SCALABILITY TESTING APPROACH FOR INTERNET OF THINGS FOR MANUFACTURING (IoTfM) SQL AND NOSQL DATABASE LATENCY AND THROUGHPUT

Abstract The proliferation of low-cost sensors and industrial data solutions have continued to push the frontier of manufacturing technology. Machine Learning and other advanced statistical techniques stand to provide tremendous ad-vantages in production capabilities, optimization, monitoring, and efficiency. The tremendous volume of data gathered continues to grow, and the methods for storing the data are critical underpinnings for advancing manufacturing technology. This work aims to investigate the ramifications and design trade offs within a decoupled architecture of two prominent Database Management Systems: SQL and NoSQL. A representative comparison is carried out with Amazon Web Services (AWS) DynamoDB and AWS Aurora MySQL. The technologies and accompanying design constraints are investigated, and a side-by-side comparison is carried out through high fidelity industrial data simulated load tests using metrics from a major US manufacturer. The results support the use of simulated client load testing for comparing latency of database management systems as a system scales up from the prototype stage into production. As a result of complex query support, MySQL is favored for higher order insights, while NoSQL can reduce system latency for known access patterns at the expense of integrated query flexibility. By reviewing this work, a manufacturer can observe that use of high-fidelity load testing can reveal tradeoffs in IoTfM write/ ingestion performance in terms of latency that are not observable through prototype-scale testing of commercially available cloud DB solutions.