METER Group is a scientific instrumentation company with 30+ years of expertise in developing sensors for the agriculture and food industries. They have applied this technical expertise to the cannabis market, creating a platform that allows growers to work more efficiently and increase their yields—and to do so consistently and at scale. They collect data about the climate through IoT sensors and serve them to their clients in real time.
Due to the legacy data loggers, METER Group used the Amazon RDS Aurora service and cobbled together a set of triggers and functions that partitioned the main reading table by each client facility. This setup worked well at first, but as they progressed from alpha to beta and the customer base grew, it became increasingly clear that it was not a long-term solution.
The main requirement for the new solution was that any chart request needed to take less than one second for the API to serve. METER Group evaluated InfluxDB and Amazon Timestream and considered going NoSQL. Nothing compared favorably to what they were able to achieve with Timescale.
Timescale’s features, like continuous aggregates, allow METER Group to ingest billions of readings every month without negatively affecting the database performance. Additionally, using Timescale’s compression and time_bucket feature allowed METER Group to take their 1.83 TB database and compress it to 700 GB.
We found Timescale’s compression to be as good as advertised, which gave us 90 %+ space savings in our underlying hypertable.
Paolo Bergantino, Director of Software at METER Group