Pipeline filter is a classic software architecture pattern and data processing paradigm, which has a wide and powerful role.
core function
1. Data flow and processing
Function: Build a data pipeline that allows data to pass through various processing stages like on an assembly line in a factory.
Example:
Compiler: Source code → Lexical analysis → Syntax analysis → Semantic analysis → Code generation
ETL process: raw data → cleaning → validation → transformation → loading into database
2. Functional decoupling
Function: Decompose complex systems into small modules (filters) with a single responsibility, where each module only focuses on its own processing logic and does not care about the implementation of other modules.
Benefits:
Easy to develop: Different teams can develop different filters in parallel
Easy to test: Each filter can be independently unit tested
Easy to maintain: Modifying a filter will not affect other components
3. Flexible combination and reuse
Function: Build different processing flows using the same set of basic filters through different arrangements and combinations.
4. Incremental processing
Function: There is no need to wait for all data to be ready, data can flow through pipelines and be produced and consumed simultaneously.