Data Flow Pipelines
Relevant source files
The following files were used as context for generating this wiki page:
Introduction
Data Flow Pipelines in the Dev-Template project represent the core architectural framework for sequential and parallel data processing. These pipelines are designed to ingest raw data, apply a series of modular transformations, and output processed information to various sinks. The primary goal is to provide a scalable and reusable structure for handling complex data lifecycles while maintaining strict type safety and error boundaries.
The pipeline system decouples data ingestion logic from processing logic, allowing developers to swap components without altering the overall flow. This modularity is achieved through a standardized interface that all pipeline components must implement, ensuring interoperability across different stages of the data journey.
Sources: src/pipeline/base.py, src/pipeline/engine.py
Pipeline Architecture
The architecture follows a modular "Plug-and-Play" design where individual processing units, known as Processors, are orchestrated by a central PipelineEngine.
Core Components
| Component | Description |
|---|---|
DataLoader | Responsible for fetching data from external or internal sources (APIs, Databases, Files). |
DataProcessor | Contains the business logic for transforming or filtering data. |
PipelineEngine | The orchestrator that manages the execution order and state of the pipeline. |
DataSink | The final destination for processed data, such as a database or an export file. |
Sources: src/pipeline/base.py:10-45, src/io/loader.py:5-20
Execution Flow
The PipelineEngine initializes the sequence by invoking the DataLoader. Once the data is buffered, it passes through a list of registered DataProcessor instances. Each processor returns a modified state which is then passed to the next unit in the chain.
The diagram above illustrates the standard synchronous flow of data from ingestion to final storage.
Sources: src/pipeline/engine.py:50-85
Component Implementation
Data Loaders
Loaders are responsible for the initial state of the pipeline. They must implement an asynchronous fetch method to prevent blocking during I/O operations.
class BaseLoader:
async def load(self) -> List[Dict]:
"""Abstract method to load data."""
pass
Sources: src/io/loader.py:12-18
Transformation Logic
Processors are strictly functional; they take an input state and return a new state without modifying the original input (immutability). This ensures that the pipeline can be retried from any point in case of failure.
The sequence diagram demonstrates the interaction between the engine and individual transformers during a processing cycle.
Sources: src/processors/transformer.py:22-40, src/pipeline/engine.py:102
Configuration and Constraints
Pipelines are configured via YAML or JSON definitions, allowing for dynamic adjustment of processing steps without code changes.
| Parameter | Type | Default | Description |
|---|---|---|---|
batch_size | Integer | 100 | Number of records processed in a single chunk. |
retry_limit | Integer | 3 | Maximum attempts for a failed processor stage. |
timeout | Float | 30.0 | Seconds before a stage is considered timed out. |
Sources: src/pipeline/base.py:55-70
Conclusion
The Data Flow Pipeline system provides a robust and extensible foundation for data-centric operations within the Dev-Template project. By enforcing a clear separation of concerns between loading, processing, and storing data, it facilitates high maintainability and testability. The use of an engine-driven approach allows for future enhancements such as distributed processing and advanced telemetry.