Normalized vs. Denormalized Lookup Speed Comparison
Blog post from Harper
The text compares the performance of normalized and denormalized data models, highlighting the trade-offs between them in terms of data retrieval efficiency, storage requirements, and write throughput. Normalized data, which organizes information into separate tables to minimize redundancy, consistently outperforms denormalized data in write throughput, being 12.12 times faster, and is significantly smaller, occupying 13.1 times less space on disk. This structure ensures data integrity and efficient updates, particularly as database size increases beyond available RAM, where normalized data maintains higher throughput and stable response times compared to denormalized data, which suffers from increased disk reads and slower performance. The testing, conducted using Harper's storage engine and a simulated user dataset, demonstrates that while denormalized data offers faster lookup speeds for small datasets, its performance diminishes with larger databases, underscoring the benefits of a "balanced" normalization strategy that retains efficient data storage and update capabilities while allowing for fast data access through in-memory caching.
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