这套组合是当前Java生态中最流行的监控解决方案之一，特别适合云原生环境下的微服务应用监控。下面我将从技术实现到最佳实践进行全面解析。
一、技术栈组成与协作
1. 组件分工
| 组件 | 角色 | 关键能力 |
|---|---|---|
| Micrometer | 应用指标门面(Facade) | 统一指标采集API，对接多种监控系统 |
| Prometheus | 时序数据库+采集器 | 指标存储、查询、告警规则处理 |
| Grafana | 可视化平台 | 仪表盘展示、数据可视化分析 |
2. 数据流动
二、Micrometer 集成实践
1. Spring Boot 配置
Maven依赖：
<dependency><groupId>io.micrometer</groupId><artifactId>micrometer-registry-prometheus</artifactId>
</dependency>
<dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
application.yml配置：
management:endpoints:web:exposure:include: health,info,prometheusmetrics:export:prometheus:enabled: truetags:application: ${spring.application.name} # 统一添加应用标签
2. 自定义指标示例
业务指标采集：
@Service
public class OrderService {private final Counter orderCounter;private final Timer orderProcessingTimer;public OrderService(MeterRegistry registry) {// 创建计数器orderCounter = Counter.builder("orders.total").description("Total number of orders").tag("type", "online").register(registry);// 创建计时器orderProcessingTimer = Timer.builder("orders.processing.time").description("Order processing time").publishPercentiles(0.5, 0.95) // 50%和95%分位.register(registry);}public void processOrder(Order order) {// 方法1: 手动计时long start = System.currentTimeMillis();try {// 业务逻辑...orderCounter.increment();} finally {long duration = System.currentTimeMillis() - start;orderProcessingTimer.record(duration, TimeUnit.MILLISECONDS);}// 方法2: 使用Lambda自动计时orderProcessingTimer.record(() -> {// 业务逻辑...orderCounter.increment();});}
}
三、Prometheus 配置优化
1. 抓取配置示例
# prometheus.yml
global:scrape_interval: 15sevaluation_interval: 30sscrape_configs:- job_name: 'spring-apps'metrics_path: '/actuator/prometheus'scrape_interval: 10s # 对应用更频繁采集static_configs:- targets: ['app1:8080', 'app2:8080']relabel_configs:- source_labels: [__address__]target_label: instance- source_labels: [__meta_service_name]target_label: service
2. 关键优化参数
存储配置：
# 控制块存储行为
storage:tsdb:retention: 15d # 数据保留时间out_of_order_time_window: 1h # 允许乱序数据窗口# 限制内存使用
query:lookback-delta: 5mmax-concurrency: 20
四、Grafana 仪表盘设计
1. 核心监控仪表盘
JVM监控面板配置：
Panel 1: Heap Memory Usage
Query: sum(jvm_memory_used_bytes{area="heap"}) by (instance) / sum(jvm_memory_max_bytes{area="heap"}) by (instance)
Visualization: Time series with % unitPanel 2: GC Pause Time
Query: rate(jvm_gc_pause_seconds_sum[1m])
Visualization: HeatmapPanel 3: Thread States
Query: jvm_threads_states_threads{instance=~"$instance"}
Visualization: Stacked bar chart
2. 业务指标可视化
订单业务看板：
{"panels": [{"title": "Orders per Minute","targets": [{"expr": "rate(orders_total[1m])","legendFormat": "{{instance}}"}],"type": "graph","yaxes": [{"format": "ops"}]},{"title": "Processing Time (95%)","targets": [{"expr": "histogram_quantile(0.95, rate(orders_processing_time_seconds_bucket[1m]))","legendFormat": "P95"}],"type": "stat","unit": "s"}]
}
五、生产环境最佳实践
1. 指标命名规范
| 类型 | 前缀 | 示例 |
|---|---|---|
| 计数器 | _total | http_requests_total |
| 计量器 | _current | queue_size_current |
| 计时器 | _seconds | api_latency_seconds |
| 分布概要 | _summary | response_size_summary |
2. 标签使用原则
- 避免高基数标签：如用户ID、订单号等
- 统一标签命名：团队内保持一致（如
envvsenvironment） - 重要维度标记：
region、az、service_version等
3. 资源优化技巧
Micrometer配置：
@Bean
MeterRegistryCustomizer<MeterRegistry> metricsCommonTags() {return registry -> registry.config().meterFilter(MeterFilter.deny(id -> {// 过滤不需要的指标return id.getName().startsWith("jvm_classes");})).commonTags("region", System.getenv("AWS_REGION"));
}
Prometheus资源限制：
# 容器部署时设置资源限制
resources:limits:memory: 8Girequests:cpu: 2memory: 4Gi
六、高级功能实现
1. 自定义Collector
public class CustomMetricsCollector extends Collector {@Overridepublic List<MetricFamilySamples> collect() {List<MetricFamilySamples> samples = new ArrayList<>();// 添加自定义指标samples.add(new MetricFamilySamples("custom_metric",Type.GAUGE,"Custom metric description",Collections.singletonList(new MetricFamilySamples.Sample("custom_metric",List.of("label1"),List.of("value1"),getCurrentValue()))));return samples;}
}// 注册Collector
new CustomMetricsCollector().register();
2. 告警规则示例
groups:
- name: application-alertsrules:- alert: HighErrorRateexpr: rate(http_server_requests_errors_total[5m]) / rate(http_server_requests_total[5m]) > 0.05for: 10mlabels:severity: criticalannotations:summary: "High error rate on {{ $labels.instance }}"description: "Error rate is {{ $value }}"- alert: GCTooLongexpr: rate(jvm_gc_pause_seconds_sum[1h]) > 0.1labels:severity: warning
这套监控组合的优势在于：
- 云原生友好：完美契合Kubernetes环境
- 低侵入性：Micrometer作为抽象层减少代码耦合
- 高效存储：Prometheus的TSDB压缩比高
- 丰富可视化：Grafana社区提供大量现成仪表盘
建议实施路径：
- 先搭建基础监控（JVM/HTTP指标）
- 逐步添加业务指标
- 最后实现自定义告警和自动化处理
))
