Context and Problem
Applications that handle large volumes of data require efficient, modular, and scalable processing techniques.
- Monolithic processing logic making scaling difficult.
- Lack of flexibility in modifying or adding processing steps.
- Inefficient processing due to tightly coupled components.
- Difficulty in parallelizing workload execution.
Solution
The Pipes and Filters pattern divides processing into independent, reusable components connected by a data flow pipeline.
- Design processing units (filters) that perform specific transformations.
- Connect filters using a pipeline (pipes) to pass data between them.
- Ensure each filter processes data independently and asynchronously.
- Allow parallel execution where possible for scalability.
- Monitor and log pipeline performance for optimization.
Benefits
- Modularity
- Components can be modified, replaced, or added without affecting others.
- Scalability
- Enables distributed processing across multiple instances.
- Maintainability
- Easier to troubleshoot and update specific parts of the pipeline.
- Flexibility
- Supports dynamic reordering or insertion of new processing steps.
Trade-offs
- Increased complexity
- Requires careful orchestration of pipeline components.
- Potential latency
- Data passes through multiple stages before final processing.
- Overhead
- Each filter introduces an additional processing step.
Issues and Considerations
- Error handling
- Managing failures and retries across multiple processing steps.
- Performance bottlenecks
- Identifying and optimizing slow filters in the pipeline.
- Data integrity
- Ensuring correct data transformations across filters.
When to Use This Pattern
- When processing large volumes of data with multiple transformation steps.
- When needing modular and flexible processing architectures.
- When enabling parallel and distributed data processing.