Distributed and decoupled pipelines: Remote worker registration in wpipe
Distributed and Decoupled Pipelines: Remote Worker Registration in wpipe Building data pipelines that scale across multiple servers or ephemeral containers often results in fragile and tightly-coupled architectures. When flows run in isolation on edge nodes or batch workers, coordinating and monitoring their lifecycle in a centralized manner requires a robust registration protocol…
Distributing and decoupled pipelines: Remote worker registration in wpipe
Building data pipelines that scale across multiple servers or ephemeral containers often results in fragile and tightly coupled architectures. When flows run isolated on edge nodes or batch workers, coordinating and monitoring their life cycle requires a robust remote registration protocol with local connection tolerance. wpipe integrates distributed coordination directly into the pipeline engine through the api_config mechanism.
The api_config dictionary specifies the base URL (http://orquestador-central.local:8418) and worker authentication token (token_secreto_worker_auth). The Pipeline class is instantiated with a worker_name (nodo_feature_eng_01), api_config, verbose mode, and an empty list of steps.
Pipeline.set_steps is called with a tuple containing the procesar_lote function (which doubles the value and marks it as processed), a descriptive name, and a version number (v1.0). Remote registration is attempted using pipeline.worker_register, passing the same worker_name and version.
If a registered worker is returned, its ID is set in the pipeline, and run is called with a dictionary containing the value 42. The result is then printed. In the event of an exception (e.g., if the remote orquestador is unavailable), the pipeline falls back to local execution via pipeline.run, printing the local result.
Wpipe's engineering advantages include centralized telemetry and local autonomy: pipelines report states, traces, and metrics when online, but continue running locally seamlessly when the network connection drops. Forensic checkpoints using SQLite WAL ensure absolute resilience against interruptions, automatically resuming exactly at the last processed step without recalculating prior steps.
Zero Global Interpreter Lock (GIL) locks enable native switching between multiprocessing, multithreading, and asyncio with a single interface. Explore the full open-source suite on GitHub.
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