Principle:Datajuicer Data juicer Pipeline Monitoring and Checkpointing
| Knowledge Sources | |
|---|---|
| Domains | Distributed_Computing, Fault_Tolerance, Observability |
| Last Updated | 2026-02-14 17:00 GMT |
Overview
A fault tolerance and observability pattern that combines structured event logging with checkpoint-based resumable processing for long-running pipelines.
Description
Pipeline Monitoring and Checkpointing addresses two critical concerns in long-running data processing: observability (knowing what is happening) and fault tolerance (recovering from failures). The event logging component records structured events (job start/complete, operator start/complete, checkpoint save/load, errors) with timestamps, durations, and metrics. The checkpoint component saves dataset state after each operator, enabling pipelines to resume from the last successful step after a failure instead of restarting from scratch.
Usage
Use this principle in any long-running pipeline, especially distributed Ray pipelines. Event logging is automatically enabled by the executor. Checkpointing is enabled when a work directory is configured.
Theoretical Basis
# Abstract algorithm (NOT real implementation)
# Event logging pattern
class EventLogger:
def log_event(event_type, message, **metadata):
event = Event(
type=event_type,
timestamp=now(),
message=message,
**metadata
)
write_to_log(event)
notify_observers(event)
# Checkpoint pattern
class Checkpointer:
def save(dataset, op_index):
serialize(dataset, checkpoint_dir / 'latest')
record_op(op_index, checkpoint_dir / 'op_record.json')
def get_resume_point():
if checkpoint_exists and config_unchanged:
return last_completed_op_index
return 0 # Start from beginning