Java招聘系统源码全解析：从架构设计到核心模块实现

Java招聘系统源码全面解析：从架构设计到核心模块实现

一、系统架构设计

1.1 整体架构概述

本招聘系统采用基于Spring Boot的微服务架构设计，结合MySQL数据库和Redis缓存，构建高可用、可扩展的招聘平台。系统分为以下几个主要模块：

• 用户服务（User Service）
• 职位服务（Job Service）
• 简历服务（Resume Service）
• 匹配服务（Matching Service）
• 通知服务（Notification Service）

1.2 技术栈选择

• 核心框架：Spring Boot 2.7.x
• 持久层：MyBatis-Plus + Spring Data JPA
• 缓存：Redis
• 消息队列：RabbitMQ
• 搜索：Elasticsearch
• 安全框架：Spring Security + JWT
• 前端：Vue.js + Element UI

二、核心模块实现详解

2.1 用户服务模块

2.1.1 数据库设计

sql

	CREATE TABLE `user` (
	`id` bigint NOT NULL AUTO_INCREMENT,
	`username` varchar(50) NOT NULL,
	`password` varchar(100) NOT NULL,
	`email` varchar(100) NOT NULL,
	`phone` varchar(20) DEFAULT NULL,
	`user_type` tinyint NOT NULL COMMENT '1-求职者 2-招聘方',
	`status` tinyint NOT NULL DEFAULT '1' COMMENT '0-禁用 1-正常',
	`create_time` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP,
	`update_time` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
	PRIMARY KEY (`id`),
	UNIQUE KEY `idx_username` (`username`),
	UNIQUE KEY `idx_email` (`email`)
	) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

2.1.2 核心代码实现

java

	@Service
	@RequiredArgsConstructor
	public class UserServiceImpl implements UserService {
	private final UserMapper userMapper;
	private final PasswordEncoder passwordEncoder;
	private final RedisTemplate redisTemplate;
	@Override
	@Transactional
	public User register(UserRegisterDTO registerDTO) {
	// 校验用户名和邮箱是否已存在
	if (userMapper.selectByUsername(registerDTO.getUsername()) != null) {
	throw new BusinessException("用户名已存在");
	}
	if (userMapper.selectByEmail(registerDTO.getEmail()) != null) {
	throw new BusinessException("邮箱已被注册");
	}
	// 创建用户
	User user = new User();
	BeanUtils.copyProperties(registerDTO, user);
	user.setPassword(passwordEncoder.encode(registerDTO.getPassword()));
	user.setCreateTime(LocalDateTime.now());
	userMapper.insert(user);
	return user;
	}
	@Override
	public String login(UserLoginDTO loginDTO) {
	User user = userMapper.selectByUsername(loginDTO.getUsername());
	if (user == null || !passwordEncoder.matches(loginDTO.getPassword(), user.getPassword())) {
	throw new BusinessException("用户名或密码错误");
	}
	// 生成JWT令牌
	Map claims = new HashMap<>();
	claims.put("userId", user.getId());
	claims.put("userType", user.getUserType());
	return JwtUtils.generateToken(claims);
	}
	}

2.2 职位服务模块

2.2.1 Elasticsearch集成

java

	@Configuration
	public class ElasticsearchConfig {
	@Bean
	public RestHighLevelClient client() {
	return new RestHighLevelClient(
	RestClient.builder(new HttpHost("elasticsearch", 9200, "http")));
	}
	}
	@Service
	@RequiredArgsConstructor
	public class JobSearchService {
	private final RestHighLevelClient client;
	private final ObjectMapper objectMapper;
	public SearchResult searchJobs(JobSearchDTO searchDTO) {
	try {
	SearchRequest searchRequest = new SearchRequest("job_index");
	SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
	// 构建布尔查询
	BoolQueryBuilder boolQuery = QueryBuilders.boolQuery();
	// 关键词查询
	if (StringUtils.isNotBlank(searchDTO.getKeyword())) {
	boolQuery.must(QueryBuilders.multiMatchQuery(searchDTO.getKeyword(),
	"title", "description", "requirements"));
	}
	// 城市过滤
	if (StringUtils.isNotBlank(searchDTO.getCity())) {
	boolQuery.filter(QueryBuilders.termQuery("city.keyword", searchDTO.getCity()));
	}
	// 薪资范围过滤
	if (searchDTO.getMinSalary() != null) {
	boolQuery.filter(QueryBuilders.rangeQuery("minSalary").gte(searchDTO.getMinSalary()));
	}
	if (searchDTO.getMaxSalary() != null) {
	boolQuery.filter(QueryBuilders.rangeQuery("maxSalary").lte(searchDTO.getMaxSalary()));
	}
	sourceBuilder.query(boolQuery);
	sourceBuilder.from((searchDTO.getPageNum() - 1) * searchDTO.getPageSize());
	sourceBuilder.size(searchDTO.getPageSize());
	// 排序
	if (StringUtils.isNotBlank(searchDTO.getSortField())) {
	SortOrder order = "desc".equalsIgnoreCase(searchDTO.getSortOrder())
	? SortOrder.DESC : SortOrder.ASC;
	sourceBuilder.sort(searchDTO.getSortField(), order);
	}
	searchRequest.source(sourceBuilder);
	SearchResponse response = client.search(searchRequest, RequestOptions.DEFAULT);
	return parseSearchResult(response);
	} catch (IOException e) {
	throw new RuntimeException("搜索失败", e);
	}
	}
	private SearchResult parseSearchResult(SearchResponse response) throws JsonProcessingException {
	SearchResult result = new SearchResult();
	// 解析总命中数
	result.setTotal(response.getHits().getTotalHits().value);
	// 解析职位列表
	List jobList = new ArrayList<>();
	for (SearchHit hit : response.getHits().getHits()) {
	Job job = objectMapper.readValue(hit.getSourceAsString(), Job.class);
	jobList.add(job);
	}
	result.setJobs(jobList);
	return result;
	}
	}

2.3 简历服务模块

2.3.1 PDF简历解析实现

java

	@Service
	@RequiredArgsConstructor
	public class ResumeParseService {
	private final Tika tika;
	private final OcrService ocrService;
	public ResumeDTO parseResume(MultipartFile file) throws IOException {
	// 判断文件类型
	String contentType = file.getContentType();
	if (contentType == null) {
	throw new BusinessException("不支持的文件类型");
	}
	ResumeDTO resume = new ResumeDTO();
	if ("application/pdf".equals(contentType)) {
	// PDF文件处理
	try (InputStream stream = file.getInputStream()) {
	// 使用Tika提取文本内容
	String text = tika.parseToString(stream);
	// 解析文本内容
	parseTextContent(text, resume);
	}
	} else if (contentType.startsWith("image/")) {
	// 图片文件处理（如扫描的简历）
	String text = ocrService.recognizeText(file.getBytes());
	parseTextContent(text, resume);
	} else {
	throw new BusinessException("不支持的文件类型");
	}
	return resume;
	}
	private void parseTextContent(String text, ResumeDTO resume) {
	// 使用正则表达式提取关键信息
	extractBasicInfo(text, resume);
	extractEducation(text, resume);
	extractWorkExperience(text, resume);
	extractSkills(text, resume);
	}
	private void extractBasicInfo(String text, ResumeDTO resume) {
	// 提取姓名
	Pattern namePattern = Pattern.compile("姓名[:：]\\s*([^\\n]+)");
	Matcher matcher = namePattern.matcher(text);
	if (matcher.find()) {
	resume.setName(matcher.group(1).trim());
	}
	// 提取电话
	Pattern phonePattern = Pattern.compile("电话[:：]\\s*(\\d{11})");
	matcher = phonePattern.matcher(text);
	if (matcher.find()) {
	resume.setPhone(matcher.group(1));
	}
	// 提取邮箱
	Pattern emailPattern = Pattern.compile("邮箱[:：]\\s*([\\w.-]+@[\\w.-]+\\.\\w+)");
	matcher = emailPattern.matcher(text);
	if (matcher.find()) {
	resume.setEmail(matcher.group(1));
	}
	}
	// 其他解析方法...
	}

2.4 匹配服务模块

2.4.1 职位与简历匹配算法

java

	@Service
	@RequiredArgsConstructor
	public class MatchingService {
	private final JobService jobService;
	private final ResumeService resumeService;
	@Async
	public CompletableFuture matchJobAndResume(Long jobId, Long resumeId) {
	Job job = jobService.getJobById(jobId);
	Resume resume = resumeService.getResumeById(resumeId);
	MatchResult result = new MatchResult();
	result.setJobId(jobId);
	result.setResumeId(resumeId);
	// 1. 基础信息匹配
	matchBasicInfo(job, resume, result);
	// 2. 技能匹配
	matchSkills(job, resume, result);
	// 3. 工作经历匹配
	matchWorkExperience(job, resume, result);
	// 4. 教育背景匹配
	matchEducation(job, resume, result);
	// 计算总分
	calculateTotalScore(result);
	return CompletableFuture.completedFuture(result);
	}
	private void matchBasicInfo(Job job, Resume resume, MatchResult result) {
	// 城市匹配
	if (job.getCity().equals(resume.getExpectedCity())) {
	result.setCityMatch(true);
	result.addScore("city", 10);
	}
	// 职位类型匹配
	if (job.getJobType().equals(resume.getJobType())) {
	result.setJobTypeMatch(true);
	result.addScore("jobType", 10);
	}
	// 薪资匹配
	if (resume.getExpectedSalary() != null) {
	if (resume.getExpectedSalary() >= job.getMinSalary()
	&& resume.getExpectedSalary() <= job.getMaxSalary()) {
	result.setSalaryMatch(true);
	result.addScore("salary", 15);
	}
	}
	}
	private void matchSkills(Job job, Resume resume, MatchResult result) {
	if (CollectionUtils.isEmpty(job.getRequiredSkills()) || CollectionUtils.isEmpty(resume.getSkills())) {
	return;
	}
	int matchCount = 0;
	for (String requiredSkill : job.getRequiredSkills()) {
	if (resume.getSkills().contains(requiredSkill)) {
	matchCount++;
	}
	}
	double matchRatio = (double) matchCount / job.getRequiredSkills().size();
	int skillScore = (int) (matchRatio * 30);
	result.setSkillMatchRatio(matchRatio);
	result.addScore("skill", skillScore);
	}
	private void calculateTotalScore(MatchResult result) {
	int totalScore = result.getDetails().values().stream()
	.mapToInt(Integer::intValue)
	.sum();
	result.setTotalScore(totalScore);
	// 评级
	if (totalScore >= 80) {
	result.setMatchLevel("高度匹配");
	} else if (totalScore >= 50) {
	result.setMatchLevel("一般匹配");
	} else {
	result.setMatchLevel("不匹配");
	}
	}
	}

三、系统优化与挑战

3.1 高并发处理方案

3.1.1 热点职位缓存策略

java

	@Service
	@RequiredArgsConstructor
	public class JobCacheService {
	private final RedisTemplate redisTemplate;
	private final JobService jobService;
	private static final String HOT_JOB_PREFIX = "hot_job:";
	private static final String HOT_JOB_RANK = "hot_job_rank";
	@Scheduled(fixedRate = 30 * 60 * 1000) // 每30分钟更新一次
	public void updateHotJobs() {
	// 1. 从数据库获取最近30天点击量最高的100个职位
	List hotJobs = jobService.getHotJobs(LocalDate.now().minusDays(30), 100);
	// 2. 更新缓存
	redisTemplate.delete(HOT_JOB_RANK);
	// 3. 使用ZSET存储热门职位排名
	for (int i = 0; i < hotJobs.size(); i++) {
	Job job = hotJobs.get(i);
	redisTemplate.opsForZSet().add(HOT_JOB_RANK, job.getId().toString(), 100 - i);
	}
	// 4. 缓存前20个热门职位详情
	for (int i = 0; i < 20 && i < hotJobs.size(); i++) {
	Job job = hotJobs.get(i);
	redisTemplate.opsForValue().set(HOT_JOB_PREFIX + job.getId(), job, 1, TimeUnit.HOURS);
	}
	}
	public List getHotJobs(int topN) {
	// 先从缓存获取排名
	Set jobIds = redisTemplate.opsForZSet().reverseRange(HOT_JOB_RANK, 0, topN - 1);
	if (CollectionUtils.isEmpty(jobIds)) {
	return Collections.emptyList();
	}
	// 获取职位详情
	List jobs = new ArrayList<>();
	for (String jobId : jobIds) {
	Job job = (Job) redisTemplate.opsForValue().get(HOT_JOB_PREFIX + jobId);
	if (job != null) {
	jobs.add(job);
	}
	}
	// 如果缓存不足，从数据库补充
	if (jobs.size() < topN) {
	List needFetchIds = jobIds.stream()
	.filter(id -> !redisTemplate.hasKey(HOT_JOB_PREFIX + id))
	.map(Long::valueOf)
	.collect(Collectors.toList());
	if (!needFetchIds.isEmpty()) {
	List dbJobs = jobService.getJobsByIds(needFetchIds);
	for (Job job : dbJobs) {
	redisTemplate.opsForValue().set(HOT_JOB_PREFIX + job.getId(), job, 1, TimeUnit.HOURS);
	jobs.add(job);
	}
	}
	}
	return jobs;
	}
	}

3.2 分布式事务处理

3.1.1 使用Seata实现分布式事务

java

	@Service
	@RequiredArgsConstructor
	public class JobApplyService {
	private final JobApplyMapper jobApplyMapper;
	private final ResumeService resumeService;
	private final NotificationService notificationService;
	@GlobalTransactional
	@Override
	public void applyJob(Long jobId, Long userId) {
	// 1. 创建申请记录
	JobApply apply = new JobApply();
	apply.setJobId(jobId);
	apply.setUserId(userId);
	apply.setStatus(ApplyStatus.PENDING.getCode());
	apply.setCreateTime(LocalDateTime.now());
	jobApplyMapper.insert(apply);
	// 2. 检查简历是否完整
	Resume resume = resumeService.getResumeByUserId(userId);
	if (resume == null || !resume.isComplete()) {
	throw new BusinessException("请先完善简历");
	}
	// 3. 发送通知
	notificationService.sendApplyNotification(jobId, userId);
	// 4. 更新职位申请人数（模拟异常测试分布式事务）
	// int i = 1 / 0; // 测试用，取消注释可触发回滚
	jobService.incrementApplyCount(jobId);
	}
	}

四、系统部署与运维

4.1 Docker部署配置

dockerfile

	# 基础镜像
	FROM openjdk:11-jre-slim
	# 设置工作目录
	WORKDIR /app
	# 复制jar包
	COPY target/job-service.jar /app/job-service.jar
	# 暴露端口
	EXPOSE 8080
	# 启动命令
	ENTRYPOINT ["java", "-jar", "job-service.jar"]

4.2 Kubernetes部署示例

yaml

	apiVersion: apps/v1
	kind: Deployment
	metadata:
	name: job-service
	spec:
	replicas: 3
	selector:
	matchLabels:
	app: job-service
	template:
	metadata:
	labels:
	app: job-service
	spec:
	containers:
	- name: job-service
	image: registry.example.com/job-service:1.0.0
	ports:
	- containerPort: 8080
	env:
	- name: SPRING_PROFILES_ACTIVE
	value: "prod"
	- name: SPRING_DATASOURCE_URL
	valueFrom:
	secretKeyRef:
	name: db-secret
	key: url
	resources:
	requests:
	cpu: "500m"
	memory: "1Gi"
	limits:
	cpu: "1000m"
	memory: "2Gi"
	---
	apiVersion: v1
	kind: Service
	metadata:
	name: job-service
	spec:
	selector:
	app: job-service
	ports:
	- protocol: TCP
	port: 80
	targetPort: 8080
	type: ClusterIP

五、总结与展望

Java招聘系统源码实现了从用户管理、职位发布、简历投递到智能匹配的完整招聘流程。系统采用微服务架构，具备良好的扩展性和可维护性。通过Elasticsearch实现高效搜索，利用Redis缓存热点数据，结合分布式事务保证数据一致性。

未来可扩展方向：

1. 引入AI技术实现更智能的简历筛选和职位推荐
2. 增加视频面试功能
3. 实现招聘数据分析看板
4. 集成区块链技术确保简历真实性