Principle:Mbzuai oryx Awesome LLM Post training Progressive Data Checkpointing
| Knowledge Sources | |
|---|---|
| Domains | Data_Collection, Fault_Tolerance |
| Last Updated | 2026-02-08 07:30 GMT |
Overview
A fault-tolerance pattern that periodically saves intermediate collection results to disk during long-running data gathering operations.
Description
Progressive Data Checkpointing is the practice of writing accumulated results to a temporary file at regular intervals during a data collection process. If the process crashes, is interrupted, or encounters an unrecoverable error, the checkpoint file preserves all data collected up to the most recent save point, eliminating the need to restart the entire collection from scratch.
This pattern is essential for any long-running data pipeline that operates over external APIs, where network failures, rate-limit exhaustion, or process termination can occur at any time. Without checkpointing, hours of collected data may be lost.
Usage
Use this principle in any data collection pipeline where:
- Collection runs for extended periods (minutes to hours)
- Data is gathered incrementally from external sources
- The cost of re-collecting lost data is significant
- Network or API failures are expected
The checkpoint frequency should balance between I/O overhead and acceptable data loss window.
Theoretical Basis
Pseudo-code Logic:
# Abstract checkpointing pattern (NOT real implementation)
data = []
for item in collection_source:
result = fetch(item)
data.append(result)
if len(data) % CHECKPOINT_INTERVAL == 0:
save_to_disk(data, "checkpoint_file")
# Final save after loop completes
save_to_disk(data, "final_output")
Key design parameters:
- Checkpoint interval: How often to save (every N records)
- Checkpoint format: Serialization format (JSON, pickle, etc.)
- Atomicity: Whether writes are atomic (write-then-rename) or in-place