VSCode部署
使用RESTful API操作ES，VSCode是个好工具。
VSCode官网免费的。。。
最好把这两插件也安装好：
Chinese (Simplified) Language Pack for Visual Studio Code
Elasticsearch for VSCode
一个是汉化包，一个是开发ES需要的拓展包。开发ES需要new一个.es或者打开一个已有的.es文件才能编辑。。。如果不小心关闭了连接地址栏，可以右键→命令面板→set host：
输入node1:9200即可连接：
--标准分词
post _analyze
{
"analyzer":"standard",
"text":"好喜欢数码宝贝"
}
--IK分词
post _analyze
{
"analyzer":"ik_max_word",
"text":"好喜欢数码宝贝"
}
简单测试下，点Run Query就能运行，发现标准分词：
{
"tokens": [
{
"token": "好",
"start_offset": 0,
"end_offset": 1,
"type": "<IDEOGRAPHIC>",
"position": 0
},
{
"token": "喜",
"start_offset": 1,
"end_offset": 2,
"type": "<IDEOGRAPHIC>",
"position": 1
},
{
"token": "欢",
"start_offset": 2,
"end_offset": 3,
"type": "<IDEOGRAPHIC>",
"position": 2
},
{
"token": "数",
"start_offset": 3,
"end_offset": 4,
"type": "<IDEOGRAPHIC>",
"position": 3
},
{
"token": "码",
"start_offset": 4,
"end_offset": 5,
"type": "<IDEOGRAPHIC>",
"position": 4
},
{
"token": "宝",
"start_offset": 5,
"end_offset": 6,
"type": "<IDEOGRAPHIC>",
"position": 5
},
{
"token": "贝",
"start_offset": 6,
"end_offset": 7,
"type": "<IDEOGRAPHIC>",
"position": 6
}
]
}
一个字一个字划分的。。。好吧。。。IK分词：
{
"tokens": [
{
"token": "好喜欢",
"start_offset": 0,
"end_offset": 3,
"type": "CN_WORD",
"position": 0
},
{
"token": "喜欢",
"start_offset": 1,
"end_offset": 3,
"type": "CN_WORD",
"position": 1
},
{
"token": "数码宝贝",
"start_offset": 3,
"end_offset": 7,
"type": "CN_WORD",
"position": 2
},
{
"token": "数码",
"start_offset": 3,
"end_offset": 5,
"type": "CN_WORD",
"position": 3
},
{
"token": "宝贝",
"start_offset": 5,
"end_offset": 7,
"type": "CN_WORD",
"position": 4
}
]
}
好家伙，连数码宝贝都能划分出来。。。真强。。。
RESTful API
索引库管理
列举索引
GET _cat/indices
这句类似MySQL中的show databases;用于查询当前所有索引库：
[]
当然目前还没有。。。
创建job_idx索引库
PUT /job_idx
{
"mappings": {
"properties" : {
"area": { "type": "text", "store": true, "analyzer": "ik_max_word"},
"exp": { "type": "text", "store": true, "analyzer": "ik_max_word"},
"edu": { "type": "keyword", "store": true},
"salary": { "type": "keyword", "store": true},
"job_type": { "type": "keyword", "store": true},
"cmp": { "type": "text", "store": true, "analyzer": "ik_max_word"},
"pv": { "type": "keyword", "store": true},
"title": { "type": "text", "store": true, "analyzer": "ik_max_word"},
"jd": { "type": "text", "store": true, "analyzer": "ik_max_word"}
}
},
"settings" : {
"number_of_shards":5,
"number_of_replicas" : 1
}
}
其中：
mappings：用于做列的定义
type：类型
store：存储原始数据
analyzer：分词器的类型
index：是否对这列构建索引，默认为true
settings：集群配置管理
"number_of_shards":5,：这个索引库有5个分区
"number_of_replicas" : 1：副本个数为1
运行后，在浏览器node1:9200：
（边框加粗的是Leader分区，边框细的是Follower分区）
5个分区和1倍数的备份。。。和预期一致。。。
查看索引
GET /job_idx/_mapping
GET /job_idx/_settings
也可以在网页端：
信息→索引状态/索引信息查看。
删除索引
delete /job_idx
执行后网页端刷新，不再有内容。
数据管理
数据插入
按照这种套路：
put /index/_doc/doc_id
{
JSON：每一列的数据
}
可以插入数据。例如：
PUT /job_idx/_doc/29097
{
"area": "深圳-南山区",
"exp": "1年经验",
"edu": "大专以上",
"salary": "6-8千/月",
"job_type": "实习",
"cmp": "乐有家",
"pv": "61.6万人浏览过 / 14人评价 / 113人正在关注",
"title": "桃园 深大销售实习 岗前培训",
"jd": "薪酬待遇】 本科薪酬7500起 大专薪酬6800起 以上无业绩要求，同时享有业绩核算比例55%~80% 人均月收入超1.3万 【岗位职责】 1.爱学习，有耐心： 通过公司系统化培训熟悉房地产基本业务及相关法律、金融知识，不功利服务客户，耐心为客户在房产交易中遇到的各类问题； 2.会聆听，会提问： 详细了解客户的核心诉求，精准匹配合适的产品信息，具备和用户良好的沟通能力，有团队协作意识和服务意识； 3.爱琢磨，善思考: 热衷于用户心理研究，善于从用户数据中提炼用户需求，利用个性化、精细化运营手段，提升用户体验。 【岗位要求】 1.18-26周岁，自考大专以上学历； 2.具有良好的亲和力、理解能力、逻辑协调和沟通能力； 3.积极乐观开朗，为人诚实守信，工作积极主动，注重团队合作； 4.愿意服务于高端客户，并且通过与高端客户面对面沟通有意愿提升自己的综合能力； 5.愿意参加公益活动，具有爱心和感恩之心。 【培养路径】 1.上千堂课程;房产知识、营销知识、交易知识、法律法规、客户维护、目标管理、谈判技巧、心理学、经济学; 2.成长陪伴：一对一的师徒辅导 3.线上自主学习平台：乐有家学院，专业团队制作，每周大咖分享 4.储备及管理课堂： 干部训练营、月度/季度管理培训会 【晋升发展】 营销【精英】发展规划：A1置业顾问-A6资深置业专家 营销【管理】发展规划：（入职次月后就可竞聘） 置业顾问-置业经理-店长-营销副总经理-营销副总裁-营销总裁 内部【竞聘】公司职能岗位：如市场、渠道拓展中心、法务部、按揭经理等都是内部竞聘 【联系人】 黄媚主任15017903212（微信同号）"
}
执行后result.json显示内容为：
{
"_index": "job_idx",
"_type": "_doc",
"_id": "29097",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 2,
"failed": 0
},
"_seq_no": 0,
"_primary_term": 1
}
在网页端刷新后在数据浏览分栏看到：
数据更新
可以这样：
POST /job_idx/_update/29097
{
"doc": {
"salary": "15-20千/月"
}
}
删除数据
DELETE /job_idx/_doc/29097
右侧的result.json会显示：
{
"_index": "job_idx",
"_type": "_doc",
"_id": "29097",
"_version": 2,
"result": "deleted",
"_shards": {
"total": 2,
"successful": 2,
"failed": 0
},
"_seq_no": 1,
"_primary_term": 1
}
显然记录了版本号及删除的信息。
BulkLoad
curl -H "Content-Type: application/json" -XPOST "node1:9200/job_idx/_bulk?pretty&refresh" --data-binary "@job_info.json"
这种方式先将文件上传到Linux再批量加载文件：
数据查询
doc_id查询
类似MySQL根据主键查询。例如：
GET /job_idx/_search
{
"query": {
"ids": {
"values": ["46313"]
}
}
}
查询器
GET /job_idx/_search
{
"query": {
"match": {
"jd": "销售"
}
},
"size":"100"
}
默认值显示10条，使用size标签可以显示多条。
或者使用multi_match实现多列字符串匹配查询器：
GET /job_idx/_search
{
"query": {
"multi_match": {
"query": "销售",
"fields": ["title", "jd"]
}
}
}
或者分页查询：
GET /job_idx/_search
{
"from": 0,
"size": 5,
"query": {
"multi_match": {
"query": "销售",
"fields": ["title", "jd"]
}
}
}
这种浅分页类似MySQL的limit M,N。from是开始条目的偏移量（第一条为0），size是每页显示的条目数。
自然还有深分页：
第一次查询，会将整个所有数据放在内存中，从第二次开始就从内存中来自动遍历每页的数据。scroll = 1m ：做深分页，将查询的结果保存在内存中1分钟。size：每一页显示多少条（能够自动进行翻页）。
例如：
第一次：
GET /job_idx/_search?scroll=1m
{
"query": {
"multi_match": {
"query": "销售",
"fields": ["title", "jd"]
}
},
"size": 100
}
在result.json中抓取到：
"_scroll_id": "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAAIWdmVueGpVeHRRd09hX3pWMU1uRUxwQQ==",
之后使用该_scroll_id就可以实现连续翻页：
GET _search/scroll?scroll=1m
{
"scroll_id": "DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAAAnFlJfaWNiQzhvU3YydTJpMUV1OUpIZlEAAAAAAAAAIhZBTEhIaW9WS1NBT3d4X3lKWXNIREN3AAAAAAAAACgWUl9pY2JDOG9TdjJ1MmkxRXU5SkhmUQAAAAAAAAAkFmlfMXlVOHB0VGYtbUFMMUdUd1JFWVEAAAAAAAAAIxZpXzF5VThwdFRmLW1BTDFHVHdSRVlR"
}
Java API
准备工作
代码太多。。。使用//region和//endregion折叠。。。
导入Maven依赖
pom.xml中：
<dependencies>
<dependency>
<groupId>org.elasticsearch.client</groupId>
<artifactId>elasticsearch-rest-high-level-client</artifactId>
<version>7.6.1</version>
</dependency>
<dependency>
<groupId>org.apache.logging.log4j</groupId>
<artifactId>log4j-core</artifactId>
<version>2.11.1</version>
</dependency>
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>fastjson</artifactId>
<version>1.2.62</version>
</dependency>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>4.12</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.testng</groupId>
<artifactId>testng</artifactId>
<version>6.14.3</version>
<scope>test</scope>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.1</version>
<configuration>
<target>1.8</target>
<source>1.8</source>
</configuration>
</plugin>
</plugins>
</build>
封装Java Bean对象
package com.aa.esClient.bean;
import com.alibaba.fastjson.annotation.JSONField;
public class JobDetail {
//region 封装数据
//无需将id序列化为文档
@JSONField(serialize = false)
private long id; // 唯一标识
private String area; // 职位所在区域
private String exp; // 岗位要求的工作经验
private String edu; // 学历要求
private String salary; // 薪资范围
private String job_type; // 职位类型（全职/兼职）
private String cmp; // 公司名
private String pv; // 浏览量
private String title; // 岗位名称
private String jd; // 职位描述
//endregion
//region get&set
public long getId() {
return id;
}
public void setId(long id) {
this.id = id;
}
public String getArea() {
return area;
}
public void setArea(String area) {
this.area = area;
}
public String getExp() {
return exp;
}
public void setExp(String exp) {
this.exp = exp;
}
public String getEdu() {
return edu;
}
public void setEdu(String edu) {
this.edu = edu;
}
public String getSalary() {
return salary;
}
public void setSalary(String salary) {
this.salary = salary;
}
public String getJob_type() {
return job_type;
}
public void setJob_type(String job_type) {
this.job_type = job_type;
}
public String getCmp() {
return cmp;
}
public void setCmp(String cmp) {
this.cmp = cmp;
}
public String getPv() {
return pv;
}
public void setPv(String pv) {
this.pv = pv;
}
public String getTitle() {
return title;
}
public void setTitle(String title) {
this.title = title;
}
public String getJd() {
return jd;
}
public void setJd(String jd) {
this.jd = jd;
}
//endregion
//region 重写toString方法
@Override
public String toString() {
return "JobDetail{" +
"id=" + id +
", area='" + area + '\'' +
", exp='" + exp + '\'' +
", edu='" + edu + '\'' +
", salary='" + salary + '\'' +
", job_type='" + job_type + '\'' +
", cmp='" + cmp + '\'' +
", pv='" + pv + '\'' +
", title='" + title + '\'' +
", jd='" + jd + '\'' +
'}';
}
//endregion
}
封装接口
package com.aa.esClient.service;
import com.aa.esClient.bean.JobDetail;
import java.io.IOException;
import java.util.List;
import java.util.Map;
public interface JobFullTextService {
//region 封装方法
//添加一个职位数据
void add(JobDetail jobDetail);
// 修改职位薪资
void update(JobDetail jobDetail) throws IOException;
// 根据ID删除指定位置数据
void deleteById(long id) throws IOException;
// 根据ID检索指定职位数据
JobDetail findById(long id) throws IOException;
// 根据关键字检索数据
List<JobDetail> searchByKeywords(String keywords) throws IOException;
// 分页检索
Map<String, Object> searchByPage(String keywords, int pageNum, int pageSize) throws IOException;
// scroll分页解决深分页问题
Map<String, Object> searchByScrollPage(String keywords, String scrollId, int pageSize) throws IOException;
// 关闭ES连接
void close() throws IOException;
//endregion
}
编写实现类
package com.aa.esClient.impl;
import com.aa.esClient.bean.JobDetail;
import com.aa.esClient.service.JobFullTextService;
import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import org.apache.http.HttpHost;
import org.elasticsearch.action.delete.DeleteRequest;
import org.elasticsearch.action.get.GetRequest;
import org.elasticsearch.action.get.GetResponse;
import org.elasticsearch.action.index.IndexRequest;
import org.elasticsearch.action.search.SearchRequest;
import org.elasticsearch.action.search.SearchResponse;
import org.elasticsearch.action.search.SearchScrollRequest;
import org.elasticsearch.action.update.UpdateRequest;
import org.elasticsearch.client.RequestOptions;
import org.elasticsearch.client.RestClient;
import org.elasticsearch.client.RestHighLevelClient;
import org.elasticsearch.common.unit.TimeValue;
import org.elasticsearch.common.xcontent.XContentType;
import org.elasticsearch.index.query.MultiMatchQueryBuilder;
import org.elasticsearch.index.query.QueryBuilders;
import org.elasticsearch.search.SearchHit;
import org.elasticsearch.search.SearchHits;
import org.elasticsearch.search.builder.SearchSourceBuilder;
import java.io.IOException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
public class JobFullTextServiceImpl implements JobFullTextService {
//region 初始化
//定义了一个连接对象
private RestHighLevelClient restHighLevelClient;
//定义索引库的名称
private static final String JOB_IDX_NAME = "job_idx";
//构造函数，用于new实例的时候，构建连接
public JobFullTextServiceImpl() {
restHighLevelClient = new RestHighLevelClient(RestClient.builder(
new HttpHost("node1", 9200, "http")
, new HttpHost("node2", 9200, "http")
, new HttpHost("node3", 9200, "http")
));
}
//endregion
//region 实现接口的方法
//region 往ES中写入一条JSON格式的数据
@Override
public void add(JobDetail jobDetail) {
//构建了一个索引请求器对象，用于写入，制定了请求器请求的索引库的名称
IndexRequest indexRequest = new IndexRequest(JOB_IDX_NAME);
//封装数据到请求器中：doc_Id + JSON数据
//从参数的对象中获取id作为docId
indexRequest.id(jobDetail.getId() +"");
//将JavaBean对象转换为JSON字符串
String jsonString = JSON.toJSONString(jobDetail);
//将JSON数据加载到请求器中，指定数据为JSON格式
indexRequest.source(jsonString, XContentType.JSON);
try {
//客户端连接调用index方法实现写入：第一个参数是索引请求器
restHighLevelClient.index(indexRequest, RequestOptions.DEFAULT);
} catch (IOException e) {
e.printStackTrace();
}
}
//endregion
//region 实现更新，将新的数据替换老的数据
@Override
public void update(JobDetail jobDetail) throws IOException {
//先判断是否存在
GetRequest getRequest = new GetRequest(JOB_IDX_NAME);
getRequest.id(jobDetail.getId()+"");
//判断是否存在
boolean exists = restHighLevelClient.exists(getRequest, RequestOptions.DEFAULT);
//如果不存在，方法调用结束
if(!exists) return;
//构建更新请求器：索引库名 + 指定更新的docid
UpdateRequest updateRequest = new UpdateRequest(JOB_IDX_NAME,jobDetail.getId()+"");
//更新请求器加载新数据
updateRequest.doc(JSON.toJSONString(jobDetail),XContentType.JSON);
//调用客户端连接中的更新方法
restHighLevelClient.update(updateRequest,RequestOptions.DEFAULT);
}
//endregion
//region 定义删除功能
@Override
public void deleteById(long id) throws IOException {
//构建删除请求器
DeleteRequest deleteRequest = new DeleteRequest(JOB_IDX_NAME);
//添加指定删除的docid
deleteRequest.id(id+"");
//调用删除方法
restHighLevelClient.delete(deleteRequest,RequestOptions.DEFAULT);
}
//endregion
//region 用于通过docId来查找数据
@Override
public JobDetail findById(long id) throws IOException {
//构建一个Get请求器对象
GetRequest getRequest = new GetRequest(JOB_IDX_NAME);
//指定get请求器的docid
getRequest.id(id+"");
//客户端连接对象调用get方法来获取某个docId对应的数据：传递get请求器
GetResponse documentFields = restHighLevelClient.get(getRequest, RequestOptions.DEFAULT);
//将每一列对应的数据取出转换为JSON String
String sourceAsString = documentFields.getSourceAsString();
//将JSONString转换为JavaBean独享
JobDetail jobDetail = JSON.parseObject(sourceAsString, JobDetail.class);
return jobDetail;
}
//endregion
//region 关键词查询
@Override
public List<JobDetail> searchByKeywords(String keywords) throws IOException {
List<JobDetail> lists = new ArrayList<>();//用作返回值
//构建Search的请求器
SearchRequest searchRequest = new SearchRequest();
//构建条件的建造器
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
//构建一个符合需求的查询器
MultiMatchQueryBuilder multiMatchQueryBuilder = QueryBuilders.multiMatchQuery(keywords, "jd", "title");
// MatchQueryBuilder c1 = QueryBuilders.matchQuery(keywords, "jd");//关键词单列查询器，模糊查询，做分词
// TermQueryBuilder c2 = QueryBuilders.termQuery(keywords, "jd");//关键词单列查询器，精准查询，不做分词
// RangeQueryBuilder c3 = QueryBuilders.rangeQuery("age").gt("18").lt("30");//范围查询 gt代表big than>，lt代表less than<
// QueryBuilders.boolQuery().must(c2).must(c3).should(c1); //must代表and并列，should表示or或，多条件组合查询
//条件建造器加载查询器
searchSourceBuilder.query(multiMatchQueryBuilder).size(100);
//加载查询的条件
searchRequest.source(searchSourceBuilder);
//调用客户端连接的search方法，实现查询
SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
SearchHit[] hits = search.getHits().getHits();
for (SearchHit hit : hits) {
//iter迭代
//获取每一条数据，将数据的内容转换为JAVA Bean
JobDetail jobDetail = JSON.parseObject(hit.getSourceAsString(), JobDetail.class);
//将docId设置为id
jobDetail.setId(Long.parseLong(hit.getId()));
lists.add(jobDetail);
}
return lists;
}
//endregion
//region 浅分页查询
@Override
public Map<String, Object> searchByPage(String keywords, int pageNum, int pageSize) throws IOException {
SearchRequest searchRequest = new SearchRequest();
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
MultiMatchQueryBuilder multiMatchQueryBuilder = QueryBuilders.multiMatchQuery(keywords, "jd", "title");
//建造器中指定查询器，指定from和size
searchSourceBuilder.query(multiMatchQueryBuilder)
.from(pageNum)
.size(pageSize);
searchRequest.source(searchSourceBuilder);
SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
//取的是第一层hits
SearchHits hits = search.getHits();
List<JobDetail> lists = new ArrayList<>();
for (SearchHit hit : hits) {
JobDetail jobDetail = JSON.parseObject(hit.getSourceAsString(), JobDetail.class);
jobDetail.setId(Long.parseLong(hit.getId()));
lists.add(jobDetail);
}
//构建返回值：返回值为Map集合
Map<String, Object> result = new HashMap<>();
//第一条数据：Key：total，Value：返回值的总条数
result.put("total", hits.getTotalHits().value);
//第二条数据：Key：content，Value：每条数据的List集合
result.put("content", lists);
return result;
}
//endregion
//region 深分页查询
@Override
public Map<String, Object> searchByScrollPage(String keywords, String scrollId, int pageSize) throws IOException {
//构建返回值
Map<String, Object> result = new HashMap<>();
List<JobDetail> jobList = new ArrayList<>();
try {
SearchResponse searchResponse = null;
//如果为null，这是第一次请求
if(scrollId == null) {
// 1. 创建搜索请求
SearchRequest searchRequest = new SearchRequest(JOB_IDX_NAME);
// 2. 构建查询条件
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
searchSourceBuilder.query(QueryBuilders.multiMatchQuery(keywords, "title", "jd"));
// 3. 设置分页大小
searchSourceBuilder.size(pageSize);
// 4. 设置查询条件、并设置滚动快照有效时间
searchRequest.source(searchSourceBuilder);
//指定数据在内存中放置时间
searchRequest.scroll(TimeValue.timeValueMinutes(1));
// 5. 发起请求
//提交查询器
searchResponse = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
}
//不是第一次，直接根据scrollid来查询
else {
//构建深分页查询器，传递scrollid
SearchScrollRequest searchScrollRequest = new SearchScrollRequest(scrollId);
searchScrollRequest.scroll(TimeValue.timeValueMinutes(1));
//调用scroll实现深分页
searchResponse = restHighLevelClient.scroll(searchScrollRequest, RequestOptions.DEFAULT);
}
// 6. 迭代响应结果
SearchHits hits = searchResponse.getHits();
for (SearchHit hit : hits) {
JobDetail jobDetail = JSONObject.parseObject(hit.getSourceAsString(), JobDetail.class);
jobDetail.setId(Long.parseLong(hit.getId()));
jobList.add(jobDetail);
}
//第一条数据：数据的呃逆荣
result.put("content", jobList);
//第二条数据；scrollid
result.put("scroll_id", searchResponse.getScrollId());
}
catch (IOException e) {
e.printStackTrace();
}
return result;
}
//endregion
//region 释放资源
@Override
public void close() throws IOException {
try {
restHighLevelClient.close();
} catch (IOException e) {
e.printStackTrace();
}
}
//endregion
//endregion
}
编写测试工具类
import com.aa.esClient.bean.JobDetail;
import com.aa.esClient.impl.JobFullTextServiceImpl;
import org.junit.After;
import org.junit.Before;
import org.junit.Test;
import java.io.IOException;
import java.util.List;
import java.util.Map;
public class JobFullTextServiceTest {
private JobFullTextServiceImpl jobFullTextService;
@Before
public void getInstance(){
//构建实现类的实例对象：构建连接
jobFullTextService = new JobFullTextServiceImpl();
}
//测试实现类add方法，写入数据
@Test
public void testAdd(){
//构建JaIndexRequestvaBean实例
JobDetail jobDetail = new JobDetail();
jobDetail.setId(1);
jobDetail.setArea("山西省-太原市");
jobDetail.setCmp("上兰村大学");
jobDetail.setEdu("本科及以上");
jobDetail.setExp("一年工作经验");
jobDetail.setTitle("大数据工程师");
jobDetail.setJob_type("全职");
jobDetail.setPv("1700次浏览");
jobDetail.setJd("会Hadoop就行");
jobDetail.setSalary("5-9千/月");
//调用add方法
jobFullTextService.add(jobDetail);
}
//测试实现类根据docID读取数据
@Test
public void testGetById() throws IOException {
//调用查询方法
JobDetail byId = jobFullTextService.findById(1);
System.out.println(byId);
}
//测试更新
@Test
public void testUpdate() throws IOException {
JobDetail jobDetail = new JobDetail();
jobDetail.setId(1);
jobDetail.setArea("山西省-太原市");
jobDetail.setCmp("上兰村大学");
jobDetail.setEdu("本科及以上");
jobDetail.setExp("一年工作经验");
jobDetail.setTitle("大数据工程师");
jobDetail.setJob_type("全职");
jobDetail.setPv("1700次浏览");
jobDetail.setJd("会Hadoop就行");
jobDetail.setSalary("20-40千/月");
//调用更新方法进行更新
jobFullTextService.update(jobDetail);
}
//测试删除
@Test
public void testDelete() throws IOException {
jobFullTextService.deleteById(1);
}
//测试关键词查询
@Test
public void testSearch() throws IOException {
List<JobDetail> jobDetails = jobFullTextService.searchByKeywords("销售");
for (JobDetail jobDetail : jobDetails) {
System.out.println(jobDetail);
}
}
//测试浅分页
@Test
public void testFromSize() throws IOException {
Map<String, Object> objectMap = jobFullTextService.searchByPage("销售", 0, 100);
System.out.println("总共："+objectMap.get("total"));
List<JobDetail> lists = (List<JobDetail>) objectMap.get("content");
for (JobDetail list : lists) {
System.out.println(list);
}
}
//第一次运行
@Test
public void searchByScrollPageTest1() throws IOException {
Map<String, Object> result = jobFullTextService.searchByScrollPage("销售", null, 10);
System.out.println("scrollId: " + result.get("scroll_id"));
List<JobDetail> content = (List<JobDetail>)result.get("content");
for (JobDetail jobDetail : content) {
System.out.println(jobDetail);
}
}
//第二次开始
@Test
public void searchByScrollPageTest2() throws IOException {
Map<String, Object> result = jobFullTextService.searchByScrollPage("销售", "DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAAA1FkFMSEhpb1ZLU0FPd3hfeUpZc0hEQ3cAAAAAAAAANxZBTEhIaW9WS1NBT3d4X3lKWXNIREN3AAAAAAAAADYWQUxISGlvVktTQU93eF95SllzSERDdwAAAAAAAAA8FlJfaWNiQzhvU3YydTJpMUV1OUpIZlEAAAAAAAAAPRZSX2ljYkM4b1N2MnUyaTFFdTlKSGZR", 10);
System.out.println("scrollId: " + result.get("scroll_id"));
List<JobDetail> content = (List<JobDetail>)result.get("content");
for (JobDetail jobDetail : content) {
System.out.println(jobDetail);
}
}
@After
public void close(){
try {
//释放ES的连接对象
jobFullTextService.close();
} catch (IOException e) {
e.printStackTrace();
}
}
}
插入数据
查询数据
JobDetail{
id=0, area='山西省-太原市', exp='一年工作经验', edu='本科及以上', salary='5-9千/月', job_type='全职', cmp='上兰村大学', pv='1700次浏览', title='大数据工程师', jd='会Hadoop就行'}
Process finished with exit code 0
更新数据
更新前：
更新后：
缺少Log4j的报错不用理会：
ERROR StatusLogger No Log4j 2 configuration file found. Using default configuration (logging only errors to the console), or user programmatically provided configurations. Set system property 'log4j2.debug' to show Log4j 2 internal initialization logging. See https://logging.apache.org/log4j/2.x/manual/configuration.html for instructions on how to configure Log4j 2
Process finished with exit code 0
删除数据
关键词查询
浅分页查询
数了一下，确实是100行。。。
深分页查询
第一次运行捕获：
scrollId: DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAABoWTl9iLWotOHZRZ21fcDk4a2JkVGc1Zw==
第二次及以后运行需要按第一次运行捕获的数据修改：
Map<String, Object> result = jobFullTextService.searchByScrollPage("销售", "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAABsWTl9iLWotOHZRZ21fcDk4a2JkVGc1Zw==", 10);
串码和内存地址有关，每次使用可能都不一样。。。貌似成功实现了深分页查询。
转载：https://blog.csdn.net/qq_41990268/article/details/117483479
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