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Prometheus+Grafana监控实战

news 2026/5/26 15:48:44

Prometheus+Grafana监控实战

引言

在微服务架构中，监控系统是保障系统稳定性和可靠性的关键基础设施。Prometheus是CNCF毕业的开源监控系统，以其强大的多维度数据模型、高效的查询语言PromQL和灵活的架构设计成为云原生监控的事实标准。Grafana则是最流行的可视化平台，可以连接多种数据源创建丰富的仪表板。本文将详细介绍Prometheus和Grafana的部署配置、在Spring Boot应用中的集成方法，以及构建完整监控体系的最佳实践。

一、Prometheus核心概念

1.1 数据模型

Prometheus采用时序数据库存储数据，每条时序数据由指标名称、标签集和时间戳组成。标签允许对指标进行多维度切分，PromQL可以基于这些标签进行灵活查询。

# 指标格式 metric_name{label1="value1", label2="value2"} value timestamp # 示例 http_requests_total{method="GET", status="200", handler="/api/users"} 1523 1704067200 process_cpu_seconds_total{instance="order-service:8080"} 4523.56 1704067200

1.2 四种指标类型

Counter是只增不减的计数器，用于统计请求数、错误数等；Gauge是可增可减的仪表盘，用于记录当前连接数、内存使用量等；Histogram用于记录观察值的分布，自动计算分位数；Summary与Histogram类似，但由客户端计算分位数。

// Spring Boot Micrometer中的指标定义 @Configuration public class MetricsConfig { @Bean public Counter orderCreatedCounter(MeterRegistry registry) { return Counter.builder("orders.created") .description("Number of orders created") .tag("type", "online") .register(registry); } @Bean public Gauge activeConnectionsGauge(MeterRegistry registry) { return Gauge.builder("connections.active", connectionPool, ConnectionPool::getActiveCount) .description("Number of active connections") .register(registry); } @Bean public Timer orderProcessingTimer(MeterRegistry registry) { return Timer.builder("order.processing.duration") .description("Time taken to process orders") .publishPercentiles(0.5, 0.95, 0.99) .register(registry); } @Bean public DistributionSummary orderAmountSummary(MeterRegistry registry) { return DistributionSummary.builder("order.amount") .description("Order amount distribution") .publishPercentiles(0.5, 0.95, 0.99) .register(registry); } }

二、Prometheus部署配置

2.1 Kubernetes部署

# prometheus-configmap.yaml apiVersion: v1 kind: ConfigMap metadata: name: prometheus-config namespace: monitoring data: prometheus.yml: | global: scrape_interval: 15s evaluation_interval: 15s external_labels: cluster: 'production' environment: 'kubernetes' alerting: alertmanagers: - static_configs: - targets: - alertmanager.monitoring.svc:9093 rule_files: - /etc/prometheus/rules/*.yml scrape_configs: # Prometheus自我监控 - job_name: 'prometheus' static_configs: - targets: ['localhost:9090'] # Kubernetes API Server - job_name: 'kubernetes-apiservers' kubernetes_sd_configs: - role: endpoints scheme: https tls_config: ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token relabel_configs: - source_labels: [__meta_kubernetes_namespace, __meta_kubernetes_service_name, __meta_kubernetes_endpoint_port_name] action: keep regex: default;kubernetes;https # Kubernetes Pods - job_name: 'kubernetes-pods' kubernetes_sd_configs: - role: pod relabel_configs: - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape] action: keep regex: true - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path] action: replace target_label: __metrics_path__ regex: (.+) - source_labels: [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port] action: replace regex: ([^:]+)(?::\d+)?;(\d+) replacement: $1:$2 target_label: __address__ - action: labelmap regex: __meta_kubernetes_pod_label_(.+)

2.2 Prometheus Operator

# Prometheus CRD apiVersion: monitoring.coreos.com/v1 kind: Prometheus metadata: name: prometheus namespace: monitoring spec: replicas: 2 retention: 15d retentionSize: 50GB serviceAccountName: prometheus serviceMonitorSelector: matchLabels: team: frontend ruleSelector: matchLabels: role: alert-rules alerting: alertmanagers: - namespace: monitoring name: alertmanager-main port: web resources: requests: memory: 2Gi cpu: 1000m limits: memory: 8Gi cpu: 4000m storage: volumeClaimTemplate: spec: storageClassName: ssd resources: requests: storage: 100Gi

三、Spring Boot应用集成

3.1 引入依赖

<dependencies> <!-- Actuator暴露健康指标 --> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-actuator</artifactId> </dependency> <!-- Micrometer Prometheus注册器 --> <dependency> <groupId>io.micrometer</groupId> <artifactId>micrometer-registry-prometheus</artifactId> </dependency> <!-- JVM和系统指标 --> <dependency> <groupId>io.micrometer</groupId> <artifactId>micrometer-core</artifactId> </dependency> </dependencies>

3.2 配置文件

management: endpoints: web: exposure: include: health,info,prometheus,metrics,logfile base-path: /actuator endpoint: health: show-details: always probes: enabled: true prometheus: enabled: true metrics: export: prometheus: enabled: true distribution: percentiles-histogram: http.server.requests: true percentiles: http.server.requests: 0.5, 0.95, 0.99 slo: http.server.requests: 50ms, 100ms, 200ms, 500ms, 1s tags: application: ${spring.application.name} environment: ${ENV:development}

3.3 自定义业务指标

@Service public class OrderMetricsService { private final Counter orderSuccessCounter; private final Counter orderFailedCounter; private final Timer orderProcessingTimer; private final Gauge activeOrdersGauge; private final AtomicInteger activeOrders; public OrderMetricsService(MeterRegistry registry) { this.orderSuccessCounter = Counter.builder("orders.success") .description("Successful order count") .tag("type", "online") .register(registry); this.orderFailedCounter = Counter.builder("orders.failed") .description("Failed order count") .tag("type", "online") .register(registry); this.orderProcessingTimer = Timer.builder("orders.processing.time") .description("Order processing duration") .publishPercentiles(0.5, 0.95, 0.99) .register(registry); this.activeOrders = new AtomicInteger(0); this.activeOrdersGauge = Gauge.builder("orders.active", activeOrders, AtomicInteger::get) .description("Active orders count") .register(registry); } public void recordOrderSuccess(Order order) { orderSuccessCounter.increment(); orderProcessingTimer.record(order.getProcessingTime(), TimeUnit.MILLISECONDS); } public void recordOrderFailed(Order order, Throwable error) { orderFailedCounter.increment(); Tags tags = Tags.of("error", error.getClass().getSimpleName()); Counter.builder("orders.failed.detailed") .tags(tags) .register(Registry.class.cast(orderSuccessCounter.getId())) .increment(); } public void incrementActiveOrders() { activeOrders.incrementAndGet(); } public void decrementActiveOrders() { activeOrders.decrementAndGet(); } }

3.4 ServiceMonitor配置

apiVersion: monitoring.coreos.com/v1 kind: ServiceMonitor metadata: name: order-service-monitor namespace: monitoring labels: team: frontend spec: selector: matchLabels: app: order-service namespaceSelector: matchNames: - production endpoints: - port: web path: /actuator/prometheus interval: 15s scrapeTimeout: 10s relabelings: - sourceLabels: [__meta_kubernetes_pod_name] targetLabel: pod - sourceLabels: [__meta_kubernetes_namespace] targetLabel: namespace jobLabel: order-service

四、AlertManager告警配置

4.1 告警规则

apiVersion: monitoring.coreos.com/v1 kind: PrometheusRule metadata: name: order-service-alerts namespace: production labels: role: alert-rules spec: groups: - name: order-service.rules rules: - alert: HighErrorRate expr: | sum(rate(orders_failed_total[5m])) / sum(rate(orders_success_total[5m])) > 0.05 for: 5m labels: severity: critical team: backend annotations: summary: "High error rate detected" description: "Order service error rate is above 5% for 5 minutes" - alert: HighLatency expr: | histogram_quantile(0.95, sum(rate(orders_processing_time_seconds_bucket[5m])) by (le)) > 2 for: 5m labels: severity: warning annotations: summary: "High order processing latency" description: "95th percentile latency is above 2 seconds" - alert: InstanceDown expr: up{job="order-service"} == 0 for: 1m labels: severity: critical annotations: summary: "Instance down" description: "Order service instance is down" - alert: HighMemoryUsage expr: | (jvm_memory_used_bytes{area="heap"} / jvm_memory_max_bytes{area="heap"}) > 0.9 for: 5m labels: severity: warning annotations: summary: "High JVM memory usage" description: "JVM heap usage is above 90%"

4.2 AlertManager配置

apiVersion: monitoring.coreos.com/v1 kind: Alertmanager metadata: name: alertmanager-main namespace: monitoring spec: replicas: 2 config: route: group_by: ['alertname', 'severity'] group_wait: 30s group_interval: 5m repeat_interval: 12h receiver: 'default-receiver' routes: - match: severity: critical receiver: 'critical-receiver' continue: true - match: team: backend receiver: 'backend-team' receivers: - name: 'default-receiver' webhook_configs: - url: 'http://notification-service:8080/webhook' send_resolved: true - name: 'critical-receiver' pagerduty_configs: - service_key: 'YOUR_PAGERDUTY_KEY' severity: critical - name: 'backend-team' email_configs: - to: 'backend-team@example.com' send_resolved: true

五、Grafana仪表板

5.1 数据源配置

apiVersion: v1 kind: ConfigMap metadata: name: grafana-datasources namespace: monitoring data: prometheus.yaml: | apiVersion: 1 datasources: - name: Prometheus type: prometheus access: proxy url: http://prometheus:9090 isDefault: true editable: true jsonData: timeInterval: 15s queryTimeout: 60s

5.2 JVM监控仪表板

{ "dashboard": { "title": "JVM Performance Dashboard", "panels": [ { "title": "JVM Memory Usage", "type": "timeseries", "gridPos": {"x": 0, "y": 0, "w": 12, "h": 8}, "targets": [ { "expr": "jvm_memory_used_bytes{area=\"heap\"}", "legendFormat": "{{id}} - {{application}}" } ], "fieldConfig": { "defaults": { "unit": "bytes", "custom": { "drawStyle": "line", "lineWidth": 2 } } } }, { "title": "GC Metrics", "type": "timeseries", "gridPos": {"x": 12, "y": 0, "w": 12, "h": 8}, "targets": [ { "expr": "rate(jvm_gc_pause_seconds_sum[5m])", "legendFormat": "{{action}} - {{cause}}" } ] }, { "title": "Thread Count", "type": "stat", "gridPos": {"x": 0, "y": 8, "w": 6, "h": 4}, "targets": [ { "expr": "jvm_threads_live_threads{application=\"order-service\"}" } ] }, { "title": "HTTP Request Rate", "type": "timeseries", "gridPos": {"x": 6, "y": 8, "w": 18, "h": 8}, "targets": [ { "expr": "sum(rate(http_server_requests_seconds_count[5m])) by (uri, status)", "legendFormat": "{{uri}} - {{status}}" } ] } ] } }

六、告警通知集成

6.1 钉钉告警

apiVersion: v1 kind: Secret metadata: name: dingtalk-webhook namespace: monitoring type: Opaque stringData: url: "https://oapi.dingtalk.com/robot/send?access_token=YOUR_TOKEN" --- apiVersion: monitoring.coreos.com/v1 kind: AlertmanagerConfig metadata: name: dingtalk-config namespace: monitoring spec: receivers: - name: 'dingtalk' dingtalkConfigs: - webhook: url: key: url name: dingtalk-webhook msgType: markdown atAll: false route: groupBy: ['alertname'] receiver: 'dingtalk'

6.2 企业微信告警

apiVersion: monitoring.coreos.com/v1 kind: AlertmanagerConfig metadata: name: wechat-config namespace: monitoring spec: receivers: - name: 'wechat' wechatConfigs: - apiURL: url: https://qyapi.weixin.qq.com/cgi-bin/ corpID: YOUR_CORP_ID agentID: "1000001" apiSecret: name: wechat-api-secret key: secret toParty: "1" toUser: "@all"

七、最佳实践

7.1 指标命名规范

指标名称应遵循以下规范：使用小写字母和下划线；包含功能域前缀；包含度量单位后缀；包含描述性的复数名词。

# 推荐命名 order_processing_duration_seconds user_login_total cache_hit_ratio # 不推荐命名 OrderProcessingTime /User/Login/Count CacheHitRate

7.2 标签使用建议

避免使用高基数标签，如用户ID、请求ID等；标签值应该有限且稳定；避免标签数量过多；使用role、instance、job等标准标签。

7.3 性能优化

# Prometheus远程写入配置 remote_write: - url: https://remote-write-endpoint/api/v1/write queue_config: capacity: 10000 max_shards: 30 min_shards: 1 max_samples_per_send: 5000 batch_send_deadline: 30s

总结

Prometheus和Grafana的组合提供了完整的云原生监控解决方案。Prometheus负责指标的采集、存储和告警，Grafana负责数据的可视化和分析。通过在Spring Boot应用中集成Micrometer，可以轻松暴露丰富的业务指标和JVM指标。结合AlertManager和各类通知渠道，可以实现及时的问题告警。建立完善的监控体系是保障系统稳定性的基础，也是SRE实践的重要组成部分。

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