elasticsearch-rest-high-level-client操作elasticsearch
Posted 可——叹——落叶飘零
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文章目录
摘要
elasticsearch-rest-high-level-client操作elasticsearch
闲的无聊，于是写了这一篇爽文，米娜桑可直接用，除非几乎不可能有bug，有bug当我没说（doge）
QA：无想的一刀欧为啥不用springboot封装的操作依赖涅?
欧认为springboot对操作类过度封装，实现普通简单操作还行，但是涉及到较为复杂的操作时，难以使用，尤其是不同版本的springboot推出的api变化频繁，更加难以使用，es官方推出的api更新不会让操作类变化太频繁，个人感觉spboot操作不如es官方推出的api灵活强大，之前在工作中遇到的需求使用springboot提供的报错难以琢磨，且难以满足需求，所以使用了官方api
elasticsearch版本：7.4
安装操作文档：https://blog.csdn.net/UnicornRe/article/details/121747039?spm=1001.2014.3001.5501
依赖
依赖最好保持与es版本一致，如果以下依赖报错，在maven < parent > 同级标签旁加上
<properties>
<java.version>1.8</java.version>
<!-- <spring-cloud.version>2020.0.2</spring-cloud.version> -->
<!--解决版本问题-->
<elasticsearch.version>7.4.0</elasticsearch.version>
</properties>
<!--elasticsearch-->
<dependency>
<groupId>org.elasticsearch.client</groupId>
<artifactId>elasticsearch-rest-high-level-client</artifactId>
<version>7.4.0</version>
</dependency>
<dependency>
<groupId>org.elasticsearch</groupId>
<artifactId>elasticsearch</artifactId>
<version>7.4.0</version>
</dependency>
yml配置
可自行修改配置和代码增加多台es机器，address逗号隔开
elasticsearch:
schema: http
address: 192.168.52.43:9200
connectTimeout: 5000
socketTimeout: 5000
connectionRequestTimeout: 5000
maxConnectNum: 100
maxConnectPerRoute: 100
连接配置
import org.apache.http.HttpHost;
import org.elasticsearch.client.RestClient;
import org.elasticsearch.client.RestClientBuilder;
import org.elasticsearch.client.RestHighLevelClient;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.context.annotation.Scope;
import java.time.Duration;
import java.util.ArrayList;
import java.util.List;
@Configuration
public class EsHighLevalConfigure
//协议
@Value("$elasticsearch.schema:http")
private String schema="http";
// 集群地址，如果有多个用“,”隔开
@Value("$elasticsearch.address")
private String address;
// 连接超时时间
@Value("$elasticsearch.connectTimeout:5000")
private int connectTimeout;
// Socket 连接超时时间
@Value("$elasticsearch.socketTimeout:10000")
private int socketTimeout;
// 获取连接的超时时间
@Value("$elasticsearch.connectionRequestTimeout:5000")
private int connectionRequestTimeout;
// 最大连接数
@Value("$elasticsearch.maxConnectNum:100")
private int maxConnectNum;
// 最大路由连接数
@Value("$elasticsearch.maxConnectPerRoute:100")
private int maxConnectPerRoute;
@Bean
public static RestHighLevelClient restHighLevelClient()
List<HttpHost> hostLists = new ArrayList<>();
String[] hostList = address.split(",");
for (String addr : hostList)
String host = addr.split(":")[0];
String port = addr.split(":")[1];
hostLists.add(new HttpHost(host, Integer.parseInt(port), schema));
HttpHost[] httpHost = hostLists.toArray(new HttpHost[]);
// 构建连接对象
RestClientBuilder builder = RestClient.builder(httpHost);
// 异步连接延时配置
builder.setRequestConfigCallback(requestConfigBuilder ->
requestConfigBuilder.setConnectTimeout(connectTimeout);
requestConfigBuilder.setSocketTimeout(socketTimeout);
requestConfigBuilder.setConnectionRequestTimeout(connectionRequestTimeout);
return requestConfigBuilder;
);
// 异步连接数配置
builder.setHttpClientConfigCallback(httpClientBuilder ->
httpClientBuilder.setMaxConnTotal(maxConnectNum);
httpClientBuilder.setMaxConnPerRoute(maxConnectPerRoute);
httpClientBuilder.setKeepAliveStrategy((response, context) -> Duration.ofMinutes(5).toMillis());
return httpClientBuilder;
);
return new RestHighLevelClient(builder);
索引结构
虽然索引结构肯定不是和你们一样的，但是代码结构不需要伤经动骨,
我来简单说说这个结构吧，一条知识产权信息內包含n个文档annex，包含n个（申请人发明人）applicant，
所以使用了 “type”: “nested"嵌套类型，不晓得与"type”: "object"区别的小伙伴自行学习吧，这里就不多说了。
想要学习部分优化的，安装，数据迁移冷备份的可以看看我的文章：（东西太多，部分就没写）https://blog.csdn.net/UnicornRe/article/details/121747039?spm=1001.2014.3001.5501
PUT /intellectual
"settings":
"number_of_shards": 1,
"number_of_replicas": 1
PUT /intellectual/_mapping
"properties":
"id":
"type": "long"
,
"name":
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
,
"type":
"type": "keyword"
,
"keycode":
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
,
"officeId":
"type": "keyword"
,
"officeName":
"type": "keyword"
,
"titular":
"type": "keyword"
,
"applyTime":
"type": "long"
,
"endTime":
"type": "long"
,
"status":
"type": "keyword"
,
"agentName":
"type": "text",
"analyzer": "ik_smart",
"search_analyzer": "ik_smart"
,
"annex":
"type": "nested",
"properties":
"id":
"type": "long"
,
"name":
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
,
"content":
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_max_word"
,
"createTime":
"type": "long"
,
"applicant":
"type": "nested",
"properties":
"id":
"type": "long"
,
"applicantId":
"type": "long"
,
"isOffice":
"type": "integer"
,
"userName":
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
,
"outUsername":
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
普通常见非嵌套结构的CRUD
先不管"type": "nested"嵌套的对象，只对普通字段操作
我先定义一个实体类IntellectualEntity字段和上面的mapping一致
所有操作都注入了RestHighLevelClient restHighLevelClient
新增
public void insertIntel(IntellectualEntity intellectualEntity) throws IOException
//intellectual为索引名
IndexRequest indexRequest = new IndexRequest("intellectual")
.source(JSON.toJSONString(intellectualEntity), XContentType.JSON)
.setRefreshPolicy(WriteRequest.RefreshPolicy.IMMEDIATE)
.id(intellectualEntity.getId()+"");//手动指定es文档的id
IndexResponse out = restHighLevelClient.index(indexRequest, RequestOptions.DEFAULT);
log.info("状态：", out.status());
更新（根据id更新）
只会更新entity不为空的字段，如同mybatisplus默认自带的update
因为es文档的id一定唯一，所以方法最多只能更新一条
public void updateIntel(IntellectualEntity entity) throws IOException
//根据IntellectualEntity的id更新文档
UpdateRequest updateRequest = new UpdateRequest("intellectual", entity.getId()+"");
byte[] json = JSON.toJSONBytes(entity);
updateRequest.doc(json, XContentType.JSON);
UpdateResponse response = restHighLevelClient.update(updateRequest, RequestOptions.DEFAULT);
log.info("状态：", response.status());
更新（高级，根据搜索条件更新，采用无痛painless脚本）
painless脚本适用很多业务复杂的场合，比如如下更新值字段为map里的字段
private void updateByQuery(IntellectualEntity entity) throws IOException
UpdateByQueryRequest updateByQueryRequest = new UpdateByQueryRequest();
updateByQueryRequest.indices("intellectual");
//搜索条件为id(因为插入时指定doc的id和实体类id一致，这样就保证了搜索结果唯一)
//如果搜索条件查出的结果很多，使用需谨慎
updateByQueryRequest.setQuery(new TermQueryBuilder("id", entity.getId()));
//map存储脚本实体参数值
Map<String,Object> map=new HashMap<>();
map.put("intelName", entity.getName());
map.put("intelStatus", entity.getStatus());
map.put("intelApplyTime", entity.getApplyTime());
map.put("intelKeyCode", entity.getKeycode());
map.put("intelEndTime", entity.getEndTime());
map.put("intelType", entity.getType());
map.put("intelTitular", entity.getTitular());
//指定哪些字段需要更新,ctx._source.xxx为es的字段，使用map的值赋值更新
updateByQueryRequest.setScript(new Script(ScriptType.INLINE,
"painless",
"ctx._source.intelName=params.intelName;" +
"ctx._source.intelStatus=params.intelStatus;"+
"ctx._source.intelApplyTime=params.intelApplyTime;"+
"ctx._source.intelKeyCode=params.intelKeyCode;"+
"ctx._source.intelType=params.intelType;"+
"ctx._source.intelTitular=params.intelTitular;"
, map));
BulkByScrollResponse bulkByScrollResponse = restHighLevelClient.updateByQuery(updateByQueryRequest, RequestOptions.DEFAULT);
log.info("创建状态：", bulkByScrollResponse.getStatus());
删除
public void deleteIntel(IntellectualEntity entity) throws IOException
DeleteRequest deleteRequest=new DeleteRequest("intellectual",entity.getId()+"");
DeleteResponse deleteResponse = restHighLevelClient.delete(deleteRequest, RequestOptions.DEFAULT);
log.info("状态：", deleteResponse.status());
删除（根据搜索条件删除）
和更新搜索条件操作类似，结合删除操作替换DeleteRequest为DeleteByQueryRequest，相信机智的你已经会了
搜索高亮（普通高亮，空格多条件搜索）
这块代码暂时不涉及nested的字段的嵌套高亮
条件设置时，should=or，must=and
步骤：设置高亮构造器->搜索出结果->将高亮数据替换掉非高亮数据->返回结果
先写一个高亮构造器吧
高亮构造器：
private static void HighlightBuilder highlightBuilder;
static
highlightBuilder = new HighlightBuilder();
highlightBuilder.numOfFragments(0);//从第一个分片获取高亮片段
highlightBuilder.preTags("<font color='#e75213'>");//自定义高亮标签
highlightBuilder.postTags("</font>");
highlightBuilder.highlighterType("unified");//高亮类型
highlightBuilder
.field("name")//需要高亮的属性值
.field("keycode")
;
highlightBuilder.requireFieldMatch(false);
搜索步骤：
public List<Map<String,Object>> queryByContent(String content,Integer pageCurrent, Date startTimeApply,Date endTimeApply,Date startTimeEnd,Date endTimeEnd ) throws IOException
//空格分割多条件，本搜索支持多搜索词条空格分开，多词条搜索关系用and
String[] manyStr = content.split("\\\\s+");
//定义一个list<map>作为返回结果
List<Map<String,Object>> list = new LinkedList<>();
//首先构造条件构造器
BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery();
if(manyStr.length>1)
for (int i=0;i<manyStr.length;i++)
BoolQueryBuilder innerBoolQueryBuilder = QueryBuilders.boolQuery();
//nestedQuery，嵌套搜索条件
innerBoolQueryBuilder.should(QueryBuilders.nestedQuery("annex",QueryBuilders.matchQuery("annex.content", manyStr[i]) , ScoreMode.Max).boost(2));
innerBoolQueryBuilder