Step-by-step Guide to Monitoring the Helix Project in Production

Monitoring the Helix project in production involves establishing robust logging, health checks, and performance metrics. This section details the steps necessary to effectively monitor the application deployed using Docker.

1. Docker Configuration

The initial step in setting up monitoring is ensuring that your Docker configuration is optimized for production. The Dockerfile provided below illustrates how to build the application and includes significant logging considerations. Logs can be particularly useful for monitoring application health and debugging.

# Backend build
FROM golang:1.22 AS go-build-env
WORKDIR /app

# <- COPY go.mod and go.sum files to the workspace
COPY go.mod .
COPY go.sum .

RUN go mod download

# COPY the source code as the last step
COPY api ./api
COPY .git /.git

WORKDIR /app/api

# Build the Go app
RUN CGO_ENABLED=0 go build -ldflags "-s -w" -o /helix

# Frontend build
FROM node:21-alpine AS ui-build-env

WORKDIR /app

RUN echo "installing apk packages" && \
  apk update && \
  apk upgrade && \
  apk add \
    bash \
    git \
    curl \
    openssh

# root config
COPY ./frontend/*.json /app/
COPY ./frontend/yarn.lock /app/yarn.lock

# Install modules
RUN yarn install

# Copy the rest of the code
COPY ./frontend /app

# Build the frontend
RUN yarn build

FROM alpine:3.17
RUN apk --update add ca-certificates

COPY --from=go-build-env /helix /helix
COPY --from=ui-build-env /app/dist /www

ENV FRONTEND_URL=/www

ENTRYPOINT ["/helix", "serve"]

2. Implementing Health Checks

To ensure that the application is responding appropriately, implement health checks via the /health endpoint in your Go application. This should respond with a status code and a message.

Here is an example implementation in Go:

package main

import (
    "net/http"
)

func healthCheck(w http.ResponseWriter, r *http.Request) {
    w.WriteHeader(http.StatusOK)
    w.Write([]byte("OK"))
}

func main() {
    http.HandleFunc("/health", healthCheck)
    http.ListenAndServe(":8080", nil)
}

This /health endpoint should be monitored by your orchestration system (like Kubernetes or Docker Compose) to ensure that instances are responding.

3. Logging Configuration

Configure logging to capture application events and errors. Use a logging framework appropriate for Go such as logrus. Basic logging can be added as follows:

import (
    log "github.com/sirupsen/logrus"
)

func main() {
    log.SetFormatter(&log.JSONFormatter{})
    log.SetOutput(os.Stdout)
    log.SetLevel(log.InfoLevel)

    log.Info("Application Starting")
    
    // ... other application code ...
}

Logs should be aggregated and monitored using tools like ELK Stack (Elasticsearch, Logstash, and Kibana) or any other observability platform to analyze log data and detect anomalies.

4. Monitoring Application Performance

Integrate monitoring to track key performance metrics such as response times and request counts. One approach is using Prometheus with a Go exporter.

Install the Prometheus Go client:

go get github.com/prometheus/client_golang/prometheus

Then, instrument your application code:

import (
    "github.com/prometheus/client_golang/prometheus/promhttp"
)

var (
    requestCount = prometheus.NewCounterVec(
        prometheus.CounterOpts{
            Name: "http_requests_total",
            Help: "Total number of HTTP requests",
        },
        []string{"method"},
    )
)

func main() {
    prometheus.MustRegister(requestCount)

    http.Handle("/metrics", promhttp.Handler())
    http.HandleFunc("/", func(w http.ResponseWriter, r *http.Request) {
        requestCount.WithLabelValues(r.Method).Inc()
        w.Write([]byte("Hello, World!"))
    })

    http.ListenAndServe(":8080", nil)
}

5. Setting Up Alerting

To notify your team of critical issues, integrate alerting based on metrics. With Prometheus, you can set alerts in your rules file:

groups:
- name: application-alerts
  rules:
  - alert: HighErrorRate
    expr: rate(http_requests_total{status="500"}[5m]) > 0.1
    for: 10m
    labels:
      severity: critical
    annotations:
      summary: "High error rate detected"
      description: "Error rate has exceeded the threshold."

6. Dashboard Visualization

Establish dashboards using Grafana to visualize metrics collected from your application. Set up graphs for request rates, error rates, and application latency. This visual representation aids in maintaining the health of your application.

Conclusion

Implementing these monitoring steps ensures that your Helix application is resilient and performant in a production environment. By leveraging health checks, logging, metrics collection, and alerting, problems can be identified and resolved proactively, mitigating risks associated with production deployments.

Source:

• Helix Dockerfile