：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
GET bank/_search { "query": { "match": { "address": "mill" } }, "aggs": { "group_by_state": { "terms": { "field": "age" } }, "avg_age": { "avg": { "field": "age" } } }, "size": 0 }- 1
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aggs:聚合函数
terms：分布
avg：平均
size：0 不显示hits详情需求二：按照年龄聚合，并且请求这些年龄段的这些人的平均薪资
会用到子聚合
GET bank/_search { "query": { "match_all": {} }, "aggs": { "age_avg": { "terms": { "field": "age", "size": 1000 } } } }- 1
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子聚合：GET bank/_search { "query": { "match_all": {} }, "aggs": { "age_avg": { "terms": { "field": "age", "size": 1000 }, "aggs":{ "balance_avg":{ "avg": { "field": "balance" } } } } } }- 1
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需求三：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
段的总体平均薪资多重子聚合
GET bank/_search { "query": { "match_all": {} }, "aggs": { "age_agg": { "terms": { "field": "age", "size": 100 }, "aggs": { "gender_agg": { "terms": { "field": "gender.keyword", "size": 100 }, "aggs": { "balance_avg": { "avg": { "field": "balance" } } } }, "balance_avg": { "avg": { "field": "balance" } } } } } }- 1
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4.10、映射
查询映射
GET /bank/_mapping- 1
修改映射
PUT /my-index { "mappings": { "properties": { "age": { "type": "integer" }, "email": { "type": "keyword" }, "name": { "type": "text" } } } }- 1
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添加新的字段映射PUT /my-index/_mapping { "properties": { "employee-id": { "type": "keyword", "index": false } } }- 1
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更新映射
对于已经存在的映射字段，我们不能更新。更新必须创建新的索引进行数据迁移##数据迁移
1、获取原来银行的映射关系
GET /bank/_mapping- 1
{ "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 } } } } } } }- 1
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2、创建出新银行映射规则
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" }, "gender": { "type": "keyword" }, "lastname": { "type": "text", "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } }, "state": { "type": "keyword" } } } }- 1
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3、查询新规则
GET /newbank/_mapping- 1
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4、数据迁移
POST _reindex { "source": { "index": "bank" }, "dest": { "index": "newbank" } }- 1
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5、查询新索引
GET /newbank/_search- 1
4.11、ik分词器
标准分词器不好用，是专门用来分割英文的。不太支持中文（它会分成一个一个字的）
比如：POST /_analyze { "analyzer": "standard", "text":"今天的清蒸鲈鱼蒸得还行" }- 1
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下载ik分词器
添加链接描述复制到linux这个文件夹下
确认是否安装好分词器进到容器内部，然后输入命令：
elasticsearch plugin list- 1
出现ik说明安装成功。然后重启容器
然后执行这段代码：
POST _analyze { "analyzer": "ik_smart", "text": "今天的清蒸鲈鱼蒸得还行" }- 1
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目前还稍微比较智能化一些了
但是实际上不能满足网络上新的单词搜索
POST /_analyze { "analyzer": "ik_max_word", "text": "你在干嘛哎哟" }- 1
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4.12、自定义分词
4.12.1、linux下安装nginx
1、暂时先启动一个nginx
docker run -p 80:80 --name nginx -d nginx:1.10- 1
2、将容器内的配置文件拷贝到当前目录
docker container cp nginx:/etc/nginx .- 1
3、修改文件名称
cd ../ mv nginx conf- 1
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4、把这个 conf 移动到/mydata/nginx 下
5、执行命令删除原容器：docker rm $ContainerId
6、创建新的 nginx
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- 1
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将index.html放到html文件里。就可以访问nginx首页了
##7、将所有es关于分词器的内容，nginx这边新建文件夹保管
8、访问该fcword.txt
可以访问
###9、ik分词器绑定fcword.txt
cd /mydata/elasticsearch/plugins/ik/config- 1
vi IKAnalyzer.cfg.xml- 1
开启远程配置
虚拟机地址/es/fcword.txt- 1
分词前：
重启容器实例分词后：
已达到分词效果5、java集成es
5.1、模块搭建
新建module
创建一个springboot模块
pom.xml4.0.0 org.springframework.boot spring-boot-starter-parent 2.1.8.RELEASE com.ljs.gulimall gulimall-search 0.0.1-SNAPSHOT gulimall-search 检索服务 1.8 7.4.2 com.ljs.gulimall gulimall-common 0.0.1-SNAPSHOT org.springframework.boot spring-boot-starter-web org.elasticsearch.client elasticsearch-rest-high-level-client ${elasticsearch.version} org.springframework.boot spring-boot-starter-test test org.springframework.boot spring-boot-maven-plugin - 1
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application.properties
spring.cloud.nacos.discovery.server-addr=127.0.0.1:8848 spring.application.name=gulimall-search- 1
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GulimallSearchApplication
package com.ljs.gulimall.search; import org.springframework.boot.SpringApplication; import org.springframework.boot.autoconfigure.SpringBootApplication; import org.springframework.boot.autoconfigure.jdbc.DataSourceAutoConfiguration; import org.springframework.cloud.client.discovery.EnableDiscoveryClient; @EnableDiscoveryClient @SpringBootApplication(exclude = DataSourceAutoConfiguration.class) public class GulimallSearchApplication { public static void main(String[] args) { SpringApplication.run(GulimallSearchApplication.class, args); } }- 1
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GulimallElasticsearchConfig
package com.ljs.gulimall.search.config; import org.apache.http.HttpHost; import org.elasticsearch.client.RestClient; import org.elasticsearch.client.RestHighLevelClient; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration; @Configuration public class GulimallElasticsearchConfig { @Bean public RestHighLevelClient getClient(){ return new RestHighLevelClient(RestClient.builder(new HttpHost("虚拟机ip", 9200,"http"))); } }- 1
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测试类
GulimallSearchApplicationTestspackage com.ljs.gulimall.search; import org.elasticsearch.client.RestHighLevelClient; import org.junit.Test; import org.junit.runner.RunWith; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.boot.test.context.SpringBootTest; import org.springframework.test.context.junit4.SpringRunner; @RunWith(SpringRunner.class) @SpringBootTest public class GulimallSearchApplicationTests { @Autowired private RestHighLevelClient client; @Test public void contextLoads() { System.out.println(client); } }- 1
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运行结果：
已经获取到bean容器的实例
5.2、es index 保存数据
GET /user/_search- 1
没有user这个索引GulimallElasticsearchConfig
public static final RequestOptions COMMON_OPTIONS; static { RequestOptions.Builder builder = RequestOptions.DEFAULT.toBuilder(); COMMON_OPTIONS = builder.build(); }- 1
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GulimallSearchApplicationTests
@Test public void indexData() throws IOException { // 创建indexRequest对象user index索引 IndexRequest indexRequest = new IndexRequest("user"); // 设置索引id indexRequest.id("1"); User user = new User(); user.setAge(18); user.setGender("男"); user.setName("帅淞"); // 要保存的内容转成JSON String String jsonString = JSON.toJSONString(user); // 将保存的内容放到indexRequest对象 indexRequest.source(jsonString, XContentType.JSON); // 执行操作 IndexResponse index = client.index(indexRequest, GulimallElasticsearchConfig.COMMON_OPTIONS); // 提取响应数据 System.out.println(index); } @Data class User{ private Integer age; private String name; private String gender; }- 1
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执行完毕说明保存成功。
再次查询。发现有值
5.3、复杂检索
GulimallSearchApplicationTests@Test public void searchData() throws IOException { // 创建检索请求 SearchRequest searchRequest = new SearchRequest(); // 指定索引 searchRequest.indices("bank"); // 指定检索条件 SearchSourceBuilder sourceBuilder = new SearchSourceBuilder(); // 构造检索条件 sourceBuilder.query(QueryBuilders.matchQuery("address","mill")); System.out.println(sourceBuilder.toString()); searchRequest.source(sourceBuilder); // 执行操作 SearchResponse response = client.search(searchRequest, GulimallElasticsearchConfig.COMMON_OPTIONS); // 提取响应数据 System.out.println(response.toString()); }- 1
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打印检索条件
{ "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 } } } }- 1
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打印检索结果
{ "took": 5, "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" } }] } }- 1
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###构造聚合条件
TermsAggregationBuilder termBuilder = AggregationBuilders.terms("group_by_state").field("age"); AvgAggregationBuilder avgBuilder = AggregationBuilders.avg("avg_age").field("age"); sourceBuilder.aggregation(termBuilder); sourceBuilder.aggregation(avgBuilder);- 1
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{ "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": { "group_by_state": { "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" }] } }, "avg_age": { "avg": { "field": "age" } } } }- 1
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返回结果：
{ "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" } }] }, "aggregations": { "avg#avg_age": { "value": 34.0 }, "lterms#group_by_state": { "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 }] } } }- 1
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java与在es上执行结果是一致的
###获取所有查到的数据
使用在线json生成工具，再生成一个实体类
将生成的实体类复制成静态内部类。到测试类中
// 获取到所有查询到的数据 SearchHits hits = response.getHits(); SearchHit[] searchHits = hits.getHits(); for (SearchHit searchHit : searchHits) { // 获取到searchHit的source的JSON字符串。转换成实体类 String sourceAsString = searchHit.getSourceAsString(); Account account = JSON.parseObject(sourceAsString, Account.class); System.out.println("account:"+account); }- 1
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@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; }- 1
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打印效果：
###获取检索分布信息
// 获取检索到的分析信息 Aggregations aggregations = response.getAggregations(); Terms groupTerm = aggregations.get("group_by_state"); Avg avg = aggregations.get("avg_age"); for (Terms.Bucket bucket : groupTerm.getBuckets()) { System.out.println("年龄:"+bucket.getKey()+"分布人数："+bucket.getDocCount()); } System.out.println("年龄平均值："+avg.getValue());- 1
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- 原文地址：https://blog.csdn.net/ljs13168734665/article/details/133327250
