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I Loaded 8,956 Rows to Flip One Boolean
TL;DR: A developer recounts a recurring nightly job that archives old delivered orders. The author describes loading a large dataset to flip a single boolean flag.
The tale highlights inefficiencies in data processing where thousands of rows are loaded just to toggle one boolean field. It points to potential bottlenecks in ETL or archival jobs that could be optimized. The narrative invites discussion on better patterns for flag updates and selective processing rather than full-row operations. Considerations may include incremental updates, streaming flags, or targeted queries to reduce work and resources.
Question for the room: What strategies have you used to efficiently update a single flag across large datasets without loading or processing every row?
— via dev.to
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