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🌬️ Apache Airflow and the DAG Model of Pipeline Orchestration

A 3D directed acyclic graph of an Airflow-style pipeline where tasks queue, run, retry and succeed under a worker-pool limit, with optional historical backfill runs.

AI & Machine Learning3DAdvanced60 FPS

A pipeline of eight tasks is laid out as a directed acyclic graph. Watch tasks move from pending to queued to running to success (or retry and fail) exactly as Airflow's scheduler would sequence them, gated by dependencies and a shared worker pool.

🔬 What It Demonstrates

A task only queues once every upstream task has succeeded; a light pulse fires down each outgoing edge on success, unlocking the next task. Failed tasks retry automatically before failing the branch, and a limited worker pool forces queued tasks to wait their turn.

🎮 How to Use

Adjust run speed, the task failure rate, and the worker pool size. Toggle backfill to launch several DAG runs at once and watch them compete for the same pool of workers, the way Airflow replays missed historical schedule intervals.

💡 Did You Know?

Because Airflow encodes dependencies as an explicit graph instead of a sequence of cron jobs, it can retry only the failed branch, run independent branches in parallel, and backfill months of missed runs — none of which a plain cron chain can do safely.

⚙ Under the hood

A 3D directed acyclic graph of an Airflow-style pipeline where tasks queue, run, retry and succeed under a worker-pool limit, with optional historical backfill runs.

pipelinedagairfloworchestrationautomationworkflowThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install