Continuous Integration and Continuous Delivery (CI/CD) automate the process of building, testing, and deploying software. Modern cloud-native systems rely on safe deployment strategies and declarative infrastructure workflows to reduce risk and improve reliability.

CI/CD Overview

Continuous Integration (CI)

Developers frequently merge code into a shared repository. Each merge triggers:

• Automated build
• Automated tests
• Static analysis
• Security scans

CI Flow

flowchart LR
    Dev[Developer] -->|Push Code| Git[Git Repository]
    Git --> CI[CI Pipeline]
    CI --> Build[Build Artifact]
    CI --> Test[Run Tests]
    CI --> Scan[Security / Lint]

Continuous Delivery / Deployment (CD)

After CI succeeds, the system:

• Packages artifacts (Docker image, binary, etc.)
• Deploys to staging
• Optionally deploys automatically to production

CD Flow

flowchart LR
    Artifact[Artifact Registry] --> Staging[Deploy to Staging]
    Staging --> Approval{Manual Approval?}
    Approval -->|Yes| Prod[Deploy to Production]
    Approval -->|No| Prod

Deployment Strategies

Deployment strategies reduce risk when releasing new versions.

Blue-Green Deployment

Two identical environments:

• Blue → current production
• Green → new version

Traffic switches only after validation.

Flow

flowchart LR
    Users --> LB[Load Balancer]
    LB --> Blue[Blue v1]
    LB -. switch .-> Green[Green v2]

Process

1. Deploy a new version to Green
2. Test Green
3. Switch traffic from Blue → Green
4. Keep Blue as a rollback target

Pros

• Instant rollback
• Minimal downtime
• Simple mental model

Cons

• Doubles infrastructure cost
• Database migrations must be backward compatible

Canary Deployment

Release to a small percentage of users first.

Flow

flowchart LR
    Users --> LB[Load Balancer]
    LB -->|90%| v1[Version 1]
    LB -->|10%| v2[Version 2 - Canary]

Gradually increase traffic if metrics are healthy.

Steps

1. Deploy v2

2. Route 5–10% traffic

3. Monitor:

   • Error rate
   • Latency
   • Business metrics

4. Gradually increase traffic

Pros

• Low risk
• Real production testing
• Metric-driven rollout

Cons

• More complex routing
• Requires observability

Rolling Deployment

Incrementally replace pods/instances.

flowchart LR
    Old1[Pod v1] --> New1[Pod v2]
    Old2[Pod v1] --> New2[Pod v2]
    Old3[Pod v1] --> New3[Pod v2]

Common in Kubernetes using:

yamlCopy
strategy:
  type: RollingUpdate
  rollingUpdate:
    maxUnavailable: 1
    maxSurge: 1

Push vs. Pull-Based Pipelines

Push-Based Deployment

The CI server pushes changes to the cluster.

Flow

flowchart LR
    Dev --> Git
    Git --> CI
    CI -->|kubectl apply| Cluster

Characteristics

• CI system needs cluster credentials
• Centralized control
• Traditional model

Risks

• Credential leakage
• Harder to scale across clusters

Pull-Based Deployment

The cluster pulls desired state from Git.

Used in GitOps.

Flow

flowchart LR
    Dev --> Git
    Git --> Agent[GitOps Agent]
    Agent --> Cluster

Examples:

• ArgoCD
• Flux

Characteristics

• Cluster has read-only access to Git
• Declarative desired state
• Self-healing

Push vs Pull Comparison

GitOps

GitOps is an operational model where:

• Git is the single source of truth
• Desired state is declarative (YAML, Helm, Kustomize)
• Cluster continuously reconciles with Git

GitOps Architecture

flowchart TD
    Dev[Developer] -->|Commit| GitRepo[Git Repository]
    GitRepo --> Agent[ArgoCD / Flux]
    Agent -->|Apply Desired State| Cluster[Kubernetes Cluster]
    Cluster -->|Report Status| Agent

GitOps Principles

1. Declarative infrastructure
2. Version-controlled desired state
3. Automated reconciliation
4. Observable system state

Reconciliation Loop

sequenceDiagram
    participant Agent
    participant Git
    participant Cluster

    Agent->>Git: Check desired state
    Agent->>Cluster: Compare actual state
    Agent->>Cluster: Apply changes if drift detected

Safe Deployment Patterns in Kubernetes

Progressive Delivery

Combines:

• Canary
• Feature flags
• Automated rollback

Often implemented with:

• Argo Rollouts
• Flagger
• Service Mesh (Istio, Linkerd)

Feature Flags

Separate deployment from release.

flowchart LR
    Deploy[Deploy New Code] --> FeatureFlag{Feature Enabled?}
    FeatureFlag -->|Yes| NewFeature
    FeatureFlag -->|No| Hidden

Benefits:

• Instant enable/disable
• A/B testing
• Gradual feature exposure

CI/CD Best Practices

• Trunk-based development
• Small frequent releases
• Immutable artifacts
• Automated testing
• Observability-first deployments
• Backward-compatible DB migrations
• Infrastructure as Code (IaC)

End-to-End Example

flowchart LR
    Dev --> Git
    Git --> CI[Build & Test]
    CI --> Registry[Push Docker Image]
    Git --> ConfigRepo[Update Helm Chart]
    ConfigRepo --> ArgoCD
    ArgoCD --> Cluster
    Cluster --> Users