Everactive is an Industrial IoT (IIoT) company that combines battery-free, self-powered sensors and powerful cloud analytics to provide end-to-end hyperscale IoT solutions to their customers. These include Fortune 500 manufacturers in many industries, such as process manufacturing and chemical processing. The company is undergoing a huge transformation from focusing only on industrial monitoring services to opening the platform to developers that can leverage their technology to create solutions and services.
Everactive experienced three main problems with PostgreSQL and non-time-series databases: major performance and indexing problems, the lack of queries specific to their data types, and a quick onboarding process for engineers as the company scaled. Everactive stored metadata in PostgreSQL on Amazon RDS and sensor data in OpenTSDB. Over time, OpenTSDB became increasingly slow and brittle—it had become customary for OpenTSDB to crash multiple times per week from users asking for too much data at once.
After switching to Timescale, Everactive's frontend view could display sensor data in just 7 seconds; a drastic change from the 10 minutes it used to take with OpenTSDB. Further improvements to their schema and access patterns have brought these queries into the sub-second range, even with data volume growth. Thanks to the compression and continuous aggregates, they have kept table sizes in check and with great performance. Consolidating time-series and relational data in Timescale Cloud reduced Everactive's operational costs and database maintenance.
The capacity of Timescale Cloud to support both traditional schemas and time-series data in the same database allowed us to consolidate into one storage solution and saved us the operating costs of running AWS RDS instances. Dealing with fewer instances and relying more on the Timescale Support Team for infrastructure maintenance has reduced our database maintenance workload significantly.
Carlos Olmos, Software Engineer at Everactive