1.Spring Boot 工程集成 Micrometer
1.1引入依赖
-
<dependency>
-
<groupId>org.springframework.boot
</groupId>
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<artifactId>spring-boot-starter-actuator
</artifactId>
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</dependency>
-
<dependency>
-
<groupId>io.micrometer
</groupId>
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<artifactId>micrometer-registry-prometheus
</artifactId>
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</dependency>
1.2配置
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management.server.port=
9003
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management.endpoints.web.exposure.include=*
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management.endpoint.metrics.enabled=
true
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management.endpoint.health.
show-details=always
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management.endpoint.health.probes.enabled=
true
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management.endpoint.prometheus.enabled=
true
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management.metrics.
export.prometheus.enabled=
true
-
management.metrics.tags.application=voice-qc-backend
这里management.endpoints.web.exposure.include=*配置为开启 Actuator 服务，因为Spring Boot Actuator 会自动配置一个 URL 为/actuator/Prometheus的 HTTP 服务来供 Prometheus 抓取数据，不过默认该服务是关闭的，该配置将打开所有的 Actuator 服务。
management.metrics.tags.application配置会将该工程应用名称添加到计量器注册表的 tag 中去，方便后边 Prometheus 根据应用名称来区分不同的服务。
1.3监控jvm信息
然后在工程启动主类中添加 Bean 如下来监控 JVM 性能指标信息：
-
@SpringBootApplication
-
public
class
GatewayDatumApplication {
-
-
public
static
void
main(
String[] args) {
-
SpringApplication.
run(
GatewayDatumApplication.
class, args);
-
}
-
-
@Bean
-
MeterRegistryCustomizer<
MeterRegistry>
configurer(
-
@Value(
"${spring.application.name}")
String applicationName) {
-
return (registry) -> registry.
config().
commonTags(
"application", applicationName);
-
}
-
-
}
1.4创建自定义监控
监控请求次数与响应时间
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package com.lianxin.gobot.api.monitor;
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-
import io.micrometer.core.instrument.Counter;
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import io.micrometer.core.instrument.MeterRegistry;
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import io.micrometer.core.instrument.Timer;
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import lombok.Getter;
-
import org.springframework.beans.factory.
annotation.Autowired;
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import org.springframework.beans.factory.
annotation.Value;
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import org.springframework.stereotype.Component;
-
-
import javax.
annotation.PostConstruct;
-
-
/**
-
* @Author: GZ
-
* @CreateTime: 2022-08-30 10:50
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* @Description: 自定义监控服务
-
* @Version: 1.0
-
*/
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@Component
-
public
class
PrometheusCustomMonitor {
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/**
-
* 上报拨打请求次数
-
*/
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@Getter
-
private Counter reportDialRequestCount;
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/**
-
* 上报拨打URL
-
*/
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@Value("${lx.call-result-report.url}")
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private String callReportUrl;
-
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/**
-
* 上报拨打响应时间
-
*/
-
@Getter
-
private Timer reportDialResponseTime;
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@Getter
-
private
final MeterRegistry registry;
-
-
-
@Autowired
-
public PrometheusCustomMonitor(MeterRegistry registry) {
-
this.registry = registry;
-
}
-
-
@PostConstruct
-
private void
init() {
-
reportDialRequestCount = registry.counter(
"go_api_report_dial_request_count",
"url",callReportUrl);
-
reportDialResponseTime= registry.timer(
"go_api_report_dial_response_time",
"url",callReportUrl);
-
}
-
}
1.5添加具体业务代码监控
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//统计请求次数
-
prometheusCustomMonitor.getReportDialRequestCount().increment();
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long startTime = System.currentTimeMillis();
-
String company = HttpUtils.post(companyUrl,
"");
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//统计响应时间
-
long endTime = System.currentTimeMillis();
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prometheusCustomMonitor.getReportDialResponseTime().
record(endTime-startTime, TimeUnit.MILLISECONDS);
在浏览器访问http://127.0.0.1:9001/actuator/prometheus，就可以看到服务的一系列不同类型 metrics 信息，例如jvm_memory_used_bytes gauge、jvm_gc_memory_promoted_bytes_total counter，go_api_report_dial_request_count等
到此，Spring Boot 工程集成 Micrometer 就已经完成，接下里就要与 Prometheus 进行集成了。
2.集成 Prometheus
2.1安装
docker pull prom/prometheus
mdkir /usr/local/prometheus
vi prometheus.yml
-
# my
global config
-
global:
-
scrape_interval:
15s #
Set the scrape interval
to every
15 seconds.
Default
is every
1 minute.
-
evaluation_interval:
15s # Evaluate rules every
15 seconds. The
default
is every
1 minute.
-
# scrape_timeout
is
set
to the
global
default (
10s).
-
-
# Alertmanager configuration
-
alerting:
-
alertmanagers:
-
- static_configs:
-
- targets:
-
# - alertmanager:
9093
-
-
# Load rules once
and periodically evaluate them according
to the
global
'evaluation_interval'.
-
rule_files:
-
# -
"first_rules.yml"
-
# -
"second_rules.yml"
-
-
# A scrape configuration containing exactly one endpoint
to scrape:
-
# Here it
's Prometheus itself.
-
scrape_configs:
-
# The job name
is added
as a label `job=<job_name>`
to any timeseries scraped
from this config.
-
- job_name:
'prometheus'
-
-
# metrics_path defaults
to
'/metrics'
-
# scheme defaults
to
'http'.
-
-
static_configs:
-
- targets: [
'192.168.136.129:9090']
docker run -d --name prometheus -p 9090:9090 -v/usr/local/prometheus/prometheus.yml:/etc/prometheus/prometheus.yml prom/prometheus
2.2集成配置
-
global:
-
scrape_interval:
15s
-
-
scrape_configs:
-
- job_name:
"prometheus"
-
static_configs:
-
- targets: [
"localhost:9090"]
-
- job_name:
"metricsLocalTest"
-
metrics_path:
"/actuator/prometheus"
-
static_configs:
-
- targets: [
"localhost:9003"]
这里localhost:9001就是上边本地启动的服务地址，也就是 Prometheus 要监控的服务地址。同时可以添加一些与应用相关的标签，方便后期执行 PromSQL 查询语句区分。最后重启 Prometheus 服务
3.使用 Grafana Dashboard 展示监控项
3.1安装grafana
docker pull grafana/grafana
docker run -d --name grafana -p 3000:3000 -v /usr/local/grafana:/var/lib/grafana grafana/grafana
默认用户名/密码 admin/admin
3.2配置prometheus数据源
3.3增加jvm面板
模板编号为4701
3.4配置业务接口监控面板
转载：https://blog.csdn.net/m0_71777195/article/details/128351051
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