Building Scalable Pipelines with pypdg

Building Scalable Pipelines with pypdg

Overview

pypdg is a Python library for building directed acyclic graph (DAG)-based pipelines that orchestrate data processing tasks. This guide shows how to design, implement, and scale robust pipelines using pypdg, covering architecture patterns, parallelism, error-handling, monitoring, and deployment strategies.

1. Design principles

• Modularity: Break work into small, reusable tasks (nodes).
• Idempotence: Ensure tasks can be retried without side effects.
• Explicit dependencies: Define clear upstream/downstream relationships.
• Data locality: Keep heavy data movement minimal; prefer processing near storage.
• Observability: Emit metrics and logs per task.

2. Core components

• Nodes: Functions or callables representing units of work.
• Edges/Dependencies: Directed links defining execution order.
• Scheduler/Executor: Runs nodes respecting dependencies and parallelism limits.
• Storage/IO connectors: Read/write data to object stores, databases, or message queues.

3. Example pipeline structure

• Ingest: read raw data from storage.
• Transform: clean, validate, and enrich data.
• Aggregate: compute summaries or features.
• Persist: write results to database or analytics store.
• Notify: emit completion events or alerts.

4. Implementation patterns

• Task encapsulation: Wrap logic in small functions with clear inputs/outputs.
• Config-driven pipelines: Separate config (schedules, resource limits, connectors) from code.
• Parameterization: Use templ