Pipeline Composition Framework Specification
Overview
The Pipeline Composition Framework enables the creation of complex, reusable pipelines through modular composition, inheritance, and dynamic assembly. This framework provides the architectural foundation for building pipelines from smaller, tested components while maintaining flexibility and extensibility.
Core Concepts
1. Composition Principles
Modularity
Every pipeline component is self-contained with well-defined inputs, outputs, and behavior. Components can be composed without knowledge of internal implementation.
Reusability
Components are designed for reuse across different pipelines and contexts. Generic components adapt to specific use cases through configuration.
Composability
Components combine naturally through standard interfaces. Complex behaviors emerge from simple component combinations.
Extensibility
New components can be added without modifying existing ones. The framework supports custom component types and behaviors.
Component Architecture
1. Base Component Structure
component:
name: component_identifier
version: "1.0.0"
type: component_type
description: "Clear description of component purpose"
metadata:
author: "component_author"
tags: ["category", "use-case"]
license: "MIT"
stability: "stable|beta|experimental"
interface:
inputs:
- name: input_name
type: data_type
required: boolean
description: "Input purpose"
validation:
schema: json_schema
outputs:
- name: output_name
type: data_type
description: "Output description"
schema: json_schema
parameters:
- name: param_name
type: data_type
default: default_value
description: "Parameter purpose"
constraints:
- constraint_definition
implementation:
type: "inline|reference|template"
content: implementation_details
requirements:
dependencies:
- dependency_spec
resources:
memory: "size"
cpu: "cores"
gpu: boolean
providers:
- provider_type
2. Component Types
Atomic Components
Indivisible units of functionality that perform single, well-defined tasks.
component:
name: text_summarizer
type: atomic
interface:
inputs:
- name: text
type: string
required: true
outputs:
- name: summary
type: string
parameters:
- name: max_length
type: integer
default: 100
implementation:
type: inline
provider: openai
prompt: |
Summarize the following text in {{ max_length }} words:
{{ text }}
Composite Components
Components built from other components, providing higher-level functionality.
component:
name: document_processor
type: composite
components:
- name: extractor
component: text_extractor
version: "1.0.0"
- name: summarizer
component: text_summarizer
version: "1.0.0"
- name: translator
component: text_translator
version: "1.0.0"
flow:
- step: extract_text
component: extractor
inputs:
document: "{{ inputs.document }}"
outputs:
- extracted_text
- step: summarize
component: summarizer
inputs:
text: "{{ extracted_text }}"
parameters:
max_length: "{{ parameters.summary_length }}"
outputs:
- summary
- step: translate
component: translator
when: "{{ parameters.target_language != 'en' }}"
inputs:
text: "{{ summary }}"
target_language: "{{ parameters.target_language }}"
outputs:
- translated_summary
interface:
inputs:
- name: document
type: file
required: true
outputs:
- name: summary
type: string
value: "{{ translated_summary | default(summary) }}"
parameters:
- name: summary_length
type: integer
default: 100
- name: target_language
type: string
default: "en"
Template Components
Parameterized components that generate other components based on configuration.
component:
name: api_client_template
type: template
template_parameters:
- name: api_name
type: string
required: true
- name: base_url
type: string
required: true
- name: auth_type
type: string
enum: ["api_key", "oauth2", "basic"]
- name: endpoints
type: array
items:
type: object
properties:
name: string
method: string
path: string
generates:
component:
name: "{{ api_name }}_client"
type: composite
components:
- name: auth_handler
component: "{{ auth_type }}_authenticator"
version: "1.0.0"
{{ #each endpoints }}
- name: "{{ name }}_caller"
component: http_request
version: "1.0.0"
{{ /each }}
flow:
- step: authenticate
component: auth_handler
inputs:
credentials: "{{ inputs.credentials }}"
outputs:
- auth_token
{{ #each endpoints }}
- step: "call_{{ name }}"
component: "{{ name }}_caller"
when: "{{ inputs.operation == '{{ name }}' }}"
inputs:
url: "{{ base_url }}{{ path }}"
method: "{{ method }}"
headers:
Authorization: "Bearer {{ auth_token }}"
body: "{{ inputs.request_body }}"
outputs:
- "{{ name }}_response"
{{ /each }}
3. Composition Patterns
Sequential Composition
Components execute in order, with outputs flowing to subsequent inputs.
pattern: sequential
components:
- data_fetcher
- data_validator
- data_transformer
- data_loader
flow:
type: sequential
error_handling: stop_on_error
data_passing: automatic
Parallel Composition
Components execute simultaneously for performance optimization.
pattern: parallel
components:
- user_data_fetcher
- product_data_fetcher
- inventory_checker
- pricing_calculator
flow:
type: parallel
merge_strategy: combine_outputs
timeout: 30s
partial_results: allowed
Conditional Composition
Components execute based on runtime conditions.
pattern: conditional
components:
- condition_evaluator
- path_a_processor
- path_b_processor
- result_merger
flow:
type: conditional
decision_points:
- after: condition_evaluator
paths:
- condition: "{{ result.score > 0.8 }}"
component: path_a_processor
- condition: "{{ result.score <= 0.8 }}"
component: path_b_processor
Loop Composition
Components execute repeatedly until conditions are met.
pattern: loop
components:
- data_fetcher
- data_processor
- completion_checker
flow:
type: loop
max_iterations: 10
continue_condition: "{{ not completion_checker.is_complete }}"
accumulate_results: true
Map-Reduce Composition
Process collections through parallel mapping and result reduction.
pattern: map_reduce
components:
- item_processor
- result_aggregator
flow:
type: map_reduce
map:
component: item_processor
parallelism: 10
batch_size: 100
reduce:
component: result_aggregator
strategy: incremental
Pipeline Inheritance
1. Base Pipeline Definition
pipeline:
name: base_analysis_pipeline
version: "1.0.0"
abstract: true
parameters:
- name: analysis_depth
type: string
enum: ["shallow", "standard", "deep"]
default: "standard"
components:
- name: data_collector
component: generic_collector
abstract: true
- name: analyzer
component: generic_analyzer
abstract: true
- name: reporter
component: generic_reporter
version: "1.0.0"
flow:
- collect_data:
component: data_collector
- analyze:
component: analyzer
inputs:
data: "{{ collect_data.output }}"
- report:
component: reporter
inputs:
analysis: "{{ analyze.output }}"
2. Derived Pipeline
pipeline:
name: security_analysis_pipeline
version: "1.0.0"
extends: base_analysis_pipeline
parameters:
- name: severity_threshold
type: string
default: "medium"
# Inherits analysis_depth from base
components:
- name: data_collector
component: security_scanner
version: "2.0.0"
override: true
- name: analyzer
component: vulnerability_analyzer
version: "1.5.0"
override: true
- name: threat_modeler
component: threat_model_generator
version: "1.0.0"
# New component not in base
flow:
# Inherits collect_data and analyze steps
- threat_model:
component: threat_modeler
inputs:
vulnerabilities: "{{ analyze.output }}"
after: analyze
# Inherits report step with modified input
- report:
inputs:
analysis: "{{ analyze.output }}"
threat_model: "{{ threat_model.output }}"
Dynamic Pipeline Assembly
1. Runtime Composition
assembly:
name: dynamic_pipeline_builder
type: runtime
selection_rules:
- condition: "{{ context.data_type == 'structured' }}"
components:
processor: structured_data_processor
validator: schema_validator
- condition: "{{ context.data_type == 'unstructured' }}"
components:
processor: nlp_processor
validator: content_validator
assembly_strategy:
type: rule_based
fallback: default_pipeline
optimization: performance
runtime_parameters:
- name: context
type: object
required: true
- name: requirements
type: object
required: true
2. Adaptive Composition
adaptive_pipeline:
name: self_optimizing_pipeline
type: adaptive
performance_metrics:
- execution_time
- resource_usage
- output_quality
- error_rate
adaptation_strategies:
- name: component_replacement
trigger:
metric: execution_time
threshold: "150% of baseline"
action:
type: replace_component
selection_criteria: faster_alternative
- name: parallelization
trigger:
metric: queue_length
threshold: 100
action:
type: increase_parallelism
max_workers: 10
- name: quality_adjustment
trigger:
metric: error_rate
threshold: 0.05
action:
type: adjust_parameters
target: quality_settings
direction: increase
Component Registry
1. Registry Structure
registry:
name: pipeline_component_registry
version: "2.0.0"
categories:
- name: data_processing
subcategories:
- extraction
- transformation
- validation
- loading
- name: ai_ml
subcategories:
- nlp
- computer_vision
- predictive
- generative
- name: integration
subcategories:
- apis
- databases
- messaging
- files
component_metadata:
required_fields:
- name
- version
- type
- category
- interface
- description
optional_fields:
- examples
- benchmarks
- compatibility
- deprecation
versioning:
scheme: semantic
compatibility_rules:
major: breaking_changes
minor: new_features
patch: bug_fixes
2. Component Discovery
discovery:
search_capabilities:
- name: keyword_search
fields: ["name", "description", "tags"]
ranking: relevance
- name: interface_matching
criteria:
- input_compatibility
- output_compatibility
- parameter_alignment
- name: capability_search
taxonomy: capability_tree
similarity: semantic
recommendation_engine:
algorithms:
- collaborative_filtering
- content_based
- hybrid_approach
factors:
- usage_patterns
- performance_metrics
- user_ratings
- compatibility_score
Composition Validation
1. Static Validation
validation:
static_checks:
- name: interface_compatibility
rules:
- output_to_input_matching
- type_compatibility
- required_field_presence
- cardinality_matching
- name: resource_analysis
checks:
- total_resource_requirements
- resource_conflicts
- scaling_limitations
- name: dependency_resolution
checks:
- circular_dependencies
- version_conflicts
- missing_dependencies
- provider_availability
2. Runtime Validation
validation:
runtime_checks:
- name: data_flow_validation
monitors:
- data_schema_compliance
- data_volume_thresholds
- data_quality_metrics
- name: performance_validation
monitors:
- execution_time_limits
- memory_usage_bounds
- throughput_requirements
- name: error_rate_monitoring
thresholds:
- component_error_rate: 0.01
- pipeline_error_rate: 0.001
- recovery_time: 60s
Advanced Features
1. Component Versioning and Migration
versioning:
strategy: semantic_versioning
migration_support:
- name: automated_migration
conditions:
- minor_version_change
- backward_compatible
actions:
- update_references
- validate_compatibility
- test_migration
- name: guided_migration
conditions:
- major_version_change
- breaking_changes
actions:
- generate_migration_guide
- identify_affected_pipelines
- provide_code_modifications
2. Component Optimization
optimization:
strategies:
- name: performance_profiling
metrics:
- execution_time
- memory_usage
- cpu_utilization
actions:
- identify_bottlenecks
- suggest_alternatives
- auto_tune_parameters
- name: cost_optimization
factors:
- resource_usage
- api_calls
- data_transfer
actions:
- recommend_efficient_components
- batch_operation_suggestions
- caching_opportunities
3. Composition Analytics
analytics:
usage_tracking:
- component_popularity
- composition_patterns
- failure_patterns
- performance_trends
insights_generation:
- common_component_combinations
- anti_patterns_detection
- optimization_opportunities
- upgrade_recommendations
reporting:
- pipeline_health_scores
- component_reliability_metrics
- composition_complexity_analysis
- cost_benefit_analysis
Implementation Guidelines
1. Component Development
development_guidelines:
design_principles:
- single_responsibility
- explicit_interfaces
- minimal_dependencies
- comprehensive_documentation
testing_requirements:
- unit_tests: 90% coverage
- integration_tests: required
- performance_tests: baseline_established
- example_usage: provided
documentation_standards:
- interface_specification
- usage_examples
- performance_characteristics
- troubleshooting_guide
2. Pipeline Composition Best Practices
best_practices:
composition_patterns:
- prefer_simple_over_complex
- use_proven_components
- implement_error_handling
- monitor_performance
anti_patterns:
- avoid_deep_nesting
- prevent_circular_dependencies
- minimize_state_sharing
- reduce_coupling
optimization_tips:
- leverage_parallelism
- implement_caching
- batch_operations
- progressive_processing
Future Enhancements
1. AI-Assisted Composition
- Automatic pipeline generation from requirements
- Intelligent component recommendation
- Performance prediction
- Anomaly detection in compositions
2. Visual Composition Tools
- Drag-and-drop pipeline builder
- Real-time validation feedback
- Visual debugging capabilities
- Performance visualization
3. Distributed Composition
- Cross-region component execution
- Federated component registries
- Edge computing support
- Hybrid cloud compositions