graph LR
Pipeline_Orchestrator_Supervisor["Pipeline Orchestrator / Supervisor"]
Stage_Processors["Stage Processors"]
Worker_Executors["Worker Executors"]
Inter_Stage_Buffers_Queues["Inter-Stage Buffers/Queues"]
Input_Output_Adapters["Input/Output Adapters"]
Pipeline_Orchestrator_Supervisor -- "initializes and orchestrates" --> Stage_Processors
Stage_Processors -- "performs transformations under direction of" --> Pipeline_Orchestrator_Supervisor
Pipeline_Orchestrator_Supervisor -- "spawns and manages" --> Worker_Executors
Worker_Executors -- "execute tasks for" --> Pipeline_Orchestrator_Supervisor
Pipeline_Orchestrator_Supervisor -- "creates and manages" --> Inter_Stage_Buffers_Queues
Inter_Stage_Buffers_Queues -- "facilitate data flow for" --> Pipeline_Orchestrator_Supervisor
Pipeline_Orchestrator_Supervisor -- "initiates data ingestion via" --> Input_Output_Adapters
Input_Output_Adapters -- "provide/consume data for" --> Pipeline_Orchestrator_Supervisor
Stage_Processors -- "utilize" --> Worker_Executors
Worker_Executors -- "process data for" --> Stage_Processors
Stage_Processors -- "exchange data through" --> Inter_Stage_Buffers_Queues
Inter_Stage_Buffers_Queues -- "buffer data between" --> Stage_Processors
Input_Output_Adapters -- "interact with" --> Inter_Stage_Buffers_Queues
Inter_Stage_Buffers_Queues -- "receive/send data from/to" --> Input_Output_Adapters
click Pipeline_Orchestrator_Supervisor href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/pypeln/Pipeline_Orchestrator_Supervisor.md" "Details"
The pypeln.process subsystem is designed around a robust pipeline architecture, centrally managed by the Pipeline Orchestrator / Supervisor. This orchestrator is responsible for setting up and overseeing the entire data processing flow. It dynamically spawns and manages Worker Executors which are the actual units of parallel computation. Data flows between different processing steps, represented by Stage Processors, through Inter-Stage Buffers/Queues, ensuring efficient and asynchronous communication. Input/Output Adapters handle the ingestion of initial data into the pipeline and the emission of final processed results. The supervisor integrates functionalities for error handling, resource management, and configuration, ensuring a cohesive and resilient pipeline execution. This design promotes modularity, allowing users to define custom processing stages while the framework handles the complexities of parallel execution and data synchronization.
Pipeline Orchestrator / Supervisor [Expand]
This is the central control unit that oversees the entire pipeline's execution. It initiates all stages, manages their lifecycle, coordinates the start and shutdown of workers and queues, and handles overall resource allocation and error propagation.
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Abstract or concrete components representing the individual transformation steps within the pipeline. Users implement these to define their data processing logic.
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Components responsible for running the actual processing tasks within each stage, leveraging Python's multiprocessing capabilities to achieve parallelism.
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Data structures (e.g., multiprocessing.Queue) that facilitate asynchronous and synchronized data transfer between pipeline stages, acting as producer-consumer buffers.
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Components responsible for ingesting raw data into the pipeline and emitting processed data from the pipeline.
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