Anatomy of a Pipeline YAML

1. Fundamental Structure

Every pipeline consists of these core components:

pipeline.yaml
├── Metadata (name, version)
├── Inputs (data entry points)
├── Outputs (results delivery)
├── Nodes (processing units) 
└── Flows (data highways)

2. Component Deep Dive

2.1 Metadata Section

name: "Face Styler"         # Pipeline identifier
version: "1.0.2"            # Versioning (SemVer recommended)
description: "Transforms portraits into artistic styles"

Why it matters:

• name appears in UI/API
• version enables change tracking
• description helps discovery

2.2 Inputs/Outputs System

Inputs (Pipeline's API)

inputs:
  user_photo:               # Logical name
    type: image             # Data type constraint
    required: true          # Validation rule
    title: "Your Portrait"  # UI label
    default: null           # Fallback value

Outputs (Results Interface)

outputs:
  styled_image:
    type: image
    title: "Artistic Version"
  analysis_report:
    type: json

Key Differences:

Aspect	Inputs	Outputs
Purpose	Data ingestion	Result delivery
Mutability	User-provided	Read-only
Validation	Required/optional	Always generated

2.3 Nodes Architecture

nodes:
  face_detector:                  # Node ID
    category: "image_analysis"    # Functional group
    script: "detect_faces.js"     # Processing logic
    inputs:                       # Required data
      photo: { type: image }
    outputs:                      # Produced artifacts
      faces: { type: json }

Node Types:

• Input processors: First data handlers
• Transformers: Data modifiers
• Terminals: Produce final outputs

2.4 Flow Mechanics

flows:
  detection_to_styling:
    from: face_detector.faces     # Source node.output
    to: style_applier.faces       # Target node.input
    conditions:                   # Optional rules
      - min_faces: 1

Flow Types:

1. Linear: Sequential (A→B→C)
2. Fan-out: One-to-many (A→B, A→C)
3. Conditional: Branched (A→B if X else C)

3. Execution Lifecycle

3.1 Startup Sequence

start:
  nodes:
    - initial_processor    # Entry point
    - parallel_starter      # Concurrent init

3.2 Environment Setup

environment:
  API_ENDPOINT:
    title: "Service URL"
    type: string
    scope: pipeline         # vs 'global' scope

4. Real-world Example

# document_processor.yaml
name: "PDF Analyzer"
inputs:
  pdf_file:
    type: file
    formats: [pdf]
outputs:
  text_content: string
  page_count: number

nodes:
  pdf_extractor:
    script: "pdf.js"
    inputs:
      document: { from: pdf_file }
    outputs:
      raw_text: { to: text_content }
      pages: { to: page_count }

flows:
  file_processing:
    from: input.pdf_file
    to: pdf_extractor.document

What happens when run:

1. User uploads PDF
2. System routes file to pdf_extractor
3. Script processes document
4. Results populate both outputs

5. Design Principles

1. Modularity:

   • Nodes should be single-purpose
   • Example: Separate face_detector and style_applier

2. Discoverability:

   description: "Extracts text from scanned PDFs using OCR"
   tags: ["documents", "text-recognition"]

3. Error Resilience:

   inputs:
     photo:
       constraints:
         min_resolution: [512, 512]

6. Anti-patterns to Avoid

❌ Monolithic Nodes

# Bad: Does too much
nodes:
  mega_processor:
    script: "do_everything.js"

✅ Preferred Approach

# Good: Separated concerns
nodes:
  preprocessor: {...}
  analyzer: {...}
  formatter: {...}

7. Debugging Tips

1. Flow Visualization:

   graph LR
     A[Input] --> B(Processor)
     B --> C[Output]

2. Validation Command:

   pipeline validate my_pipeline.yaml

3. Inspection Points:

   nodes:
     debug_logger:
       script: "log_intermediate.js"

IN WORK