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Core Components ClusterIssuer Configuration apiVersion: cert-manager.io/v1 kind: ClusterIssuer metadata: name: letsencrypt-prod spec: acme: server: https://acme-v02.api.letsencrypt.org/directory email: [email protected] privateKeySecretRef: name: letsencrypt-prod-account-key solvers: - http01: ingress: class: nginx - dns01: cloudflare: email: [email protected] apiTokenSecretRef: name: cloudflare-api-token key: api-token Certificate Management Wildcard Certificate apiVersion: cert-manager.io/v1 kind: Certificate metadata: name: wildcard-cert namespace: cert-manager spec: secretName: wildcard-tls commonName: "*.example.com" dnsNames: - "*.example.com" - "example.com" issuerRef: name: letsencrypt-prod kind: ClusterIssuer usages: - digital signature - key encipherment - server auth Production Implementation # Complete cert-manager setup apiVersion: cert-manager.io/v1 kind: Certificate metadata: name: production-certs namespace: production spec: secretName: production-tls duration: 2160h # 90 days renewBefore: 360h # 15 days subject: organizations: - Example Corp commonName: api.example.com dnsNames: - api.example.com - web.example.com - admin.example.com issuerRef: name: letsencrypt-prod kind: ClusterIssuer keystores: jks: create: true passwordSecretRef: name: jks-password key: password --- apiVersion: networking.k8s.io/v1 kind: Ingress metadata: name: secured-ingress annotations: cert-manager.io/cluster-issuer: "letsencrypt-prod" spec: tls: - hosts: - api.example.com - web.example.com secretName: production-tls rules: - host: api.example.com http: paths: - path: / pathType: Prefix backend: service: name: api-service port: number: 80

Trivy Implementation Container Scanning Pipeline # GitLab CI Pipeline Configuration container_scan: image: aquasec/trivy:latest variables: TRIVY_NO_PROGRESS: "true" TRIVY_CACHE_DIR: ".trivycache/" script: - trivy image --exit-code 1 --severity HIGH,CRITICAL --no-progress --format template --template "@/contrib/sarif.tpl" -o trivy-results.sarif $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA artifacts: reports: security: trivy-results.sarif Kubernetes Manifest Scanning apiVersion: batch/v1 kind: CronJob metadata: name: trivy-cluster-scan spec: schedule: "0 0 * * *" jobTemplate: spec: template: spec: serviceAccountName: trivy-scanner containers: - name: trivy image: aquasec/trivy:latest args: - k8s - --report=summary - --severity=HIGH,CRITICAL - all volumeMounts: - name: results mountPath: /results volumes: - name: results persistentVolumeClaim: claimName: scan-results Snyk Integration GitHub Action Integration name: Snyk Security Scan on: pull_request jobs: security: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - name: Run Snyk to check for vulnerabilities uses: snyk/actions/node@master env: SNYK_TOKEN: ${{ secrets.SNYK_TOKEN }} with: args: --severity-threshold=high - name: Container Scan uses: snyk/actions/docker@master env: SNYK_TOKEN: ${{ secrets.SNYK_TOKEN }} with: image: your-registry/app:latest args: --file=Dockerfile - name: IaC Scan uses: snyk/actions/iac@master env: SNYK_TOKEN: ${{ secrets.SNYK_TOKEN }} with: args: --severity-threshold=high Wiz Implementation Cloud Configuration Scanning # Terraform configuration for Wiz resource "wiz_automation_rule" "critical_vuln" { name = "Critical Vulnerability Alert" description = "Alert on critical vulnerabilities in production" enabled = true trigger { type = "VULNERABILITY" vulnerabilities { severity = ["CRITICAL"] has_fix = true } } actions { create_issue { provider = "JIRA" project = "SEC" type = "Bug" } send_notification { channels = ["SLACK"] template = "critical-vuln" } } } Production Implementation Multi-Scanner Integration # ArgoCD Application for Security Scanning apiVersion: argoproj.io/v1alpha1 kind: Application metadata: name: security-scanners spec: project: security source: repoURL: https://github.com/org/security-configs.git targetRevision: HEAD path: scanners/ destination: server: https://kubernetes.default.svc namespace: security syncPolicy: automated: prune: true selfHeal: true syncOptions: - CreateNamespace=true --- # Scanner Configuration apiVersion: v1 kind: ConfigMap metadata: name: scanner-config data: trivy.yaml: | severity: CRITICAL,HIGH ignore-unfixed: true timeout: 10m snyk.yaml: | severity-threshold: high fail-on: upgradable wiz.yaml: | scan-interval: 6h alert-threshold: CRITICAL compliance-frameworks: - SOC2 - PCI

Command Query Responsibility Segregation (CQRS) is an architectural pattern that separates read and write operations in your application. While it adds complexity, CQRS can provide significant benefits for applications with complex business logic, different read/write workloads, or high scalability requirements. Let’s explore how to implement it effectively. Why Consider CQRS? Before diving into implementation, let’s understand when CQRS makes sense: Different Scaling Needs: Your read and write workloads have different scaling requirements Complex Business Logic: Your write operations involve complex business rules Performance Optimization: You need to optimize read and write operations independently Eventual Consistency: Your system can tolerate eventual consistency for read operations Core Components of CQRS Command Stack Implementation The command stack handles all write operations. Here’s how to implement it in TypeScript: ...

While Kubernetes provides built-in scaling based on CPU and memory usage, real-world applications often need to scale based on business-specific metrics. Whether it’s database connections, queue length, or request latency, custom metrics scaling allows you to adapt your infrastructure to your application’s unique needs. Let’s explore how to implement this in a production environment. Why Custom Metrics Scaling? Traditional resource-based scaling (CPU/memory) often fails to capture the true load on your system. Consider these scenarios: ...

As your application grows, database performance often becomes the primary bottleneck. Whether you’re handling millions of users or processing massive datasets, understanding and implementing the right scaling patterns is crucial. Let’s explore practical strategies for scaling databases in production environments. The Three Pillars of Database Scaling Before diving into implementations, it’s important to understand the three main approaches to database scaling: Read Replicas: Scale read operations by distributing them across multiple database copies Sharding: Partition data across multiple databases to distribute write load Caching: Reduce database load by serving frequently-accessed data from memory Let’s explore how to implement each of these strategies in a production environment. ...