A Performance Comparison of NoSQL and SQL Databases for Different Scales of Ecommerce Systems

Shen, Wenbin
Clear, Tony
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Master of Computer and Information Sciences
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Auckland University of Technology

Customers have changed their shopping behaviour from shopping in physical stores to shopping on virtual online platforms over the last decades especially since covid-19 lockdowns. Correspondently, this change in shopping behaviours has made it essential for businesses owners to improve their ecommerce platforms to become robust and scalable, while database enhancement is the most critical part of this robust platform. With the emerging technologies of NoSQL, and various database options on the market, ecommerce system developers would wonder whether NoSQL is a better option for their platforms. To help ecommerce system developers make better database decisions, this research conducted 9 use case tests (5 single- thread tests, 4 multiple- thread tests) with CRUD (Create, Read, Update, Delete) operations to compare the performance of SQL (PostgreSQL) and NoSQL (MongoDB) databases with real ecommerce data exported from Kaggle (Kaggle, n.d.-a). In these 9 tests, PostgreSQL outperformed MongoDB in nearly 7 tests (3 single-thread tests, 4 multiple-thread tests), while MongoDB performed better in insert and delete operations with single thread scenarios. Therefore, this research found (within its single host design constraints) that PostgreSQL is a better option for ecommerce platforms where large amounts of concurrent requests happen frequently. The author also suspects that the nature of the ecommerce data model, which is more relational, determines the result that SQL performs better than NoSQL in ecommerce scenarios.

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