# Spring Boot Actuator: Micrometer ve Prometheus ile Üretim İzleme

> Üretim ortamında izleme için kapsamlı Spring Boot Actuator rehberi. Micrometer yapılandırması, Prometheus metrikleri, özel endpointler ve uyarılar.

- Published: 2026-03-24
- Updated: 2026-05-04
- Author: SharpSkill
- Tags: spring boot actuator, micrometer, prometheus, monitoring, observability
- Reading time: 16 min

---

Spring Boot Actuator, sağlık denetimi, metrikler ve tanılama için üretime hazır endpointler sunarak Java uygulamalarının izlenmesini dönüştürür. Micrometer ve Prometheus ile birleştirildiğinde üretim ortamları için eksiksiz bir gözlemlenebilirlik çözümü oluşturur.

> **Anahtar Nokta**
>
> Actuator, ek bir yapılandırma gerektirmeden 50'den fazla JVM ve uygulama metriğini otomatik olarak ortaya çıkarır. Micrometer, bu metrikleri Prometheus, Grafana, Datadog veya başka herhangi bir izleme sistemine yayımlamak için bir cephe işlevi görür.

## Spring Boot 3 ile Temel Yapılandırma

### Gerekli Maven Bağımlılıkları

Actuator'ın Prometheus ile entegrasyonu üç ana bağımlılık gerektirir. Actuator starter'ı endpointleri etkinleştirir, Micrometer metrik soyutlamasını sağlar ve Prometheus registry, verileri scraping için biçimlendirir.

```xml
<!-- pom.xml -->
<!-- Actuator + Micrometer + Prometheus Configuration -->
<dependencies>
    <!-- Spring Boot Actuator - monitoring endpoints -->
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-actuator</artifactId>
    </dependency>

    <!-- Micrometer Registry Prometheus -->
    <!-- Exposes metrics in Prometheus format -->
    <dependency>
        <groupId>io.micrometer</groupId>
        <artifactId>micrometer-registry-prometheus</artifactId>
    </dependency>

    <!-- AOP for @Timed and @Counted metrics -->
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-aop</artifactId>
    </dependency>
</dependencies>
```

Bu bağımlılıklar, Prometheus'un periyodik olarak sorgulayabileceği `/actuator/prometheus` endpointini sunmaya yeterlidir.

### Actuator Endpointlerinin Yapılandırılması

Varsayılan olarak HTTP üzerinden yalnızca `health` ve `info` endpointleri açıktır. Açık bir yapılandırma, üretimde hangi endpointlerin erişilebilir kalacağını belirler.

```yaml
# application.yml
# Actuator configuration for production
management:
  endpoints:
    web:
      exposure:
        # Endpoints exposed over HTTP
        # health, info, prometheus are minimum for monitoring
        include: health,info,prometheus,metrics,env,loggers
      base-path: /actuator
    # Disable unused endpoints to reduce attack surface
    enabled-by-default: false
  endpoint:
    # Enable each required endpoint individually
    health:
      enabled: true
      show-details: when-authorized
      show-components: when-authorized
    info:
      enabled: true
    prometheus:
      enabled: true
    metrics:
      enabled: true
    env:
      enabled: true
      # Mask sensitive values
      show-values: when-authorized
    loggers:
      enabled: true
```

`show-details: when-authorized` seçeneği sağlık ayrıntılarını yalnızca uygun role sahip kimliği doğrulanmış kullanıcılara gösterir.

```java
// ActuatorSecurityConfig.java
// Securing Actuator endpoints
package com.example.monitoring.config;

import org.springframework.boot.actuate.autoconfigure.security.servlet.EndpointRequest;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.security.config.annotation.web.builders.HttpSecurity;
import org.springframework.security.web.SecurityFilterChain;

@Configuration
public class ActuatorSecurityConfig {

    @Bean
    SecurityFilterChain actuatorSecurityFilterChain(HttpSecurity http) throws Exception {
        return http
            .securityMatcher(EndpointRequest.toAnyEndpoint())
            .authorizeHttpRequests(auth -> auth
                // Health and info public for load balancers
                .requestMatchers(EndpointRequest.to("health", "info")).permitAll()
                // Prometheus accessible from internal network
                .requestMatchers(EndpointRequest.to("prometheus")).hasIpAddress("10.0.0.0/8")
                // Other endpoints restricted to admins
                .anyRequest().hasRole("ACTUATOR_ADMIN")
            )
            .httpBasic(basic -> {})
            .build();
    }
}
```

Bu yapılandırma, hassas endpointleri korurken temel endpointlere herkese açık erişim sağlar.

## Micrometer ile Özel Metrikler

### Uygulama Sayaçları ve Gauge'ları

Micrometer, farklı kullanım senaryolarına uygun çeşitli metrik türleri sunar. Counter'lar kümülatif olayları, gauge'lar anlık değerleri ve timer'lar bir işlemin süresini ölçer.

```java
// OrderMetricsService.java
// Custom business metrics service
package com.example.monitoring.metrics;

import io.micrometer.core.instrument.Counter;
import io.micrometer.core.instrument.Gauge;
import io.micrometer.core.instrument.MeterRegistry;
import io.micrometer.core.instrument.Timer;
import org.springframework.stereotype.Service;

import java.util.concurrent.atomic.AtomicInteger;
import java.util.function.Supplier;

@Service
public class OrderMetricsService {

    // Counter for orders created with status tag
    private final Counter ordersCreatedCounter;
    // Timer to measure processing duration
    private final Timer orderProcessingTimer;
    // Atomic value for pending orders gauge
    private final AtomicInteger pendingOrdersCount = new AtomicInteger(0);

    public OrderMetricsService(MeterRegistry registry) {
        // Counter with tags for filtering in Prometheus
        this.ordersCreatedCounter = Counter.builder("orders.created.total")
            .description("Total number of orders created")
            .tag("application", "order-service")
            .register(registry);

        // Timer with histogram for percentiles
        this.orderProcessingTimer = Timer.builder("orders.processing.duration")
            .description("Order processing duration")
            .publishPercentiles(0.5, 0.95, 0.99)
            .publishPercentileHistogram()
            .register(registry);

        // Gauge linked to atomic value
        // Updates automatically on each scrape
        Gauge.builder("orders.pending.count", pendingOrdersCount, AtomicInteger::get)
            .description("Number of orders pending processing")
            .register(registry);
    }

    public void recordOrderCreated() {
        ordersCreatedCounter.increment();
        pendingOrdersCount.incrementAndGet();
    }

    public void recordOrderProcessed(Runnable processingLogic) {
        // Automatically measures execution duration
        orderProcessingTimer.record(processingLogic);
        pendingOrdersCount.decrementAndGet();
    }

    public <T> T recordOrderProcessedWithResult(Supplier<T> processingLogic) {
        return orderProcessingTimer.record(processingLogic);
    }
}
```

Etiket kullanımı, Prometheus'ta hassas PromQL sorgularıyla metriklerin filtrelenmesini ve toplanmasını mümkün kılar.

### @Timed ve @Counted Anotasyonları

Kalıp kodu önlemek için Micrometer, metotları otomatik olarak araçsallaştıran AOP anotasyonları sunar.

```java
// PaymentService.java
// Automatic instrumentation with annotations
package com.example.monitoring.service;

import io.micrometer.core.annotation.Counted;
import io.micrometer.core.annotation.Timed;
import org.springframework.stereotype.Service;

@Service
public class PaymentService {

    // @Timed automatically creates a Timer
    // Measures each call and publishes count, sum, max
    @Timed(
        value = "payment.process.duration",
        description = "Payment processing duration",
        percentiles = {0.5, 0.95, 0.99},
        histogram = true
    )
    public PaymentResult processPayment(PaymentRequest request) {
        // Payment logic
        validatePayment(request);
        return executePayment(request);
    }

    // @Counted increments a counter on each call
    // Useful for discrete events
    @Counted(
        value = "payment.refunds.total",
        description = "Total number of refunds"
    )
    public void refundPayment(String transactionId) {
        // Refund logic
    }

    // Combining both annotations
    @Timed(value = "payment.validation.duration")
    @Counted(value = "payment.validation.total")
    private void validatePayment(PaymentRequest request) {
        // Payment validation
    }
}
```

```java
// TimedAspectConfig.java
// Required configuration to enable @Timed
package com.example.monitoring.config;

import io.micrometer.core.aop.CountedAspect;
import io.micrometer.core.aop.TimedAspect;
import io.micrometer.core.instrument.MeterRegistry;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class TimedAspectConfig {

    // Aspect required for @Timed to work
    @Bean
    TimedAspect timedAspect(MeterRegistry registry) {
        return new TimedAspect(registry);
    }

    // Aspect for @Counted
    @Bean
    CountedAspect countedAspect(MeterRegistry registry) {
        return new CountedAspect(registry);
    }
}
```

> **AOP Sınırlaması**
>
> @Timed ve @Counted anotasyonları yalnızca Spring bean'lerinde ve dış çağrılarda çalışır. Aynı sınıf içindeki dahili çağrılar AOP proxy'sini atlar ve araçsallaştırılmaz.

## Özel Sağlık Endpointleri

### İş Sağlık Göstergeleri

Health Indicator'lar dış bağımlılıkların ve kritik iş bileşenlerinin durumunu doğrular. Spring Boot, veritabanları, Redis ve diğer yaygın hizmetler için varsayılan göstergeler sunar.

```java
// PaymentGatewayHealthIndicator.java
// Health indicator for payment gateway
package com.example.monitoring.health;

import org.springframework.boot.actuate.health.Health;
import org.springframework.boot.actuate.health.HealthIndicator;
import org.springframework.stereotype.Component;
import org.springframework.web.client.RestClient;

import java.time.Duration;
import java.time.Instant;

@Component
public class PaymentGatewayHealthIndicator implements HealthIndicator {

    private final RestClient restClient;
    private final String gatewayHealthUrl;

    public PaymentGatewayHealthIndicator(RestClient.Builder restClientBuilder) {
        this.restClient = restClientBuilder.build();
        this.gatewayHealthUrl = "https://api.payment-gateway.com/health";
    }

    @Override
    public Health health() {
        Instant start = Instant.now();

        try {
            // Call gateway health endpoint
            var response = restClient.get()
                .uri(gatewayHealthUrl)
                .retrieve()
                .toBodilessEntity();

            Duration responseTime = Duration.between(start, Instant.now());

            if (response.getStatusCode().is2xxSuccessful()) {
                return Health.up()
                    .withDetail("responseTime", responseTime.toMillis() + "ms")
                    .withDetail("statusCode", response.getStatusCode().value())
                    .build();
            } else {
                return Health.down()
                    .withDetail("statusCode", response.getStatusCode().value())
                    .withDetail("reason", "Unexpected status code")
                    .build();
            }
        } catch (Exception e) {
            Duration responseTime = Duration.between(start, Instant.now());

            return Health.down()
                .withDetail("error", e.getClass().getSimpleName())
                .withDetail("message", e.getMessage())
                .withDetail("responseTime", responseTime.toMillis() + "ms")
                .build();
        }
    }
}
```

Bu gösterge `/actuator/health` altında `paymentGateway` adıyla otomatik olarak görünür.

### Kubernetes için Sağlık Grupları

Sağlık grupları, Kubernetes liveness ve readiness probe'ları için ayrı endpointler oluşturmayı sağlar.

```yaml
# application.yml
# Health groups configuration for Kubernetes
management:
  endpoint:
    health:
      group:
        # Liveness probe - is the application alive?
        liveness:
          include: livenessState
          show-details: always
        # Readiness probe - can the application receive traffic?
        readiness:
          include: readinessState,db,redis,paymentGateway
          show-details: always
        # Custom probe for critical dependencies
        critical:
          include: db,paymentGateway
          show-details: when-authorized
  health:
    # Enable Kubernetes states
    livenessstate:
      enabled: true
    readinessstate:
      enabled: true
```

```java
// KubernetesHealthConfig.java
// Programmatic health groups configuration
package com.example.monitoring.config;

import org.springframework.boot.actuate.availability.LivenessStateHealthIndicator;
import org.springframework.boot.actuate.availability.ReadinessStateHealthIndicator;
import org.springframework.boot.availability.ApplicationAvailability;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class KubernetesHealthConfig {

    @Bean
    LivenessStateHealthIndicator livenessStateHealthIndicator(
            ApplicationAvailability availability) {
        return new LivenessStateHealthIndicator(availability);
    }

    @Bean
    ReadinessStateHealthIndicator readinessStateHealthIndicator(
            ApplicationAvailability availability) {
        return new ReadinessStateHealthIndicator(availability);
    }
}
```

Kubernetes probe'ları daha sonra özel endpointleri işaret eder:

```yaml
# kubernetes-deployment.yml
# Kubernetes probes configuration
spec:
  containers:
    - name: order-service
      livenessProbe:
        httpGet:
          path: /actuator/health/liveness
          port: 8080
        initialDelaySeconds: 30
        periodSeconds: 10
        failureThreshold: 3
      readinessProbe:
        httpGet:
          path: /actuator/health/readiness
          port: 8080
        initialDelaySeconds: 10
        periodSeconds: 5
        failureThreshold: 3
```

## Prometheus ve Grafana Entegrasyonu

### Prometheus Scraping Yapılandırması

Prometheus, `/actuator/prometheus` endpointini periyodik olarak sorgulayarak metrikleri toplar. Yapılandırma, scrape edilecek hedefleri belirler.

```yaml
# prometheus.yml
# Prometheus configuration for Spring Boot
global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: 'spring-boot-apps'
    metrics_path: '/actuator/prometheus'
    scrape_interval: 10s
    static_configs:
      - targets:
          - 'order-service:8080'
          - 'payment-service:8080'
          - 'inventory-service:8080'
    # Relabeling to add metadata
    relabel_configs:
      - source_labels: [__address__]
        target_label: instance
        regex: '([^:]+):\d+'
        replacement: '${1}'

  # Kubernetes service discovery
  - job_name: 'kubernetes-pods'
    kubernetes_sd_configs:
      - role: pod
    relabel_configs:
      # Only scrape pods with annotation
      - 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: (.+)
```

### Varsayılan JVM Metrikleri

Micrometer ile birlikte Actuator, ayrıntılı JVM metriklerini otomatik olarak ortaya çıkarır. İzleme için en önemli olanları aşağıdadır.

```promql
# PromQL queries for JVM monitoring

# Heap memory usage
jvm_memory_used_bytes{area="heap"}

# Memory usage percentage
jvm_memory_used_bytes{area="heap"} / jvm_memory_max_bytes{area="heap"} * 100

# Active threads
jvm_threads_live_threads

# Garbage collection - time spent
rate(jvm_gc_pause_seconds_sum[5m])

# GC count per minute
rate(jvm_gc_pause_seconds_count[1m]) * 60

# CPU used by JVM
process_cpu_usage

# Active database connections
hikaricp_connections_active

# Connection pool utilization
hikaricp_connections_active / hikaricp_connections_max * 100
```

```java
// CustomJvmMetrics.java
// Additional JVM metrics
package com.example.monitoring.metrics;

import io.micrometer.core.instrument.Gauge;
import io.micrometer.core.instrument.MeterRegistry;
import io.micrometer.core.instrument.binder.MeterBinder;
import org.springframework.stereotype.Component;

import java.lang.management.ManagementFactory;
import java.lang.management.OperatingSystemMXBean;

@Component
public class CustomJvmMetrics implements MeterBinder {

    @Override
    public void bindTo(MeterRegistry registry) {
        OperatingSystemMXBean osBean = ManagementFactory.getOperatingSystemMXBean();

        // System load average
        Gauge.builder("system.load.average", osBean, OperatingSystemMXBean::getSystemLoadAverage)
            .description("System load average over 1 minute")
            .register(registry);

        // Available processors count
        Gauge.builder("system.cpu.count", osBean, OperatingSystemMXBean::getAvailableProcessors)
            .description("Number of available processors")
            .register(registry);

        // Application uptime
        Gauge.builder("application.uptime.seconds",
                ManagementFactory.getRuntimeMXBean(),
                bean -> bean.getUptime() / 1000.0)
            .description("Application uptime in seconds")
            .register(registry);
    }
}
```

### Kullanıma Hazır Grafana Dashboard'ları

Grafana, Spring Boot için önceden yapılandırılmış dashboard'lar sunar. ID 12900'lü dashboard, Actuator metriklerinin eksiksiz bir görünümünü sağlar.

```json
{
  "annotations": {
    "list": []
  },
  "panels": [
    {
      "title": "Request Rate",
      "type": "graph",
      "targets": [
        {
          "expr": "rate(http_server_requests_seconds_count{application=\"$application\"}[5m])",
          "legendFormat": "{{method}} {{uri}} - {{status}}"
        }
      ]
    },
    {
      "title": "Response Time P99",
      "type": "graph",
      "targets": [
        {
          "expr": "histogram_quantile(0.99, rate(http_server_requests_seconds_bucket{application=\"$application\"}[5m]))",
          "legendFormat": "{{method}} {{uri}}"
        }
      ]
    },
    {
      "title": "Error Rate",
      "type": "singlestat",
      "targets": [
        {
          "expr": "sum(rate(http_server_requests_seconds_count{application=\"$application\",status=~\"5..\"}[5m])) / sum(rate(http_server_requests_seconds_count{application=\"$application\"}[5m])) * 100"
        }
      ]
    }
  ]
}
```

> **Grafana'ya Aktarım**
>
> Dashboard içe aktarmak için: Grafana → Dashboards → Import → ID 12900 (Spring Boot Statistics) veya 4701 (JVM Micrometer). Bu dashboard'lar standart Actuator metrikleriyle doğrudan çalışır.

## Prometheus ile Uyarılar

### Temel Uyarı Kuralları

Prometheus uyarı kuralları, metrikler kritik eşikleri aştığında bildirimleri tetikler.

```yaml
# alerting-rules.yml
# Alert rules for Spring Boot applications
groups:
  - name: spring-boot-alerts
    rules:
      # Alert if application is down
      - alert: ApplicationDown
        expr: up{job="spring-boot-apps"} == 0
        for: 1m
        labels:
          severity: critical
        annotations:
          summary: "Application {{ $labels.instance }} is down"
          description: "{{ $labels.instance }} has been down for more than 1 minute"

      # Alert on HTTP error rate
      - alert: HighErrorRate
        expr: |
          sum(rate(http_server_requests_seconds_count{status=~"5.."}[5m])) by (application)
          /
          sum(rate(http_server_requests_seconds_count[5m])) by (application)
          > 0.05
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High error rate on {{ $labels.application }}"
          description: "Error rate is {{ $value | humanizePercentage }}"

      # Alert on P99 latency
      - alert: HighLatency
        expr: |
          histogram_quantile(0.99,
            rate(http_server_requests_seconds_bucket[5m])
          ) > 2
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High latency detected"
          description: "P99 latency is {{ $value | humanizeDuration }}"

      # Heap memory alert
      - alert: HighHeapUsage
        expr: |
          jvm_memory_used_bytes{area="heap"}
          / jvm_memory_max_bytes{area="heap"}
          > 0.85
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High heap memory usage on {{ $labels.instance }}"
          description: "Heap usage is at {{ $value | humanizePercentage }}"

      # Database connection pool exhausted alert
      - alert: DatabaseConnectionPoolExhausted
        expr: |
          hikaricp_connections_active
          / hikaricp_connections_max
          > 0.9
        for: 2m
        labels:
          severity: critical
        annotations:
          summary: "Database connection pool nearly exhausted"
          description: "{{ $value | humanizePercentage }} of connections in use"

      # Excessive GC alert
      - alert: HighGCPause
        expr: |
          rate(jvm_gc_pause_seconds_sum[5m])
          / rate(jvm_gc_pause_seconds_count[5m])
          > 0.5
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High GC pause time"
          description: "Average GC pause is {{ $value | humanizeDuration }}"
```

Bu uyarılar en yaygın üretim sorunlarını kapsar: erişilebilirlik, performans ve kaynaklar.

## HTTP ve Veritabanı Metrikleri

### HTTP İsteklerinin Otomatik Araçsallaştırılması

Spring Boot 3, gelen tüm HTTP isteklerini ayrıntılı metriklerle otomatik olarak araçsallaştırır.

```yaml
# application.yml
# HTTP metrics configuration
management:
  metrics:
    distribution:
      # Enable histograms for percentiles
      percentiles-histogram:
        http.server.requests: true
      percentiles:
        http.server.requests: 0.5, 0.75, 0.95, 0.99
      # Define SLA buckets
      slo:
        http.server.requests: 100ms, 500ms, 1s, 2s
    tags:
      # Global tags added to all metrics
      application: ${spring.application.name}
      environment: ${spring.profiles.active:default}
```

```java
// WebMvcMetricsConfig.java
// HTTP tags customization
package com.example.monitoring.config;

import io.micrometer.core.instrument.Tag;
import org.springframework.boot.actuate.metrics.web.servlet.WebMvcTagsContributor;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.web.servlet.HandlerMapping;

import jakarta.servlet.http.HttpServletRequest;
import jakarta.servlet.http.HttpServletResponse;
import java.util.Collections;

@Configuration
public class WebMvcMetricsConfig {

    @Bean
    WebMvcTagsContributor customTagsContributor() {
        return (request, response, handler, exception) -> {
            // Add custom tags to HTTP metrics
            String userId = request.getHeader("X-User-Id");
            String tenantId = request.getHeader("X-Tenant-Id");

            return java.util.List.of(
                Tag.of("user.type", userId != null ? "authenticated" : "anonymous"),
                Tag.of("tenant", tenantId != null ? tenantId : "default")
            );
        };
    }
}
```

### HikariCP ve SQL Sorgu Metrikleri

HikariCP bağlantı havuzunun metrikleri otomatik olarak yayımlanır. SQL sorguları için ek bir yapılandırma, sorgu sürelerinin izlenmesini etkinleştirir.

```yaml
# application.yml
# HikariCP configuration with metrics
spring:
  datasource:
    hikari:
      pool-name: OrderServicePool
      maximum-pool-size: 20
      minimum-idle: 5
      connection-timeout: 30000
      idle-timeout: 600000
      max-lifetime: 1800000
      # Enable detailed metrics
      register-mbeans: true
```

```java
// DataSourceMetricsConfig.java
// Additional metrics for SQL queries
package com.example.monitoring.config;

import io.micrometer.core.instrument.MeterRegistry;
import net.ttddyy.dsproxy.listener.logging.SLF4JLogLevel;
import net.ttddyy.dsproxy.support.ProxyDataSourceBuilder;
import org.springframework.beans.factory.annotation.Qualifier;
import org.springframework.boot.autoconfigure.jdbc.DataSourceProperties;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.context.annotation.Primary;

import javax.sql.DataSource;

@Configuration
public class DataSourceMetricsConfig {

    @Bean
    @Primary
    DataSource metricsDataSource(
            DataSourceProperties properties,
            MeterRegistry registry) {

        // Original DataSource
        DataSource originalDataSource = properties
            .initializeDataSourceBuilder()
            .build();

        // Proxy with metrics
        return ProxyDataSourceBuilder.create(originalDataSource)
            .name("order-service-db")
            .listener(new MicrometerQueryMetricsListener(registry))
            .logQueryBySlf4j(SLF4JLogLevel.DEBUG)
            .build();
    }
}
```

```java
// MicrometerQueryMetricsListener.java
// Listener for SQL query metrics
package com.example.monitoring.metrics;

import io.micrometer.core.instrument.MeterRegistry;
import io.micrometer.core.instrument.Timer;
import net.ttddyy.dsproxy.ExecutionInfo;
import net.ttddyy.dsproxy.QueryInfo;
import net.ttddyy.dsproxy.listener.QueryExecutionListener;

import java.util.List;
import java.util.concurrent.TimeUnit;

public class MicrometerQueryMetricsListener implements QueryExecutionListener {

    private final Timer queryTimer;

    public MicrometerQueryMetricsListener(MeterRegistry registry) {
        this.queryTimer = Timer.builder("sql.query.duration")
            .description("SQL query execution duration")
            .publishPercentiles(0.5, 0.95, 0.99)
            .register(registry);
    }

    @Override
    public void beforeQuery(ExecutionInfo execInfo, List<QueryInfo> queryInfoList) {
        // Before execution
    }

    @Override
    public void afterQuery(ExecutionInfo execInfo, List<QueryInfo> queryInfoList) {
        // Record duration for each query
        long elapsedTime = execInfo.getElapsedTime();
        queryTimer.record(elapsedTime, TimeUnit.MILLISECONDS);
    }
}
```

## Üretim için En İyi Uygulamalar

### Metrik Kardinalitesi

Aşırı kardinalite Prometheus'un performansını düşürür. Her benzersiz etiket kombinasyonu ayrı bir zaman serisi oluşturur.

```java
// AntiPatternHighCardinality.java
// ❌ AVOID - Explosive cardinality
package com.example.monitoring.antipattern;

@Service
public class AntiPatternHighCardinality {

    private final MeterRegistry registry;

    // ❌ BAD: userId creates one series per user
    public void trackUserAction(String userId, String action) {
        Counter.builder("user.actions")
            .tag("userId", userId)  // Millions of possible values!
            .tag("action", action)
            .register(registry)
            .increment();
    }
}
```

```java
// GoodPracticeCardinality.java
// ✅ Controlled cardinality
package com.example.monitoring.bestpractice;

@Service
public class GoodPracticeCardinality {

    private final MeterRegistry registry;

    // ✅ GOOD: User category instead of ID
    public void trackUserAction(User user, String action) {
        Counter.builder("user.actions")
            .tag("userType", user.getSubscriptionType())  // FREE, PREMIUM, ENTERPRISE
            .tag("action", action)
            .register(registry)
            .increment();
    }

    // ✅ GOOD: Grouping by range
    public void trackResponseTime(long responseTimeMs) {
        String bucket = categorizeResponseTime(responseTimeMs);
        Counter.builder("response.time.bucket")
            .tag("bucket", bucket)  // fast, normal, slow, very_slow
            .register(registry)
            .increment();
    }

    private String categorizeResponseTime(long ms) {
        if (ms < 100) return "fast";
        if (ms < 500) return "normal";
        if (ms < 2000) return "slow";
        return "very_slow";
    }
}
```

### Üretime Hazır Yapılandırma

```yaml
# application-production.yml
# Optimized configuration for production
management:
  endpoints:
    web:
      exposure:
        include: health,info,prometheus
  endpoint:
    health:
      show-details: when-authorized
      probes:
        enabled: true
  metrics:
    export:
      prometheus:
        enabled: true
        step: 30s
    distribution:
      percentiles-histogram:
        http.server.requests: true
      minimum-expected-value:
        http.server.requests: 1ms
      maximum-expected-value:
        http.server.requests: 30s
    tags:
      application: ${spring.application.name}
      environment: production
      version: ${app.version:unknown}
  server:
    # Separate port for management endpoints
    port: 9090

# Disable non-essential endpoints in production
  endpoint:
    env:
      enabled: false
    beans:
      enabled: false
    configprops:
      enabled: false
    mappings:
      enabled: false
```

## Sonuç

Micrometer ve Prometheus ile birleşen Spring Boot Actuator eksiksiz bir izleme çözümü sunar:

✅ **Minimum yapılandırma** - Spring Boot Starter ile üretime hazır endpointler

✅ **Otomatik JVM metrikleri** - bellek, thread'ler, GC, CPU; ek kod gerekmez

✅ **Özel metrikler** - @Timed/@Counted anotasyonlarıyla Counter, Gauge, Timer

✅ **Health Indicator'lar** - dış bağımlılıkların ve Kubernetes durumlarının kontrolü

✅ **Prometheus entegrasyonu** - scraping ve uyarı için standart format

✅ **Yerleşik güvenlik** - hassas endpointlerde erişim denetimi

✅ **Grafana dashboard'ları** - önceden yapılandırılmış dashboard'larla anında görselleştirme

✅ **Uyarı** - üretim anomalilerini saptayan PromQL kuralları

Bu gözlemlenebilirlik yığını, Spring Boot uygulamalarını üretim ortamında güvenle işletmek için zorunlu temeli oluşturur.

---

Source: SharpSkill (https://sharpskill.dev), tech interview preparation for your real stack.
HTML version of this page: https://sharpskill.dev/tr/blog/spring-boot/spring-boot-actuator-monitoring-micrometer-prometheus