SpringBoot中5种服务可用性保障技术分享
作者：风象南
服务可用性已成为系统设计的核心关注点,SpringBoot作为Java生态系统中流行的应用开发框架,提供了丰富的工具和库来保障服务的高可用性,本文将介绍SpringBoot中5种关键的服务可用性保障技术,需要的朋友可以参考下
1. 熔断器模式（Circuit Breaker）
基本原理
熔断器模式借鉴了电路熔断器的概念，当检测到系统中某个服务或组件频繁失败时，自动"断开"对该服务的调用，防止级联故障，同时为故障服务提供恢复时间。熔断器有三种状态：
- 关闭状态：正常执行操作，同时监控失败率
- 开启状态：拒绝访问，直接返回错误或执行降级逻辑
- 半开状态：尝试恢复，允许有限的请求通过以测试服务是否恢复
SpringBoot实现与集成
在SpringBoot中，我们可以使用Resilience4j实现熔断器模式，它是Hystrix的轻量级替代方案，专为Java 8和函数式编程设计。
首先添加依赖：
<dependency>
<groupId>io.github.resilience4j</groupId>
<artifactId>resilience4j-spring-boot2</artifactId>
<version>1.7.0</version>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-aop</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
然后在application.yml中配置熔断器参数：
resilience4j:
circuitbreaker:
instances:
orderService:
registerHealthIndicator: true
slidingWindowSize: 10
minimumNumberOfCalls: 5
permittedNumberOfCallsInHalfOpenState: 3
automaticTransitionFromOpenToHalfOpenEnabled: true
waitDurationInOpenState: 5s
failureRateThreshold: 50
eventConsumerBufferSize: 10
使用熔断器的示例代码：
@Service
public class OrderService {
private final PaymentServiceClient paymentServiceClient;
public OrderService(PaymentServiceClient paymentServiceClient) {
this.paymentServiceClient = paymentServiceClient;
}
@CircuitBreaker(name = "orderService", fallbackMethod = "processOrderFallback")
public OrderResponse processOrder(OrderRequest orderRequest) {
// 正常的订单处理逻辑，包括调用支付服务
PaymentResponse paymentResponse = paymentServiceClient.processPayment(orderRequest.getPaymentDetails());
return new OrderResponse(orderRequest.getOrderId(), "PROCESSED", paymentResponse.getTransactionId());
}
// 降级方法，在熔断器触发时执行
public OrderResponse processOrderFallback(OrderRequest orderRequest, Exception e) {
log.error("Circuit breaker triggered for order: {}. Error: {}", orderRequest.getOrderId(), e.getMessage());
// 返回降级响应，可能是从本地缓存获取，或使用默认值
return new OrderResponse(orderRequest.getOrderId(), "PENDING", null);
}
}
最佳实践
- 适当的窗口大小：设置合理的
slidingWindowSize，太小可能导致熔断器过于敏感，太大则反应迟钝。 - 合理的阈值：根据业务需求设置
failureRateThreshold，一般建议在50%-60%之间。 - 监控熔断器状态：集成Spring Boot Actuator监控熔断器状态：
management:
endpoints:
web:
exposure:
include: health,circuitbreakers
health:
circuitbreakers:
enabled: true
- 细粒度熔断：为不同的服务依赖配置不同的熔断器实例，避免一个服务故障影响多个业务流程。
- 测试熔断行为：通过混沌测试验证熔断器在故障情况下的行为是否符合预期。
2. 限流技术（Rate Limiting）
基本原理
限流用于控制系统的请求处理速率，防止系统过载。常见的限流算法包括：
- 令牌桶：以固定速率向桶中添加令牌，请求需要消耗令牌才能被处理。
- 漏桶：请求以固定速率处理，超出部分排队或拒绝。
- 计数器：在固定时间窗口内限制请求数量。
SpringBoot实现与集成
在SpringBoot中，我们可以使用Bucket4j实现API限流，这是一个基于令牌桶算法的Java限流库。
添加依赖：
<dependency>
<groupId>com.github.vladimir-bukhtoyarov</groupId>
<artifactId>bucket4j-core</artifactId>
<version>4.10.0</version>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-cache</artifactId>
</dependency>
<dependency>
<groupId>com.github.ben-manes.caffeine</groupId>
<artifactId>caffeine</artifactId>
</dependency>
配置缓存和限流：
@Configuration
public class RateLimitingConfig {
@Bean
public CacheManager cacheManager() {
CaffeineCacheManager cacheManager = new CaffeineCacheManager("rateLimit");
cacheManager.setCaffeine(Caffeine.newBuilder()
.expireAfterWrite(1, TimeUnit.HOURS)
.maximumSize(1000));
return cacheManager;
}
@Bean
public Bucket4jCacheConfiguration bucket4jCacheConfiguration() {
return new Bucket4jCacheConfiguration(cacheManager(), "rateLimit");
}
}
实现限流拦截器：
@Component
public class RateLimitingInterceptor implements HandlerInterceptor {
private final Cache<String, Bucket> cache;
public RateLimitingInterceptor() {
this.cache = Caffeine.newBuilder()
.expireAfterWrite(1, TimeUnit.HOURS)
.maximumSize(1000)
.build();
}
@Override
public boolean preHandle(HttpServletRequest request, HttpServletResponse response, Object handler) throws Exception {
String apiKey = request.getHeader("X-API-KEY");
if (apiKey == null || apiKey.isEmpty()) {
response.sendError(HttpStatus.BAD_REQUEST.value(), "Missing API key");
return false;
}
Bucket bucket = cache.get(apiKey, key -> createNewBucket());
ConsumptionProbe probe = bucket.tryConsumeAndReturnRemaining(1);
if (probe.isConsumed()) {
response.addHeader("X-Rate-Limit-Remaining", String.valueOf(probe.getRemainingTokens()));
return true;
} else {
long waitForRefill = probe.getNanosToWaitForRefill() / 1_000_000_000;
response.addHeader("X-Rate-Limit-Retry-After-Seconds", String.valueOf(waitForRefill));
response.sendError(HttpStatus.TOO_MANY_REQUESTS.value(), "Rate limit exceeded");
return false;
}
}
private Bucket createNewBucket() {
BucketConfiguration config = Bucket4j.configurationBuilder()
.addLimit(Bandwidth.classic(100, Refill.intervally(100, Duration.ofMinutes(1))))
.addLimit(Bandwidth.classic(1000, Refill.intervally(1000, Duration.ofHours(1))))
.build();
return Bucket4j.builder().withConfiguration(config).build();
}
}
@Configuration
public class WebMvcConfig implements WebMvcConfigurer {
@Autowired
private RateLimitingInterceptor rateLimitingInterceptor;
@Override
public void addInterceptors(InterceptorRegistry registry) {
registry.addInterceptor(rateLimitingInterceptor)
.addPathPatterns("/api/**");
}
}
在Spring Cloud Gateway中实现限流：
spring:
cloud:
gateway:
routes:
- id: order-service
uri: lb://order-service
predicates:
- Path=/orders/**
filters:
- name: RequestRateLimiter
args:
redis-rate-limiter.replenishRate: 10
redis-rate-limiter.burstCapacity: 20
redis-rate-limiter.requestedTokens: 1
key-resolver: "#{@userKeyResolver}"
@Configuration
public class GatewayConfig {
@Bean
public KeyResolver userKeyResolver() {
return exchange -> {
String userId = exchange.getRequest().getHeaders().getFirst("User-Id");
if (userId == null) {
userId = "anonymous";
}
return Mono.just(userId);
};
}
}
最佳实践
- 分级限流：基于不同用户类型或API重要性设置不同的限流阈值。
- 应用多级限流：例如，同时应用用户级、IP级和全局级限流。
- 限流响应：在限流触发时返回合适的HTTP状态码（通常是429）和明确的错误信息，包括重试建议。
- 监控限流指标：收集限流指标，用于分析和调整限流策略。
- 优雅降级：当达到限流阈值时，考虑提供降级服务而非完全拒绝。
3. 服务降级与容错处理
基本原理
服务降级是一种在系统高负载或部分服务不可用时，通过提供有限但可接受的服务来维持系统整体可用性的策略。容错处理则是指系统能够检测并处理错误，同时继续正常运行的能力。
SpringBoot实现与集成
在SpringBoot中，服务降级可以通过多种方式实现，包括与熔断器结合、使用异步回退，以及实现超时控制。
使用Resilience4j的Fallback实现服务降级：
@Service
public class ProductService {
private final ProductRepository productRepository;
private final ProductCacheService productCacheService;
@Autowired
public ProductService(ProductRepository productRepository, ProductCacheService productCacheService) {
this.productRepository = productRepository;
this.productCacheService = productCacheService;
}
@CircuitBreaker(name = "productService", fallbackMethod = "getProductDetailsFallback")
@Bulkhead(name = "productService", fallbackMethod = "getProductDetailsFallback")
@TimeLimiter(name = "productService", fallbackMethod = "getProductDetailsFallback")
public CompletableFuture<ProductDetails> getProductDetails(String productId) {
return CompletableFuture.supplyAsync(() -> {
// 正常的产品详情获取逻辑
Product product = productRepository.findById(productId)
.orElseThrow(() -> new ProductNotFoundException(productId));
// 获取实时库存和价格信息
InventoryInfo inventory = inventoryService.getInventory(productId);
PricingInfo pricing = pricingService.getCurrentPrice(productId);
return new ProductDetails(product, inventory, pricing);
});
}
// 降级方法，提供基本产品信息和缓存的库存和价格
public CompletableFuture<ProductDetails> getProductDetailsFallback(String productId, Exception e) {
log.warn("Fallback for product {}. Reason: {}", productId, e.getMessage());
return CompletableFuture.supplyAsync(() -> {
// 从缓存获取基本产品信息
Product product = productCacheService.getProductFromCache(productId)
.orElse(new Product(productId, "Unknown Product", "No details available"));
// 使用默认的库存和价格信息
InventoryInfo inventory = new InventoryInfo(productId, 0, false);
PricingInfo pricing = new PricingInfo(productId, 0.0, false);
return new ProductDetails(product, inventory, pricing, true);
});
}
}
配置超时和服务隔离：
resilience4j:
timelimiter:
instances:
productService:
timeoutDuration: 2s
cancelRunningFuture: true
bulkhead:
instances:
productService:
maxConcurrentCalls: 20
maxWaitDuration: 500ms
实现优雅降级策略的过滤器：
@Component
public class GracefulDegradationFilter extends OncePerRequestFilter {
private final HealthCheckService healthCheckService;
@Autowired
public GracefulDegradationFilter(HealthCheckService healthCheckService) {
this.healthCheckService = healthCheckService;
}
@Override
protected void doFilterInternal(HttpServletRequest request, HttpServletResponse response,
FilterChain filterChain) throws ServletException, IOException {
String path = request.getRequestURI();
// 检查系统健康状态
SystemHealth health = healthCheckService.getCurrentHealth();
if (health.isHighLoad() && isNonCriticalPath(path)) {
// 在高负载下降级非关键路径请求
sendDegradedResponse(response, "Service temporarily operating at reduced capacity");
return;
} else if (health.isInMaintenance() && !isAdminPath(path)) {
// 在维护模式下只允许管理请求
sendMaintenanceResponse(response);
return;
} else if (health.hasFailedDependencies() && dependsOnFailedServices(path, health.getFailedServices())) {
// 如果请求依赖的服务不可用，返回降级响应
sendDependencyFailureResponse(response, health.getFailedServices());
return;
}
// 正常处理请求
filterChain.doFilter(request, response);
}
private boolean isNonCriticalPath(String path) {
// 判断请求是否是非关键路径
return path.startsWith("/api/recommendations") ||
path.startsWith("/api/analytics") ||
path.startsWith("/api/marketing");
}
private boolean isAdminPath(String path) {
return path.startsWith("/admin") || path.startsWith("/management");
}
private boolean dependsOnFailedServices(String path, List<String> failedServices) {
// 检查请求是否依赖失败的服务
Map<String, List<String>> serviceDependencies = new HashMap<>();
serviceDependencies.put("/api/orders", Arrays.asList("payment-service", "inventory-service"));
serviceDependencies.put("/api/payments", Arrays.asList("payment-service"));
// ... 其他路径与服务的依赖关系
String matchingPath = findMatchingPath(path, serviceDependencies.keySet());
if (matchingPath != null) {
List<String> dependencies = serviceDependencies.get(matchingPath);
return dependencies.stream().anyMatch(failedServices::contains);
}
return false;
}
private String findMatchingPath(String requestPath, Set<String> configuredPaths) {
// 查找请求路径匹配的配置路径
return configuredPaths.stream()
.filter(requestPath::startsWith)
.findFirst()
.orElse(null);
}
private void sendDegradedResponse(HttpServletResponse response, String message) throws IOException {
response.setStatus(HttpStatus.SERVICE_UNAVAILABLE.value());
response.setContentType(MediaType.APPLICATION_JSON_VALUE);
Map<String, Object> responseBody = new HashMap<>();
responseBody.put("status", "degraded");
responseBody.put("message", message);
responseBody.put("retry_after", 30); // 建议30秒后重试
response.getWriter().write(new ObjectMapper().writeValueAsString(responseBody));
}
// 其他响应处理方法...
}
最佳实践
- 分级降级策略：针对不同的故障场景和服务重要性制定分级降级策略。
- 静态降级：预先准备好静态资源或缓存数据，在服务不可用时使用。
- 功能降级：暂时关闭非核心功能，保证核心业务正常。
- 特定用户群体降级：在高负载情况下，优先保证VIP用户的体验。
- 服务隔离：使用Bulkhead模式隔离不同服务的资源，防止一个服务的问题影响其他服务。
- 超时控制：设置合理的超时时间，防止长时间等待影响用户体验。
4. 重试机制（Retry）
基本原理
重试机制用于处理暂时性故障，通过自动重新尝试失败的操作来提高系统的弹性。对于网络抖动、数据库临时不可用等场景尤其有效。
SpringBoot实现与集成
SpringBoot中可以使用Spring Retry库实现重试功能。
添加依赖：
<dependency>
<groupId>org.springframework.retry</groupId>
<artifactId>spring-retry</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-aop</artifactId>
</dependency>
启用重试功能：
@SpringBootApplication
@EnableRetry
public class MyApplication {
public static void main(String[] args) {
SpringApplication.run(MyApplication.class, args);
}
}
使用声明式重试：
@Service
public class RemoteServiceClient {
private final RestTemplate restTemplate;
@Autowired
public RemoteServiceClient(RestTemplate restTemplate) {
this.restTemplate = restTemplate;
}
@Retryable(
value = {ResourceAccessException.class, HttpServerErrorException.class},
maxAttempts = 3,
backoff = @Backoff(delay = 1000, multiplier = 2)
)
public ResponseEntity<OrderData> getOrderDetails(String orderId) {
log.info("Attempting to fetch order details for {}", orderId);
return restTemplate.getForEntity("/api/orders/" + orderId, OrderData.class);
}
@Recover
public ResponseEntity<OrderData> recoverGetOrderDetails(Exception e, String orderId) {
log.error("All retries failed for order {}. Last error: {}", orderId, e.getMessage());
// 返回缓存数据或默认响应
return ResponseEntity.ok(new OrderData(orderId, "UNKNOWN", new Date(), Collections.emptyList()));
}
}
使用编程式重试：
@Service
public class PaymentService {
private final RetryTemplate retryTemplate;
@Autowired
public PaymentService(RetryTemplate retryTemplate) {
this.retryTemplate = retryTemplate;
}
public PaymentResult processPayment(PaymentRequest paymentRequest) {
return retryTemplate.execute(context -> {
// 获取当前重试次数
int retryCount = context.getRetryCount();
log.info("Processing payment attempt {} for order {}",
retryCount + 1, paymentRequest.getOrderId());
try {
// 执行支付处理
return paymentGateway.submitPayment(paymentRequest);
} catch (PaymentGatewayException e) {
// 分析异常并决定是否重试
if (e.isRetryable()) {
log.warn("Retryable payment error: {}. Will retry.", e.getMessage());
throw e; // 抛出异常以触发重试
} else {
log.error("Non-retryable payment error: {}", e.getMessage());
throw new NonRetryableException("Payment failed with non-retryable error", e);
}
}
}, context -> {
// 恢复策略
log.error("All payment retries failed for order {}", paymentRequest.getOrderId());
// 返回失败结果并记录需要后续处理
return PaymentResult.failed(paymentRequest.getOrderId(), "Maximum retries exceeded");
});
}
}
@Configuration
public class RetryConfig {
@Bean
public RetryTemplate retryTemplate() {
RetryTemplate retryTemplate = new RetryTemplate();
// 设置重试策略
SimpleRetryPolicy retryPolicy = new SimpleRetryPolicy();
retryPolicy.setMaxAttempts(3);
// 设置退避策略
ExponentialBackOffPolicy backOffPolicy = new ExponentialBackOffPolicy();
backOffPolicy.setInitialInterval(1000); // 1秒
backOffPolicy.setMultiplier(2.0); // 每次失败后等待时间翻倍
backOffPolicy.setMaxInterval(10000); // 最长等待10秒
retryTemplate.setRetryPolicy(retryPolicy);
retryTemplate.setBackOffPolicy(backOffPolicy);
return retryTemplate;
}
}
结合Resilience4j的重试功能：
resilience4j.retry:
instances:
paymentService:
maxRetryAttempts: 3
waitDuration: 1s
enableExponentialBackoff: true
exponentialBackoffMultiplier: 2
retryExceptions:
- org.springframework.web.client.ResourceAccessException
- com.example.service.exception.TemporaryServiceException
@Service
public class PaymentServiceWithResilience4j {
private final PaymentGateway paymentGateway;
@Autowired
public PaymentServiceWithResilience4j(PaymentGateway paymentGateway) {
this.paymentGateway = paymentGateway;
}
@Retry(name = "paymentService", fallbackMethod = "processPaymentFallback")
public PaymentResult processPayment(PaymentRequest request) {
return paymentGateway.submitPayment(request);
}
public PaymentResult processPaymentFallback(PaymentRequest request, Exception e) {
log.error("Payment processing failed after retries for order: {}", request.getOrderId());
return PaymentResult.failed(request.getOrderId(), "Payment processing temporarily unavailable");
}
}
最佳实践
- 区分暂时性和永久性故障：只对暂时性故障进行重试，对永久性故障立即失败。
- 指数退避：使用指数退避策略，避免重试风暴。
- 合理的重试次数：设置适当的最大重试次数，通常3-5次。
- 重试后监控：记录重试次数和结果，帮助识别问题服务。
- 幂等操作：确保被重试的操作是幂等的，以避免重复处理导致的问题。
- 设置超时：每次重试都应该有合理的超时时间。
- 与熔断器结合：将重试机制与熔断器结合使用，当故障持续存在时快速失败。
5. 健康检查与监控（Health Checks and Monitoring）
基本原理
健康检查和监控是保障服务可用性的基础设施，用于实时了解系统状态，及早发现并解决问题。通过系统指标收集、健康状态检查和警报机制，可以提前预防或快速解决服务故障。
SpringBoot实现与集成
SpringBoot Actuator提供了丰富的监控和管理功能，可以轻松集成到应用中。
添加依赖：
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
<dependency>
<groupId>io.micrometer</groupId>
<artifactId>micrometer-registry-prometheus</artifactId>
</dependency>
配置Actuator端点：
management:
endpoints:
web:
exposure:
include: health,info,metrics,prometheus,loggers,env
endpoint:
health:
show-details: always
group:
readiness:
include: db,redis,rabbit,diskSpace
health:
circuitbreakers:
enabled: true
ratelimiters:
enabled: true
metrics:
export:
prometheus:
enabled: true
enable:
jvm: true
system: true
process: true
http: true
自定义健康检查器：
@Component
public class ExternalServiceHealthIndicator implements HealthIndicator {
private final RestTemplate restTemplate;
@Autowired
public ExternalServiceHealthIndicator(RestTemplate restTemplate) {
this.restTemplate = restTemplate;
}
@Override
public Health health() {
try {
// 检查外部服务健康状态
ResponseEntity<Map> response = restTemplate.getForEntity("https://api.external-service.com/health", Map.class);
if (response.getStatusCode().is2xxSuccessful()) {
return Health.up()
.withDetail("status", response.getBody().get("status"))
.withDetail("version", response.getBody().get("version"))
.build();
} else {
return Health.down()
.withDetail("statusCode", response.getStatusCodeValue())
.withDetail("reason", "Unexpected status code")
.build();
}
} catch (Exception e) {
return Health.down()
.withDetail("error", e.getMessage())
.build();
}
}
}
配置应用就绪探针和活性探针：
@Configuration
public class HealthCheckConfig {
@Bean
public HealthContributorRegistry healthContributorRegistry(
ApplicationAvailabilityBean availabilityBean) {
HealthContributorRegistry registry = new DefaultHealthContributorRegistry();
// 添加应用启动完成的就绪检查
registry.registerContributor("readiness", new ApplicationAvailabilityHealthIndicator(
availabilityBean, ApplicationAvailabilityBean.LivenessState.CORRECT));
// 添加应用正在运行的活性检查
registry.registerContributor("liveness", new ApplicationAvailabilityHealthIndicator(
availabilityBean, ApplicationAvailabilityBean.ReadinessState.ACCEPTING_TRAFFIC));
return registry;
}
}
自定义指标收集：
@Component
public class OrderMetrics {
private final MeterRegistry meterRegistry;
private final Counter orderCounter;
private final DistributionSummary orderAmountSummary;
private final Timer orderProcessingTimer;
public OrderMetrics(MeterRegistry meterRegistry) {
this.meterRegistry = meterRegistry;
this.orderCounter = Counter.builder("orders.created")
.description("Number of orders created")
.tag("application", "order-service")
.register(meterRegistry);
this.orderAmountSummary = DistributionSummary.builder("orders.amount")
.description("Order amount distribution")
.tag("application", "order-service")
.publishPercentiles(0.5, 0.95, 0.99)
.register(meterRegistry);
this.orderProcessingTimer = Timer.builder("orders.processing.time")
.description("Order processing time")
.tag("application", "order-service")
.publishPercentiles(0.5, 0.95, 0.99)
.register(meterRegistry);
}
public void recordOrderCreated(String orderType) {
orderCounter.increment();
meterRegistry.counter("orders.created.by.type", "type", orderType).increment();
}
public void recordOrderAmount(double amount) {
orderAmountSummary.record(amount);
}
public Timer.Sample startOrderProcessing() {
return Timer.start(meterRegistry);
}
public void endOrderProcessing(Timer.Sample sample) {
sample.stop(orderProcessingTimer);
}
}
@Service
public class OrderServiceWithMetrics {
private final OrderRepository orderRepository;
private final OrderMetrics orderMetrics;
@Autowired
public OrderServiceWithMetrics(OrderRepository orderRepository, OrderMetrics orderMetrics) {
this.orderRepository = orderRepository;
this.orderMetrics = orderMetrics;
}
public Order createOrder(OrderRequest request) {
Timer.Sample timer = orderMetrics.startOrderProcessing();
try {
Order order = new Order();
// 处理订单
order.setItems(request.getItems());
order.setTotalAmount(calculateTotalAmount(request.getItems()));
order.setType(request.getType());
Order savedOrder = orderRepository.save(order);
// 记录指标
orderMetrics.recordOrderCreated(order.getType());
orderMetrics.recordOrderAmount(order.getTotalAmount());
return savedOrder;
} finally {
orderMetrics.endOrderProcessing(timer);
}
}
private double calculateTotalAmount(List<OrderItem> items) {
// 计算总金额
return items.stream()
.mapToDouble(item -> item.getPrice() * item.getQuantity())
.sum();
}
}
集成Grafana和Prometheus监控：
# docker-compose.yml
version: '3.8'
services:
app:
image: my-spring-boot-app:latest
ports:
- "8080:8080"
prometheus:
image: prom/prometheus:latest
volumes:
- ./prometheus.yml:/etc/prometheus/prometheus.yml
ports:
- "9090:9090"
grafana:
image: grafana/grafana:latest
depends_on:
- prometheus
ports:
- "3000:3000"
volumes:
- grafana-storage:/var/lib/grafana
volumes:
grafana-storage:
# prometheus.yml
global:
scrape_interval: 15s
scrape_configs:
- job_name: 'spring-boot-app'
metrics_path: '/actuator/prometheus'
static_configs:
- targets: ['app:8080']
最佳实践
- 多层次健康检查：实现浅层和深层健康检查，前者快速响应，后者全面检查。
- 关键业务指标监控：监控关键业务指标，如订单数量、转化率等。
- 系统资源监控：监控CPU、内存、磁盘、网络等系统资源。
- 设置合理的警报阈值：基于业务重要性和系统特性设置警报阈值。
- 关联分析：将不同服务的指标关联起来，便于问题根因分析。
- 日志与指标结合：将日志和指标结合起来，提供更完整的系统视图。
- 预测性监控：使用趋势分析预测潜在问题，如磁盘空间预测用尽时间。
总结
本文介绍了SpringBoot中5种核心的服务可用性保障技术：熔断器模式、限流技术、服务降级与容错处理、重试机制以及健康检查与监控。这些技术不是孤立的，而是相互配合、协同工作，共同构建起应用的防御体系。
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