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.