ReductSoftware UG

Many people try to use TimescaleDB to keep a history of unstructured data. I've investigated the approach and done some benchmarks against ReductStore My conclusion, you can use TimescaleDB with blob data but only with objects smaller than 20-30K, and you have to build your own solution for data retention solution based on disk usage.

In many IoT or computer vision applications, we often need to store a history of unstructured data. It is probably a bad idea to write blobs to a SQL database, especially continuously as a time series. However, MongoDB is a NoSql database that allows time series, it still loses ReductStore in performance.

See the link for the numbers.

In many IoT or computer vision applications, we often need to store a history of unstructured data. It is probably a bad idea to write blobs to a SQL database, especially continuously as a time series. However, MongoDB is a NoSql database that allows time series, it still loses ReductStore in performance.

See the link for the numbers.

In many applications, you may need to capture a history of unstructured data. Previously, we built an in-house solution based on Minio and InfluxDB. Apart from the fact that it wasn't an easy development task, we also ran into performance issues with blobs smaller than 100KB over high latency networks. ReductStore is a time series database for blob data designed specifically for edge computing. It uses HTTP to access data over time intervals and shows much better performance for small blobs due to its batching approach.