SpringCloud DataFlow 4. Prometheus + Grafana 监控

0. SpringCloud DataFlow — 0. 本地部署部署
1. SpringCloud DataFlow — 1. 自定义Processor
2. SpringCloud DataFlow — 2. 自定义Sink
3. SpringCloud DataFlow — 3. 暴露properties
4. SpringCloud DataFlow — 4. Prometheus + Grafana 监控
5. SpringCloud DataFlow — 5. 多分支负载

以 unpack-processor-kafka 为例

1. pom.xml 中添加依赖

1.1 dependencies

    org.springframework.boot
    spring-boot-starter-actuator



    org.springframework.cloud.stream.app
    app-starters-micrometer-common


    org.springframework.cloud.stream.app
    app-starters-security-common


    io.micrometer
    micrometer-registry-prometheus



    org.springframework.boot
    spring-boot-starter-web

1.2 dependencyManagement

    org.springframework.cloud.stream.app
    app-starters-core-dependencies
    2.1.0.RELEASE
    pom
    import

2. Prometheus Service Discovery

2.1 下载zip代码包

https://github.com/tzolov/spring-cloud-dataflow-prometheus-service-discovery

2.2 解压缩并切换路径

cd spring-cloud-dataflow-prometheus-service-discovery-master

2.3 打包jar

./mvnw clean package -Dmaven.test.skip=true

2.4 运行服务

java -jar target/spring-cloud-dataflow-prometheus-service-discovery-0.0.5.BUILD-SNAPSHOT.jar --metrics.prometheus.target.discoveryUrl='http://127.0.0.1:9393/runtime/apps' --metrics.prometheus.target.cron='0/30 * * * * *' --metrics.prometheus.target.mode=local --metrics.prometheus.target.overrideIp='127.0.0.1' --server.port=8181

3. Prometheus

3.1 安装prometheus

brew install prometheus

3.2 进入安装目录

cd /usr/local/Cellar/prometheus/2.11.1

3.3 修改homebrew.mxcl.prometheus.plist

# 修改第十一行
/usr/local/opt/prometheus/bin/prometheus --config.file=/usr/local/etc/prometheus.yml

3.4 添加prometheus.yml

vim /usr/local/etc/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=` to any timeseries scraped from this config.
  - job_name: 'prometheus'

    # metrics_path defaults to '/metrics'
    # scheme defaults to 'http'.

    static_configs:
    - targets: ['localhost:9090']

  - job_name: 'dataflow'
    metrics_path: '/actuator/prometheus'
    file_sd_configs:
      - files:
          - /tmp/targets.json

3.4 启动服务

brew services start prometheus

3.5 查看

http://localhost:9090/targets

4. Grafana

4.1 安装Grafana

brew install grafana

4.2 启动服务

brew services start grafana

4.3 查看

http://localhost:3000

4.4 配置

4.4.1 data source

Name: prometheus
URL: http://localhost:9090
Access: server(default)

Save & Test

4.4.2 dashboard

# 为了简便, 导入被人写好的配置
1. 访问: https://grafana.com/grafana/dashboards
2. Search: data flow
3. 点击: "SCDF Streams Prometheus"
4. 获得dashboard编号: 9933
5. 访问: http://localhost:3000/dashboard/import
6. Grafana.com Dashboard 输入 9933
7. 点击 "load"
8. ScdfPrometheus 选择4.4.1中创建的数据源数据源 "prometheus"
9. 点击 "Import"

转载于:https://my.oschina.net/tianshl/blog/3097717