architecture-paradigm-pipeline git:20260921.ad5ceb4A

Immutable. This exact content never changes and is served at /api/v1/blob/c948bc6c2936d6ac.

---
name: architecture-paradigm-pipeline
description: Applies pipes-and-filters for sequential data transformations. Use when data flows through discrete stages like ETL, streaming analytics, or CI/CD pipelines.
alwaysApply: false
category: architectural-pattern
tags:
- architecture
- pipeline
- pipes-filters
- ETL
- streaming
- data-processing
dependencies: []
tools: []
usage_patterns:
- paradigm-implementation
- data-transformation
- workflow-automation
complexity: medium
model_hint: standard
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.

## When NOT To Use

- Interactive request/response systems (use
  `archetypes:architecture-paradigm-client-server`)
- Stages that must share mutable state, which the pattern cannot express

## 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.

## Concrete Components

Vocabulary for the tools and abstractions an implementation of this
paradigm tends to carry. Not dependencies, and not ``tools:`` frontmatter.

- ``stream-processor``: the runtime that executes a filter (e.g. Flink, Apache Beam, Faust)
- ``message-queue``: the durable pipe between filters (e.g. Kafka, RabbitMQ, in-memory channel)
- ``data-validator``: schema-checks every record at filter input and output

## Exit Criteria

- [ ] An ADR documents every filter in the pipeline, the chosen pipe technology, the
  error-handling strategy (DLQ, retry count, dead-letter routing), and the data replay
  mechanism.
- [ ] Each filter has a contract test covering its input and output schema; schema drift between
  adjacent filters is caught by a CI compatibility check.
- [ ] Observability dashboards are configured showing per-stage latency, throughput, and error
  rate before the pipeline is promoted to production.
- [ ] Load testing validates that back-pressure and buffering mechanisms prevent data loss at
  2x the expected peak throughput.