Architecture Paradigm Pipeline

name: architecture-paradigm-pipeline description: |

Triggers: pipeline, streaming, data-processing, architecture, pipes-filters Compose processing stages using a pipes-and-filters model for ETL, media processing, or compiler-like workloads.

Triggers: pipeline architecture, pipes and filters, ETL, data transformation, stream processing, CI/CD pipeline, media processing, batch processing

Use when: data flows through fixed sequence of transformations, stages can be independently developed and tested, parallel processing of stages is beneficial

DO NOT use when: selecting from multiple paradigms - use architecture-paradigms first. DO NOT use when: data flow isn't sequential or predictable. DO NOT use when: complex branching/merging logic dominates.

Consult this skill when designing data pipelines or transformation workflows. version: 1.3.5 category: architectural-pattern tags: [architecture, pipeline, pipes-filters, ETL, streaming, data-processing] dependencies: [] tools: [stream-processor, message-queue, data-validator] usage_patterns: - paradigm-implementation - data-transformation - workflow-automation complexity: medium estimated_tokens: 700

The Pipeline (Pipes and Filters) Paradigm

When to Employ This Paradigm

• When data must flow through a fixed sequence of discrete transformations, such as in ETL jobs, streaming analytics, or CI/CD pipelines.
• When reusing individual processing stages is needed, either independently or to scale bottleneck stages separately from others.
• When failure isolation between stages is a critical requirement.

Adoption Steps

1. Define Filters: Design each stage (filter) to perform a single, well-defined transformation. Each filter must have a clear input and output data schema.
2. Connect via Pipes: Connect the filters using "pipes," which can be implemented as streams, message queues, or in-memory channels. validate these pipes support back-pressure and buffering.
3. Maintain Stateless Filters: Where possible, design filters to be stateless. Any required state should be persisted externally or managed at the boundaries of the pipeline.
4. Instrument Each Stage: Implement monitoring for each filter to track key metrics such as latency, throughput, and error rates.
5. Orchestrate Deployments: Design the deployment strategy to allow each stage to be scaled horizontally and upgraded independently.

Key Deliverables

• An Architecture Decision Record (ADR) documenting the filters, the chosen pipe technology, the error-handling strategy, and the tools for replaying data.
• A suite of contract tests for each filter, plus integration tests that cover representative end-to-end pipeline executions.
• Observability dashboards that visualize stage-level Key Performance Indicators (KPIs).

Risks & Mitigations

• Single-Stage Bottlenecks:
  • Mitigation: Implement auto-scaling for individual filters. If a single filter remains a bottleneck, consider refactoring it into a more granular sub-pipeline.
• Schema Drift Between Stages:
  • Mitigation: Centralize schema definitions in a shared repository and enforce compatibility tests as part of the CI/CD process to prevent breaking changes.
• Back-Pressure Failures:
  • Mitigation: Conduct rigorous load testing to simulate high-volume scenarios. Validate that buffering, retry logic, and back-pressure mechanisms behave as expected under stress.

Troubleshooting

Common Issues

Command not found Ensure all dependencies are installed and in PATH

Permission errors Check file permissions and run with appropriate privileges

Unexpected behavior Enable verbose logging with --verbose flag