DevOps Applications in E-commerce Real-World Examples

import http from "k6/http";
import { check, sleep } from "k6";

export const options = {
  stages: [
    { duration: "2m", target: 200 },  // ramp up
    { duration: "5m", target: 200 },  // hold
    { duration: "1m", target: 0 },    // ramp down
  ],
  thresholds: {
    http_req_duration: ["p(95)<800"], // 95% under 800 ms
    http_req_failed: ["rate<0.01"],   // under 1% errors
  },
};

export default function () {
  const res = http.get("https://staging.example-shop.in/api/products?category=shoes");
  check(res, { "status is 200": (r) => r.status === 200 });
  sleep(1);
}

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: catalog-api
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: catalog-api
  minReplicas: 3
  maxReplicas: 30
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 65

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: checkout
spec:
  replicas: 10
  strategy:
    canary:
      steps:
        - setWeight: 10
        - pause: { duration: 10m }
        - setWeight: 50
        - pause: { duration: 10m }
  selector:
    matchLabels:
      app: checkout
  template:
    metadata:
      labels:
        app: checkout
    spec:
      containers:
        - name: checkout
          image: registry.example.in/checkout:2.8.0

import os

def new_coupon_engine_enabled(user_id: int) -> bool:
    # Roll out to a percentage of users, controlled by config
    percent = int(os.environ.get("NEW_COUPON_PERCENT", "0"))
    return (user_id % 100) < percent

def apply_coupon(cart, user_id):
    if new_coupon_engine_enabled(user_id):
        return new_engine(cart)
    return old_engine(cart)

# Checkout success rate over the last 5 minutes
sum(rate(http_requests_total{service="checkout", code=~"2.."}[5m]))
/
sum(rate(http_requests_total{service="checkout"}[5m]))

# p95 latency for the product API
histogram_quantile(0.95,
  sum(rate(http_request_duration_seconds_bucket{service="catalog"}[5m])) by (le))

groups:
  - name: checkout-slo
    rules:
      - alert: CheckoutSuccessLow
        expr: |
          sum(rate(http_requests_total{service="checkout", code=~"2.."}[10m]))
          /
          sum(rate(http_requests_total{service="checkout"}[10m])) < 0.995
        for: 10m
        labels:
          severity: page
        annotations:
          summary: "Checkout success below 99.5% for 10 minutes"

Sale readiness checklist (2-3 weeks before)
[ ] Load test: browse, search, cart, checkout
[ ] Autoscaling limits reviewed (min/max replicas)
[ ] Database connections and read replicas checked
[ ] CDN cache rules for images and static pages
[ ] Code freeze window agreed for critical services
[ ] On-call rota and escalation contacts confirmed
[ ] Dashboards for orders, latency and errors ready

Incident: Checkout errors for UPI payments
Start / end time:
Customer impact (what users saw):
Detection: which alert or report
Timeline of actions:
Root cause:
Fix applied:
Follow-ups (owner, due date):

Mini e-commerce DevOps lab
1. Two services: catalog and checkout (any language)
2. Docker images + Kubernetes (kind or minikube)
3. HPA on catalog, k6 test to trigger scaling
4. Canary rollout for checkout (Argo Rollouts)
5. Prometheus + Grafana with a success-rate panel