checkpoint-workflow-builder

Resumable workflows use checkpoints to save progress at each state transition, enabling automatic recovery from the last successful checkpoint when the process restarts. This approach preserves state and prevents re-processing completed phases in long-running tasks like ETL pipelines, migrations, and multi-phase deployments.

Checkpoints save the complete workflow state at defined recovery points, allowing resumption without losing prior progress or re-running completed steps. Traditional error handling alone doesn't provide this state preservation; checkpoints enable deterministic recovery and clear visibility into where a workflow stopped and can restart.

Yes. Checkpoint-based workflows integrate naturally with saga patterns in distributed systems by marking saga steps as checkpoints, enabling compensation logic and manual intervention at failure points while maintaining clear progress visibility across service boundaries.

Place checkpoints between pipeline phases—such as download, process, and finalize—so partial failures isolate to a single phase. Manual intervention becomes possible at known recovery points, and the pipeline resumes only the failed phase rather than restarting from the beginning.

State-machine workflows with defined states (INIT, DOWNLOAD, PROCESS, FINALIZE) map directly to checkpoints. Each state transition becomes a recovery point, giving workflows explicit progress visibility and clear decision points for resuming, retrying, or manual intervention after failures.

Checkpoint workflows suit long-running tasks where re-executing from the start is expensive or impossible—migrations that alter data, deployments with side effects, or ETL jobs processing large datasets. Retries alone waste resources and don't preserve partial progress.