Principle:Mbzuai oryx Awesome LLM Post training Progressive Json Saving
| Knowledge Sources | |
|---|---|
| Domains | Trend_Analysis, Fault_Tolerance |
| Last Updated | 2026-02-08 07:30 GMT |
Overview
A progressive persistence pattern that overwrites a structured JSON results file after each keyword is fully processed in a trend analysis pipeline.
Description
Progressive Json Saving differs from the checkpoint pattern used in deep collection in a key way: rather than saving periodically based on a count threshold, it saves after every logical unit of work (each keyword's full year range has been queried). The entire results dictionary is written each time, so the file always represents a consistent, complete snapshot of all keywords processed so far.
This is appropriate for trend analysis because each keyword requires multiple API calls (one per year), and losing the results for even one keyword means re-querying the API for all its years. The per-keyword save granularity balances I/O cost against data loss risk.
Usage
Use this principle in iterative processing pipelines where each iteration completes a logically coherent unit of work and the cost of repeating an iteration is non-trivial.
Theoretical Basis
Pseudo-code Logic:
# Abstract progressive save pattern (NOT real implementation)
results = {}
for keyword in keywords:
results[keyword] = process_keyword(keyword)
# Save after each keyword completes
save_json(results, "output.json") # Full overwrite
Key difference from periodic checkpointing:
- Periodic: Save every N items regardless of logical boundaries
- Progressive: Save at logical completion points (each keyword fully processed)