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A hands-on comparison of how MongoDB and PostgreSQL handle updates to denormalized, duplicated data within DDD aggregates. Using a dataset of 100,000 documents each embedding 1,000 player scores, the author benchmarks a bulk rename operation. MongoDB's updateMany with arrayFilters updates only affected array elements and their index entries (~12 seconds, minimal write amplification). PostgreSQL JSONB requires rebuilding entire documents and regenerating all index entries for any change, producing ~64 WAL records per document. The conclusion: duplication itself isn't the problem—what matters is whether the database's storage model supports fine-grained updates within aggregates. MongoDB fits DDD's aggregate model better for write-heavy denormalized patterns, while PostgreSQL JSONB optimizes reads at the cost of write amplification.

Updating "denormalized" aggregates with "duplicates": MongoDB vs. PostgreSQL

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