Spring-Boot-Interceptor-Usage
在构建企业级Web应用时,我们经常需要在请求处理的不同阶段执行一些通用逻辑,如权限验证、日志记录、性能监控等。
Spring MVC的拦截器(Interceptor)机制提供了一种优雅的方式来实现这些横切关注点,而不必在每个控制器中重复编写相同的代码。
本文将介绍SpringBoot中6种常见的拦截器使用场景及其实现方式。
拦截器基础
什么是拦截器?
拦截器是Spring MVC框架提供的一种机制,用于在控制器(Controller)处理请求前后执行特定的逻辑。
拦截器与过滤器的区别
- 归属不同 :过滤器(Filter)属于Servlet规范,拦截器属于Spring框架。
- 拦截范围 :过滤器能拦截所有请求,拦截器只能拦截Spring MVC的请求。
- 执行顺序 :请求首先经过过滤器,然后才会被拦截器处理。
拦截器的生命周期
方法拦截器通过实现HandlerInterceptor接口来定义,该接口包含三个核心方法:
- preHandle() :在控制器方法执行前调用,返回true表示继续执行,返回false表示中断请求。
- postHandle() :在控制器方法执行后、视图渲染前调用。
- afterCompletion() :在整个请求完成后调用,无论是否有异常发生。
场景一:用户认证拦截器
使用场景
用户认证拦截器主要用于:
- 验证用户是否已登录
- 检查用户是否有权限访问特定资源
- 实现无状态API的JWT token验证
实现代码
@Component
public class AuthenticationInterceptor implements HandlerInterceptor {
@Autowired
private JwtTokenProvider jwtTokenProvider;
@Autowired
private UserService userService;
@Override
public boolean preHandle(HttpServletRequest request, HttpServletResponse response, Object handler)
throws Exception {
// 跳过非控制器方法的处理
if (!(handler instanceof HandlerMethod)) {
returntrue;
}
HandlerMethod handlerMethod = (HandlerMethod) handler;
// 检查是否有@PermitAll注解,有则跳过认证
PermitAll permitAll = handlerMethod.getMethodAnnotation(PermitAll.class);
if (permitAll != null) {
returntrue;
}
// 从请求头中获取token
String token = request.getHeader("Authorization");
if (token == null || !token.startsWith("Bearer ")) {
response.setStatus(HttpServletResponse.SC_UNAUTHORIZED);
response.getWriter().write("{"error": "未授权,请先登录"}");
returnfalse;
}
token = token.substring(7); // 去掉"Bearer "前缀
try {
// 验证token
if (!jwtTokenProvider.validateToken(token)) {
response.setStatus(HttpServletResponse.SC_UNAUTHORIZED);
response.getWriter().write("{"error": "Token已失效,请重新登录"}");
returnfalse;
}
// 从token中获取用户信息并设置到请求属性中
String username = jwtTokenProvider.getUsernameFromToken(token);
User user = userService.findByUsername(username);
if (user == null) {
response.setStatus(HttpServletResponse.SC_UNAUTHORIZED);
response.getWriter().write("{"error": "用户不存在"}");
returnfalse;
}
// 检查方法是否有@RequireRole注解
RequireRole requireRole = handlerMethod.getMethodAnnotation(RequireRole.class);
if (requireRole != null) {
// 检查用户是否有所需角色
String[] roles = requireRole.value();
boolean hasRole = false;
for (String role : roles) {
if (user.hasRole(role)) {
hasRole = true;
break;
}
}
if (!hasRole) {
response.setStatus(HttpServletResponse.SC_FORBIDDEN);
response.getWriter().write("{"error": "权限不足"}");
returnfalse;
}
}
// 将用户信息放入请求属性
request.setAttribute("currentUser", user);
returntrue;
} catch (Exception e) {
response.setStatus(HttpServletResponse.SC_UNAUTHORIZED);
response.getWriter().write("{"error": "Token验证失败"}");
returnfalse;
}
}
}
配置注册
@Configuration
public class WebMvcConfig implements WebMvcConfigurer {
@Autowired
private AuthenticationInterceptor authenticationInterceptor;
@Override
public void addInterceptors(InterceptorRegistry registry) {
registry.addInterceptor(authenticationInterceptor)
.addPathPatterns("/api/**")
.excludePathPatterns("/api/auth/login", "/api/auth/register");
}
}
自定义注解
@Target(ElementType.METHOD)
@Retention(RetentionPolicy.RUNTIME)
public @interface PermitAll {
}
@Target(ElementType.METHOD)
@Retention(RetentionPolicy.RUNTIME)
public @interface RequireRole {
String[] value();
}
最佳实践
- 使用注解来标记需要认证或特定权限的接口
- 将拦截器中的业务逻辑抽取到专门的服务类中
- 为不同安全级别的API设计不同的路径前缀
- 添加详细的日志记录,便于问题排查
场景二:日志记录拦截器
使用场景
日志记录拦截器主要用于:
- 记录API请求和响应内容
- 跟踪用户行为
- 收集系统使用统计数据
- 辅助问题排查
实现代码
@Component
@Slf4j
public class LoggingInterceptor implements HandlerInterceptor {
@Override
public boolean preHandle(HttpServletRequest request, HttpServletResponse response, Object handler)
throws Exception {
// 记录请求开始时间
long startTime = System.currentTimeMillis();
request.setAttribute("startTime", startTime);
// 记录请求信息
String requestURI = request.getRequestURI();
String method = request.getMethod();
String remoteAddr = request.getRemoteAddr();
String userAgent = request.getHeader("User-Agent");
// 获取当前用户(如果已通过认证拦截器)
Object currentUser = request.getAttribute("currentUser");
String username = currentUser != null ? ((User) currentUser).getUsername() : "anonymous";
// 记录请求参数
Map<String, String[]> paramMap = request.getParameterMap();
StringBuilder params = new StringBuilder();
if (!paramMap.isEmpty()) {
for (Map.Entry<String, String[]> entry : paramMap.entrySet()) {
params.append(entry.getKey())
.append("=")
.append(String.join(",", entry.getValue()))
.append("&");
}
if (params.length() > 0) {
params.deleteCharAt(params.length() - 1);
}
}
// 记录请求体(仅POST/PUT/PATCH请求)
String requestBody = "";
if (HttpMethod.POST.matches(method) ||
HttpMethod.PUT.matches(method) ||
HttpMethod.PATCH.matches(method)) {
// 使用包装请求对象来多次读取请求体
ContentCachingRequestWrapper wrappedRequest =
new ContentCachingRequestWrapper(request);
// 为了触发内容缓存,我们需要获取一次输入流
if (wrappedRequest.getContentLength() > 0) {
wrappedRequest.getInputStream().read();
requestBody = new String(wrappedRequest.getContentAsByteArray(),
wrappedRequest.getCharacterEncoding());
}
}
log.info(
"REQUEST: {} {} from={} user={} userAgent={} params={} body={}",
method,
requestURI,
remoteAddr,
username,
userAgent,
params,
requestBody
);
returntrue;
}
@Override
public void afterCompletion(HttpServletRequest request, HttpServletResponse response,
Object handler, Exception ex) throws Exception {
// 计算请求处理时间
long startTime = (Long) request.getAttribute("startTime");
long endTime = System.currentTimeMillis();
long processingTime = endTime - startTime;
// 记录响应状态和处理时间
int status = response.getStatus();
String requestURI = request.getRequestURI();
String method = request.getMethod();
if (ex != null) {
log.error(
"RESPONSE: {} {} status={} time={}ms error={}",
method,
requestURI,
status,
processingTime,
ex.getMessage()
);
} else {
log.info(
"RESPONSE: {} {} status={} time={}ms",
method,
requestURI,
status,
processingTime
);
}
}
}
配置与使用
@Bean
public FilterRegistrationBean<ContentCachingFilter> contentCachingFilter() {
FilterRegistrationBean<ContentCachingFilter> registrationBean = new FilterRegistrationBean<>();
registrationBean.setFilter(new ContentCachingFilter());
registrationBean.addUrlPatterns("/api/*");
return registrationBean;
}
@Override
public void addInterceptors(InterceptorRegistry registry) {
registry.addInterceptor(loggingInterceptor)
.addPathPatterns("/**");
}
自定义内容缓存过滤器
public class ContentCachingFilter extends OncePerRequestFilter {
@Override
protected void doFilterInternal(HttpServletRequest request, HttpServletResponse response,
FilterChain filterChain) throws ServletException, IOException {
ContentCachingRequestWrapper wrappedRequest = new ContentCachingRequestWrapper(request);
ContentCachingResponseWrapper wrappedResponse = new ContentCachingResponseWrapper(response);
try {
filterChain.doFilter(wrappedRequest, wrappedResponse);
} finally {
wrappedResponse.copyBodyToResponse();
}
}
}
最佳实践
- 对敏感信息(如密码、信用卡号等)进行脱敏处理
- 设置合理的日志级别和轮转策略
- 针对大型请求/响应体,考虑只记录部分内容或摘要
- 使用MDC(Mapped Diagnostic Context)记录请求ID,便于跟踪完整请求链路
场景三:性能监控拦截器
使用场景
性能监控拦截器主要用于:
- 监控API响应时间
- 识别性能瓶颈
- 统计慢查询
- 提供性能指标用于系统优化
实现代码
@Component
@Slf4j
publicclass PerformanceMonitorInterceptor implements HandlerInterceptor {
// 慢请求阈值,单位毫秒
@Value("${app.performance.slow-request-threshold:500}")
privatelong slowRequestThreshold;
@Autowired
private MetricsService metricsService;
@Override
public boolean preHandle(HttpServletRequest request, HttpServletResponse response, Object handler)
throws Exception {
if (handler instanceof HandlerMethod) {
HandlerMethod handlerMethod = (HandlerMethod) handler;
String controllerName = handlerMethod.getBeanType().getSimpleName();
String methodName = handlerMethod.getMethod().getName();
request.setAttribute("controllerName", controllerName);
request.setAttribute("methodName", methodName);
request.setAttribute("startTime", System.currentTimeMillis());
}
returntrue;
}
@Override
public void afterCompletion(HttpServletRequest request, HttpServletResponse response,
Object handler, Exception ex) throws Exception {
Long startTime = (Long) request.getAttribute("startTime");
if (startTime != null) {
long processingTime = System.currentTimeMillis() - startTime;
String controllerName = (String) request.getAttribute("controllerName");
String methodName = (String) request.getAttribute("methodName");
String uri = request.getRequestURI();
// 记录性能数据
metricsService.recordApiPerformance(controllerName, methodName, uri, processingTime);
// 记录慢请求
if (processingTime > slowRequestThreshold) {
log.warn("Slow API detected: {} {}.{} - {}ms (threshold: {}ms)",
uri, controllerName, methodName, processingTime, slowRequestThreshold);
// 记录慢请求到专门的监控系统
metricsService.recordSlowRequest(controllerName, methodName, uri, processingTime);
}
}
}
}
指标服务实现
@Service
@Slf4j
publicclass MetricsServiceImpl implements MetricsService {
// 使用滑动窗口记录最近的性能数据
privatefinal ConcurrentMap<String, SlidingWindowMetric> apiMetrics = new ConcurrentHashMap<>();
// 慢请求记录队列
privatefinal Queue<SlowRequestRecord> slowRequests = new ConcurrentLinkedQueue<>();
// 保留最近1000条慢请求记录
privatestaticfinalint MAX_SLOW_REQUESTS = 1000;
@Override
public void recordApiPerformance(String controller, String method, String uri, long processingTime) {
String apiKey = controller + "." + method;
apiMetrics.computeIfAbsent(apiKey, k -> new SlidingWindowMetric())
.addSample(processingTime);
// 可以在这里添加Prometheus或其他监控系统的指标记录
}
@Override
public void recordSlowRequest(String controller, String method, String uri, long processingTime) {
SlowRequestRecord record = new SlowRequestRecord(
controller, method, uri, processingTime, LocalDateTime.now()
);
slowRequests.add(record);
// 如果队列超过最大容量,移除最早的记录
while (slowRequests.size() > MAX_SLOW_REQUESTS) {
slowRequests.poll();
}
}
@Override
public List<ApiPerformanceMetric> getApiPerformanceMetrics() {
List<ApiPerformanceMetric> metrics = new ArrayList<>();
for (Map.Entry<String, SlidingWindowMetric> entry : apiMetrics.entrySet()) {
String[] parts = entry.getKey().split("\.");
String controller = parts[0];
String method = parts.length > 1 ? parts[1] : "";
SlidingWindowMetric metric = entry.getValue();
metrics.add(new ApiPerformanceMetric(
controller,
method,
metric.getAvg(),
metric.getMin(),
metric.getMax(),
metric.getCount()
));
}
return metrics;
}
@Override
public List<SlowRequestRecord> getSlowRequests() {
returnnew ArrayList<>(slowRequests);
}
// 滑动窗口指标类
privatestaticclass SlidingWindowMetric {
privatefinal LongAdder count = new LongAdder();
privatefinal LongAdder sum = new LongAdder();
privatefinal AtomicLong min = new AtomicLong(Long.MAX_VALUE);
privatefinal AtomicLong max = new AtomicLong(0);
public void addSample(long value) {
count.increment();
sum.add(value);
// 更新最小值
while (true) {
long currentMin = min.get();
if (value >= currentMin || min.compareAndSet(currentMin, value)) {
break;
}
}
// 更新最大值
while (true) {
long currentMax = max.get();
if (value <= currentMax || max.compareAndSet(currentMax, value)) {
break;
}
}
}
public long getCount() {
return count.sum();
}
public double getAvg() {
long countValue = count.sum();
return countValue > 0 ? (double) sum.sum() / countValue : 0;
}
public long getMin() {
return min.get() == Long.MAX_VALUE ? 0 : min.get();
}
public long getMax() {
return max.get();
}
}
}
实体类定义
@Data
@AllArgsConstructor
publicclass ApiPerformanceMetric {
private String controllerName;
private String methodName;
privatedouble avgProcessingTime;
privatelong minProcessingTime;
privatelong maxProcessingTime;
privatelong requestCount;
}
@Data
@AllArgsConstructor
publicclass SlowRequestRecord {
private String controllerName;
private String methodName;
private String uri;
privatelong processingTime;
private LocalDateTime timestamp;
}
指标服务接口
public interface MetricsService {
void recordApiPerformance(String controller, String method, String uri, long processingTime);
void recordSlowRequest(String controller, String method, String uri, long processingTime);
List<ApiPerformanceMetric> getApiPerformanceMetrics();
List<SlowRequestRecord> getSlowRequests();
}
性能监控控制器
@RestController
@RequestMapping("/admin/metrics")
publicclass MetricsController {
@Autowired
private MetricsService metricsService;
@GetMapping("/api-performance")
public List<ApiPerformanceMetric> getApiPerformanceMetrics() {
return metricsService.getApiPerformanceMetrics();
}
@GetMapping("/slow-requests")
public List<SlowRequestRecord> getSlowRequests() {
return metricsService.getSlowRequests();
}
}
最佳实践
- 使用滑动窗口统计,避免内存无限增长
- 为不同API设置不同的性能阈值
- 将性能数据导出到专业监控系统(如Prometheus)
- 设置告警机制,及时发现性能问题
- 只对重要接口进行详细监控,避免过度监控带来的性能开销
场景四:接口限流拦截器
使用场景
接口限流拦截器主要用于:
- 防止接口被恶意频繁调用
- 保护系统资源,避免过载
- 实现API访问量控制
- 防止DoS攻击
实现代码
@Component
@Slf4j
public class RateLimitInterceptor implements HandlerInterceptor {
@Autowired
private RedisTemplate<String, Object> redisTemplate;
@Value("${app.rate-limit.enabled:true}")
privateboolean enabled;
@Override
public boolean preHandle(HttpServletRequest request, HttpServletResponse response, Object handler)
throws Exception {
if (!enabled) {
returntrue;
}
if (!(handler instanceof HandlerMethod)) {
returntrue;
}
HandlerMethod handlerMethod = (HandlerMethod) handler;
// 获取限流注解
RateLimit rateLimit = handlerMethod.getMethodAnnotation(RateLimit.class);
if (rateLimit == null) {
// 没有配置限流注解,不进行限流
returntrue;
}
// 获取限流类型
RateLimitType limitType = rateLimit.type();
// 根据限流类型获取限流键
String limitKey = getLimitKey(request, limitType);
// 获取限流配置
int limit = rateLimit.limit();
int period = rateLimit.period();
// 执行限流检查
boolean allowed = checkRateLimit(limitKey, limit, period);
if (!allowed) {
// 超过限流,返回429状态码
response.setStatus(HttpStatus.TOO_MANY_REQUESTS.value());
response.setContentType(MediaType.APPLICATION_JSON_VALUE);
response.getWriter().write("{"error":"Too many requests","message":"请求频率超过限制,请稍后再试"}");
returnfalse;
}
returntrue;
}
private String getLimitKey(HttpServletRequest request, RateLimitType limitType) {
String key = "rate_limit:";
switch (limitType) {
case IP:
key += "ip:" + getClientIp(request);
break;
case USER:
// 从认证信息获取用户ID
Object currentUser = request.getAttribute("currentUser");
String userId = currentUser != null ?
String.valueOf(((User) currentUser).getId()) : "anonymous";
key += "user:" + userId;
break;
case API:
key += "api:" + request.getRequestURI();
break;
case IP_API:
key += "ip_api:" + getClientIp(request) + ":" + request.getRequestURI();
break;
case USER_API:
Object user = request.getAttribute("currentUser");
String id = user != null ?
String.valueOf(((User) user).getId()) : "anonymous";
key += "user_api:" + id + ":" + request.getRequestURI();
break;
default:
key += "global";
}
return key;
}
private boolean checkRateLimit(String key, int limit, int period) {
// 使用Redis的原子操作进行限流检查
Long count = redisTemplate.execute(connection -> {
// 递增计数器
Long currentCount = connection.stringCommands().incr(key.getBytes());
// 如果是第一次递增,设置过期时间
if (currentCount != null && currentCount == 1) {
connection.keyCommands().expire(key.getBytes(), period);
}
return currentCount;
}, true);
return count != null && count <= limit;
}
private String getClientIp(HttpServletRequest request) {
String ipAddress = request.getHeader("X-Forwarded-For");
if (ipAddress == null || ipAddress.isEmpty() || "unknown".equalsIgnoreCase(ipAddress)) {
ipAddress = request.getHeader("Proxy-Client-IP");
}
if (ipAddress == null || ipAddress.isEmpty() || "unknown".equalsIgnoreCase(ipAddress)) {
ipAddress = request.getHeader("WL-Proxy-Client-IP");
}
if (ipAddress == null || ipAddress.isEmpty() || "unknown".equalsIgnoreCase(ipAddress)) {
ipAddress = request.getRemoteAddr();
if ("127.0.0.1".equals(ipAddress) || "0:0:0:0:0:0:0:1".equals(ipAddress)) {
// 根据网卡取本机配置的IP
try {
InetAddress inet = InetAddress.getLocalHost();
ipAddress = inet.getHostAddress();
} catch (UnknownHostException e) {
log.error("获取本机IP失败", e);
}
}
}
// 对于多个代理的情况,第一个IP为客户端真实IP
if (ipAddress != null && ipAddress.contains(",")) {
ipAddress = ipAddress.substring(0, ipAddress.indexOf(","));
}
return ipAddress;
}
}
限流注解
@Target(ElementType.METHOD)
@Retention(RetentionPolicy.RUNTIME)
public@interface RateLimit {
/**
* 限流类型
*/
RateLimitType type() default RateLimitType.IP;
/**
* 限制次数
*/
int limit() default 100;
/**
* 时间周期(秒)
*/
int period() default 60;
}
publicenum RateLimitType {
/**
* 按IP地址限流
*/
IP,
/**
* 按用户限流
*/
USER,
/**
* 按接口限流
*/
API,
/**
* 按IP和接口组合限流
*/
IP_API,
/**
* 按用户和接口组合限流
*/
USER_API,
/**
* 全局限流
*/
GLOBAL
}
使用示例
@RestController
@RequestMapping("/api/products")
public class ProductController {
@Autowired
private ProductService productService;
@GetMapping
@RateLimit(type = RateLimitType.IP, limit = 100, period = 60)
public List<Product> getProducts() {
return productService.findAll();
}
@GetMapping("/{id}")
@RateLimit(type = RateLimitType.IP, limit = 200, period = 60)
public Product getProduct(@PathVariable Long id) {
return productService.findById(id)
.orElseThrow(() -> new ResourceNotFoundException("Product not found"));
}
@PostMapping
@RequireRole("ADMIN")
@RateLimit(type = RateLimitType.USER, limit = 10, period = 60)
public Product createProduct(@RequestBody @Valid ProductRequest productRequest) {
return productService.save(productRequest);
}
}
最佳实践
- 根据接口重要性和资源消耗设置不同的限流规则
- 使用分布式限流解决方案,如Redis+Lua脚本
- 为特定用户群体设置不同的限流策略
- 在限流响应中提供合理的重试建议
- 监控限流情况,及时调整限流阈值
场景五:请求参数验证拦截器
使用场景
请求参数验证拦截器主要用于:
- 统一处理参数验证逻辑
- 提供友好的错误信息
- 防止非法参数导致的安全问题
- 减少控制器中的重复代码