The bottleneck was the database, not the optics. Detection results were written to a hosted MySQL instance where tens of thousands of daily records were held together by sharding plus pre-aggregation. On MOI, write performance improved by an order of magnitude and the platform scaled past 70 devices without re-architecture.
GHSense (Nanjing Guangheng Industrial Technology) builds high-precision opto-electronic sensing and inspection equipment for industrial manufacturers, combining laser sensing with machine vision and AI. Its core team previously worked on national programs including the Chang'e lunar missions and the Fengyun-4 meteorological satellites.
The bottleneck was the database, not the optics. Structured detection results flowed from upstream software into a hosted MySQL instance, where tens of thousands of records a day accumulated until performance steadily degraded. As deployments grew to dozens of inspection devices, time-series write throughput hit a ceiling, and keeping queries usable required table partitioning plus pre-aggregation strategies-complexity that had to be re-engineered at every new site.
GHSense replaced MySQL with OmniFabric's converged engine. The partitioning-plus-aggregation workaround gave way to a native date-partitioned table design, so no sharding is needed as detection history accumulates, and serverless instances scale resources automatically with production load. More than 70 inspection devices now run on the platform.
Write performance improved by an order of magnitude over MySQL, with most writes completing in milliseconds.
The majority of analytical queries over accumulated detection history complete within seconds, without the pre-aggregation the old stack required.
Native date-based partitioning replaced table partitioning plus aggregation strategies-new sites deploy without bespoke database engineering.