║Arc                             ║
║            ┌────────┐          ║
║┌──────────┐│Workload│          ║
║│ Dataset  │└────────┘          ║
║└──────────┘    _               ║
║  .─────.      ╱ ╲      .─────. ║
║ (|||||||)═══▶▕   ▏═══▶(|||||||)║
║  `─────'      ╲ ╱      `─────' ║
║                ▔               ║

Learn more about provided Components.

If datasets are nodes in a graph, joined by a workload, then an arc is the source dataset, the workload, and the sink dataset.

An arc listens for availability events and executes its declared workload against the newly arrived data or metadata.

Developers can create the code deployed as a workload in an arc, or use native Clusterless components available for a given provider.

Arcs have state. They are running or complete. Or they may have finished in some intermediate state.

Arc state is important for identifying whether a workload completed successfully or only partially completed, or attempted to run but likely failed or was aborted.

Partially completed workloads for a given lot of data should be retried. Depending on the workload type, the partial data should be removed before re-running the process, or the process should use that information to skip performing reduntant work (e.g. on the retry, only copy the files that didn’t copy on the previous attempt).

This is an important quality of the Clusterless framework and allows simpler maintenance and recovery from errors and outages or even preemption of resources (spot priced resources).

See the ADR arc-state-and-data-metadata for more details on arc state.