A scheduled sync job ran every hour on a small free server, and its logs claimed success after every single run. The target database, however, was quietly missing records that the upstream API clearly contained, which made the logs look like a deliberate lie. This article walks through that failure from the first symptom to the actual root cause, and it highlights three debugging techniques that matter more than any single fix.
The job was deliberately simple in its design. A Python script pulled new records from an upstream endpoint, inserted them into a local SQLite database, and wrote a summary line to stdout for the log. Cron invoked the script through a pipeline that appended the output to a log file, and the whole thing lived on a free server with a strict time budget and a process manager that could reclaim the job at any moment.
The first sign of trouble appeared when a comparison query showed a gap between the upstream record count and the local one. The log file ended with "sync completed" on every run, so the initial assumption was a data mismatch rather than a crash in the job itself. The operator asked an AI model, accessed through MonkeyCode's free model access, to review the script. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The model suggested three plausible improvements: add a retry loop with exponential backoff, print more diagnostic lines, and wrap per-record processing in a try/except so one bad record could not stop the batch.
