Understanding Pipelines

Your Code's Journey from Commit to Cloud

Every time you push code, a Pipeline springs to life. It's the automated workflow that transforms your source code into a running application. Think of it as your personal deployment assistant that never sleeps, never makes mistakes, and always follows the same reliable process.

Pipeline Philosophy: We believe deployments should be boring—predictable, fast, and forgettable. Pipelines make that happen.

The Anatomy of a Pipeline

When you push code, here's what happens in your pipeline:

graph LR
    A[Git Push] --> B[Webhook Received]
    B --> C[Pipeline Created]
    C --> D[Clone Repository]
    D --> E[Build Image]
    E --> F[Push to Registry]
    F --> G[Deploy to Environment]
    G --> H[Health Check]

    style C fill:#4CAF50,stroke:#333,stroke-width:2px
    style E fill:#2196F3,stroke:#333,stroke-width:2px
    style G fill:#FF9800,stroke:#333,stroke-width:2px

Let's break down each stage:

1. Trigger and Initialization

🎯 Smart Triggering

Pipelines start when:

Code is pushed to your configured branch
A pull request is opened (if enabled)
You manually trigger a rebuild
Relevant files change (based on trigger paths)

2. Repository Operations

The pipeline efficiently clones your code:

# What happens behind the scenes
- task: git-clone
  params:
    url: $(params.repo-url)
    revision: $(params.commit-sha)
    sparse-checkout-directories: $(params.sparse-dirs)  # Optional

Smart Features:

Sparse Checkout: Only clone what you need (great for monorepos)
Shallow Clones: Faster clones for large repositories
Cached Layers: Reuse unchanged dependencies

3. Build Stage

This is where the magic happens. Depending on your configuration:

Buildpacks (Auto-Magic)

Cloud Native Buildpacks analyze your code and create an optimized image:

# Behind the scenes
Detecting language...       ✓ Node.js detected
Installing dependencies...  ✓ npm install
Building application...     ✓ npm run build
Creating layers...          ✓ Optimized for caching
Generating image...         ✓ Secure, minimal runtime

What Buildpacks Do For You:

Select the right base image
Install language runtime (correct version)
Configure build environment
Install dependencies efficiently
Apply security patches automatically
Optimize for container layer caching

Dockerfile

Your Dockerfile is executed with build optimizations:

# Your Dockerfile
FROM node:18-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --production
COPY . .
EXPOSE 3000
CMD ["node", "server.js"]

Build Cache: We cache Docker layers between builds. Structure your Dockerfile to copy dependencies first, code last.

4. Image Publishing

Once built, your container image is tagged and pushed:

# Image naming convention
<registry>/<image-repository-path>:<git-commit-sha>

# Example
us-central1-docker.pkg.dev/my-project/my-api:a3f4c2b7

Key Points:

Images are tagged with the exact commit SHA
Immutable tags ensure you can always rollback
Registry is environment-specific (dev/staging/prod)

5. Deployment

The final stage deploys your new image:

Kubernetes Deployments

Rolling updates with zero downtime
Automatic rollback on failures
Health checks before traffic
Auto-scaling based on load

Other Platforms

AWS ECS task definitions
Google Cloud Run services
Azure Container Instances
Platform-specific optimizations

Pipeline Performance

We've optimized pipelines for speed. Here's what makes them fast:

Parallel Execution

When possible, tasks run in parallel:

graph TD
    A[Clone Repo] --> B[Build Frontend]
    A --> C[Build Backend]
    A --> D[Run Tests]
    B --> E[Push Images]
    C --> E
    D --> E
    E --> F[Deploy All]

Intelligent Caching

What Gets Cached:

Dependency Cache: npm modules, pip packages, Go modules, Maven dependencies
Layer Cache: Docker layers between builds for faster image creation
Build Cache: Compiled artifacts, build outputs
Test Cache: Test results for unchanged code

Resource Allocation

Pipelines get dedicated resources:

CPU: 2-4 cores for build tasks
Memory: 4-8GB RAM
Network: High-speed registry access
Storage: SSD-backed workspace

Monitoring Your Pipelines

Real-Time Logs

Follow your pipeline execution:

# Stream logs as they happen
planton pipeline logs <pipeline-id> --follow

# Get logs for a specific stage
planton pipeline logs <pipeline-id> --stage build

Pipeline Status

Understand what's happening:

# Quick status check
planton pipeline status <pipeline-id>

# Output
Pipeline: pipeline-7f3a2b1c
Status: Running
Stage: Building Image (2/4)
Duration: 2m 34s
Commit: feat: add user authentication

Notifications

Get notified about pipeline events:

Success: Deployment complete
Failure: Build or deploy failed
Manual Approval: Waiting for approval
Rollback: Automatic rollback triggered

Advanced Pipeline Features

Manual Gates

Add approval steps for production deployments:

# In your pipeline configuration
stages:
  - name: build
    tasks: [...]
  - name: deploy-staging
    tasks: [...]
  - name: manual-approval
    type: manual
    approvers: ["platform-team", "product-owner"]
  - name: deploy-production
    tasks: [...]
    when: manual-approval.approved

Conditional Execution

Run different pipelines based on conditions:

# Deploy to production only from main branch
- task: deploy-production
  when:
    branch: main

# Run integration tests only for API changes
- task: integration-tests
  when:
    paths: ["api/**", "shared/**"]

Custom Tasks

Extend pipelines with custom tasks:

# Using Tekton tasks
- task: database-migration
  params:
    - name: migration-script
      value: $(workspaces.source.path)/migrations/latest.sql
    - name: database-url
      value: $(secrets.database.url)

Pull Request Pipelines

When you enable PR deployments, each pull request gets:

Preview Environment Features:

Isolated deployment of your branch
Unique URL for testing (e.g., pr-123.preview.your-domain.com)
Automatic cleanup when PR is closed
Comments on PR with deployment status and URL

Enable it in your service:

pipeline_configuration:
  enable_pull_request_deployments: true

Debugging Failed Pipelines

When things go wrong (they sometimes do), here's how to debug:

1. Check the Logs

# Get detailed logs
planton pipeline logs <pipeline-id> -v

# Focus on the failed stage
planton pipeline logs <pipeline-id> --stage build --tail 100

2. Common Issues and Fixes

Build fails with "out of memory"

Your build needs more resources. Add to your pipeline:

resources:
  requests:
    memory: "8Gi"
    cpu: "4"

Cannot find module/package

Ensure all dependencies are properly declared:

Node.js: Check package.json
Python: Check requirements.txt
Go: Run go mod tidy

Deployment times out

Check your health checks and startup time:

Increase initialDelaySeconds in health checks
Ensure your app starts within the timeout
Check for missing environment variables

3. Re-run with Debug Mode

# Re-run pipeline with verbose logging
planton pipeline rerun <pipeline-id> --debug

# This enables:
# - Verbose build output
# - Detailed dependency resolution
# - Step-by-step execution logs

Pipeline Best Practices

1. Optimize Build Times

Use specific base images in Dockerfiles
Order Dockerfile commands for better caching
Minimize build context with .dockerignore
Parallelize independent tasks

2. Secure Your Pipelines

Never hardcode secrets - use Secrets Manager
Pin dependency versions for reproducibility
Scan images for vulnerabilities (automatic)
Use least-privilege service accounts

3. Make Pipelines Reliable

Add health checks to catch issues early
Use retry logic for flaky operations
Set resource limits to prevent runaway builds
Monitor pipeline metrics for trends

What's Next?

Now that you understand pipelines, explore:

Custom Pipelines - Write your own Tekton pipelines
Pipeline Optimization - Make builds even faster
CI/CD Best Practices - Industry patterns
Monitoring & Alerts - Stay informed about your deployments

Remember: The best pipeline is one you don't have to think about. Set it up once, let it run forever.

Planton Documentation