How a SQLite Job Queue Prevents Data Loss in Large-Scale LLM Batch Processing
A developer writing on DEV Community describes a method for running large language model batch jobs reliably on a free server without losing progress if the process crashes mid-run. The approach uses SQLite as a lightweight job queue, replacing fragile for-loops or cron scripts that have no memory of completed work. Each job is assigned a unique idempotency key derived from a hash of its payload, so re-running the pipeline after a crash skips already-processed records automatically. A single SQL UPDATE statement atomically claims and locks batches of jobs, preventing duplicate processing even if multiple workers run simultaneously. The article uses a 10,000-line log-normalization task — converting unstructured error logs into structured JSON — as a practical demonstration of the pattern.
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