How to Integrate Grafana & Prometheus Monitoring into Midway Applications

Prometheus is an open‑source monitoring system originally built at SoundCloud and later graduated to the Cloud Native Computing Foundation (CNCF). Grafana is an open‑source analytics and visualization suite that can use Prometheus as a data source.

This article shows how to integrate Grafana and Prometheus with a Midway application.

Install Component

First install the Midway Prometheus monitoring component: npm install @midwayjs/prometheus -S Then import the component in configuration.ts:

// src/configuration.ts
import { Configuration } from '@midwayjs/decorator';
import * as prometheus from '@midwayjs/prometheus'; // import module
import { join } from 'path';
@Configuration({
  imports: [prometheus], // import module
  importConfigs: [join(__dirname, 'config')]
})
export class AutoConfiguration {}

After starting the application, a new endpoint ${host}:${port}/metrics becomes available.

Prometheus pulls monitoring data via HTTP, so load a web framework such as Koa or Express and start the application in multi‑framework mode.

Visiting the endpoint returns the current metrics.

Other Configurations

The component provides configuration options that can be customized in config.default.ts:

import { DefaultConfig } from '@midwayjs/prometheus';
export const prometheus: DefaultConfig = {
  labels: {
    APP_NAME: 'demo_project'
  }
};

Additional settings are available in the DefaultConfig definition. For example, you can label nodes belonging to the same application to distinguish them in the metrics.

Data Collection

The Prometheus component added to Midway collects metrics inside the Node process. To let Prometheus scrape these metrics, set up a Prometheus server.

Set up Prometheus

Use docker‑compose.yml to launch Prometheus:

version: "2.2"
services:
  tapi:
    logging:
      driver: "json-file"
      options:
        max-size: "50m"
    image: prom/prometheus
    restart: always
    volumes:
      - ./prometheus_data:/prometheus_data:rw
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
      - ./targets.json:/etc/prometheus/targets.json
    command:
      - '--storage.tsdb.path=/prometheus_data'
      - '--config.file=/etc/prometheus/prometheus.yml'
      - '--storage.tsdb.retention=10d'
    ports:
      - "9090:9090"

The prometheus.yml configuration:

global:
  scrape_interval: 15s # Set the scrape interval to every 15 seconds.
  evaluation_interval: 15s # Evaluate rules every 15 seconds.
scrape_configs:
  - job_name: "node"
    file_sd_configs:
    - refresh_interval: 1m
      files:
        - "/etc/prometheus/targets.json"
  - job_name: 'prometheus'
    static_configs:
      - targets: ['localhost:9090']

The targets.json file (replace ${ip} with the Node.js server IP):

[
  {
    "targets": [ "${ip}:3002" ],
    "labels": {
      "env": "prod",
      "job": "api"
    }
  }
]

Start Prometheus with: $ docker-compose up Prometheus will now scrape the metrics exposed by the Midway application.

Data Display

Use Grafana to visualize the collected data. Run Grafana via Docker:

$ docker run -d --name=grafana -p 3000:3000 grafana/grafana

Access http://127.0.0.1:3000 with the default credentials admin:admin . Then add Prometheus as a data source and create dashboards as shown in the screenshots.

After importing a sample dashboard (ID 11159), developers can monitor their Node services, detect issues such as memory leaks or unexpected restarts, and perform custom analyses.