jvm接入prometheus监控
- 创建以下两个配置类:
package com.haoze.doctor.config;
import com.alibaba.druid.pool.DruidDataSource;
import io.micrometer.core.instrument.Gauge;
import io.micrometer.core.instrument.MeterRegistry;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.stereotype.Component;
import java.util.List;
import java.util.function.ToDoubleFunction;
@Component
public class DruidCollector {
@Value("${spring.application.name}")
private String applicationName;
private static final String LABEL_NAME = "druid_pool";
private final MeterRegistry registry;
DruidCollector(MeterRegistry registry) {
this.registry = registry;
}
void register(List<DruidDataSource> dataSources) {
dataSources.forEach((druidDataSource) -> {
// basic configurations
createGauge(druidDataSource, "druid_initial_size", "Initial size", (datasource) -> (double) druidDataSource.getInitialSize());
createGauge(druidDataSource, "druid_min_idle", "Min idle", datasource -> (double) druidDataSource.getMinIdle());
createGauge(druidDataSource, "druid_max_active", "Max active", datasource -> (double) druidDataSource.getMaxActive());
// connection pool core metrics
createGauge(druidDataSource, "druid_active_count", "Active count", datasource -> (double) druidDataSource.getActiveCount());
createGauge(druidDataSource, "druid_active_peak", "Active peak", datasource -> (double) druidDataSource.getActivePeak());
createGauge(druidDataSource, "druid_pooling_peak", "Pooling peak", datasource -> (double) druidDataSource.getPoolingPeak());
createGauge(druidDataSource, "druid_pooling_count", "Pooling count", datasource -> (double) druidDataSource.getPoolingCount());
createGauge(druidDataSource, "druid_wait_thread_count", "Wait thread count", datasource -> (double) druidDataSource.getWaitThreadCount());
// connection pool detail metrics
createGauge(druidDataSource, "druid_not_empty_wait_count", "Not empty wait count", datasource -> (double) druidDataSource.getNotEmptyWaitCount());
createGauge(druidDataSource, "druid_not_empty_wait_millis", "Not empty wait millis", datasource -> (double) druidDataSource.getNotEmptyWaitMillis());
createGauge(druidDataSource, "druid_not_empty_thread_count", "Not empty thread count", datasource -> (double) druidDataSource.getNotEmptyWaitThreadCount());
createGauge(druidDataSource, "druid_logic_connect_count", "Logic connect count", datasource -> (double) druidDataSource.getConnectCount());
createGauge(druidDataSource, "druid_logic_close_count", "Logic close count", datasource -> (double) druidDataSource.getCloseCount());
createGauge(druidDataSource, "druid_logic_connect_error_count", "Logic connect error count", datasource -> (double) druidDataSource.getConnectErrorCount());
createGauge(druidDataSource, "druid_physical_connect_count", "Physical connect count", datasource -> (double) druidDataSource.getCreateCount());
createGauge(druidDataSource, "druid_physical_close_count", "Physical close count", datasource -> (double) druidDataSource.getDestroyCount());
createGauge(druidDataSource, "druid_physical_connect_error_count", "Physical connect error count", datasource -> (double) druidDataSource.getCreateErrorCount());
// sql execution core metrics
createGauge(druidDataSource, "druid_error_count", "Error count", datasource -> (double) druidDataSource.getErrorCount());
createGauge(druidDataSource, "druid_execute_count", "Execute count", datasource -> (double) druidDataSource.getExecuteCount());
// transaction metrics
createGauge(druidDataSource, "druid_start_transaction_count", "Start transaction count", datasource -> (double) druidDataSource.getStartTransactionCount());
createGauge(druidDataSource, "druid_commit_count", "Commit count", datasource -> (double) druidDataSource.getCommitCount());
createGauge(druidDataSource, "druid_rollback_count", "Rollback count", datasource -> (double) druidDataSource.getRollbackCount());
// sql execution detail
createGauge(druidDataSource, "druid_prepared_statement_open_count", "Prepared statement open count", datasource -> (double) druidDataSource.getPreparedStatementCount());
createGauge(druidDataSource, "druid_prepared_statement_closed_count", "Prepared statement closed count", datasource -> (double) druidDataSource.getClosedPreparedStatementCount());
createGauge(druidDataSource, "druid_ps_cache_access_count", "PS cache access count", datasource -> (double) druidDataSource.getCachedPreparedStatementAccessCount());
createGauge(druidDataSource, "druid_ps_cache_hit_count", "PS cache hit count", datasource -> (double) druidDataSource.getCachedPreparedStatementHitCount());
createGauge(druidDataSource, "druid_ps_cache_miss_count", "PS cache miss count", datasource -> (double) druidDataSource.getCachedPreparedStatementMissCount());
createGauge(druidDataSource, "druid_execute_query_count", "Execute query count", datasource -> (double) druidDataSource.getExecuteQueryCount());
createGauge(druidDataSource, "druid_execute_update_count", "Execute update count", datasource -> (double) druidDataSource.getExecuteUpdateCount());
createGauge(druidDataSource, "druid_execute_batch_count", "Execute batch count", datasource -> (double) druidDataSource.getExecuteBatchCount());
// none core metrics, some are static configurations
createGauge(druidDataSource, "druid_max_wait", "Max wait", datasource -> (double) druidDataSource.getMaxWait());
createGauge(druidDataSource, "druid_max_wait_thread_count", "Max wait thread count", datasource -> (double) druidDataSource.getMaxWaitThreadCount());
createGauge(druidDataSource, "druid_login_timeout", "Login timeout", datasource -> (double) druidDataSource.getLoginTimeout());
createGauge(druidDataSource, "druid_query_timeout", "Query timeout", datasource -> (double) druidDataSource.getQueryTimeout());
createGauge(druidDataSource, "druid_transaction_query_timeout", "Transaction query timeout", datasource -> (double) druidDataSource.getTransactionQueryTimeout());
});
}
private void createGauge(DruidDataSource weakRef, String metric, String help, ToDoubleFunction<DruidDataSource> measure) {
Gauge.builder(metric, weakRef, measure)
.description(help)
.tag(LABEL_NAME, weakRef.getUsername() + "-" + weakRef.getUrl())
.tag("application", applicationName)
.register(this.registry);
}
}
package com.haoze.doctor.config;
import com.alibaba.druid.pool.DruidDataSource;
import io.micrometer.core.instrument.MeterRegistry;
import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.boot.actuate.autoconfigure.metrics.MeterRegistryCustomizer;
import org.springframework.boot.autoconfigure.condition.ConditionalOnClass;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import javax.sql.DataSource;
import java.sql.SQLException;
import java.util.ArrayList;
import java.util.Collection;
import java.util.List;
@Configuration
@ConditionalOnClass({DruidDataSource.class, MeterRegistry.class})
@Slf4j
public class MetricsConfiguration {
@Value("${spring.application.name}")
private String applicationName;
private final MeterRegistry registry;
@Autowired
private DruidCollector druidCollector;
public MetricsConfiguration(MeterRegistry registry) {
this.registry = registry;
}
@Bean
MeterRegistryCustomizer<MeterRegistry> configurer() {
return (registry) -> registry.config().commonTags("application", applicationName);
}
@Autowired
public void bindMetricsRegistryToDruidDataSources(Collection<DataSource> dataSources) throws SQLException {
List<DruidDataSource> druidDataSources = new ArrayList<>(dataSources.size());
for (DataSource dataSource : dataSources) {
DruidDataSource druidDataSource = dataSource.unwrap(DruidDataSource.class);
if (druidDataSource != null) {
druidDataSources.add(druidDataSource);
}
}
// DruidCollector druidCollector = new DruidCollector(druidDataSources, registry);
druidCollector.register(druidDataSources);
log.info("finish register metrics to micrometer");
}
}
- pom文件中的druid版本需要在这个以上:
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>druid</artifactId>
<version>1.1.14</version>
</dependency>
其他需要引入的maven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>
<!-- micrometer获取JVM相关信息,并展示在Grafana上 -->
<dependency>
<groupId>io.github.mweirauch</groupId>
<artifactId>micrometer-jvm-extras</artifactId>
<version>0.2.2</version>
</dependency>
- actuator配置需要支持prometheus
management:
endpoints:
web:
exposure:
include: "httptrace,health,shutdown,prometheus"
shutdown:
enabled: true
health:
show-details: ALWAYS
- 本地验证:
5. 对于普通的部署方式,到这一步之后,我们直接到prometheus上去配置ip和端口就可以实现监控了,像下面这样在prometheus.yml配置文件中进行配置,并重启服务prometheus服务即可。
#新增jvm监控任务
- job_name: "jvm"
# 采集数据间隔时间
scrape_interval: 5s
# 采集时的超时时间
scrape_timeout: 5s
# 采集数据的路径
metrics_path: '/actuator/prometheus'
# 应用服务的地址
static_configs:
- targets: ['169.169.169.98:8203']
- 但对于容器化部署,这种ip会变的场景下,需要借助prometheus的注册发现来实现了,比如这里我们通过nacos注册发现。nacos的application.properties文件中进行修改,修改后重启nacos服务
## 配置设置为true
nacos.prometheus.metrics.enabled=true
然后可以通过 http://ip:8848/nacos/prometheus/,查看注册内容。
6. prometheus配置自动发现。修改prometheus的prometheus.yml,添加以下内容:
- job_name: 'prod-jvm'
metrics_path: /actuator/prometheus
scheme: http
http_sd_configs:
## namespaceId/prod 表示指定prod这个命名空间下的
## 如果不指定命名空间,可以省去这部分,直接:http://ip:8848/nacos/prometheus/
- url: http://ip:8848/nacos/prometheus/namespaceId/prod