Anthony Cavin

TimescaleDB is an open-source time-series database optimized for fast ingest and complex queries. It is engineered up from PostgreSQL and offers the power, reliability, and ease-of-use of a relational database, combined with the scalability typically seen in NoSQL systems. It is particularly suited for storing and analyzing things that happen over time, such as metrics, events, and real-time analytics.

Since TimescaleDB is based on PostgreSQL, it supports blob data and can be used to store a history of unstructured data such as images, binary sensor data, or large text documents. In this article, we will use the database as a time-series blob storage and compare its performance with ReductStore, which is designed specifically for this use case.

TimescaleDB and ReductStore both have Python Client SDKs. We'll create simple Python functions to read and write data, then compare performance with different blob sizes. To repeat these benchmarks on your own machine, use this repository.

Your robot loses its uplink halfway through an eight hour shift. Cameras and sensors keep writing. Sending every frame to the cloud was never an option.

ReductStore and INSAION have formalized their partnership for robotics edge recording. It makes official what already ships in production: ReductStore is the storage engine behind INSAION's edge recording. Every Rolling Buffer, every device recording, and every Surgical Cloud Sync runs on ReductStore embedded directly in the INSAION Agent.

Robotics teams today wrestle with data that grows faster than their infrastructure. Every robot generates streams of images, sensor readings, logs, and events in different formats. These data piles are fragmented, expensive to move, and slow to analyze. Teams often rely on generic cloud tools that are not built for robotics. They charge way too much per gigabyte (when it should cost little per terabyte), hide the raw data behind proprietary APIs, and make it hard for robots (and developers) to access or use their own data.

ReductStore introduces a new category: a database purpose built for robotics data pipelines. It is open, efficient, and developer friendly. It lets teams store, query, and manage any time series of unstructured data directly from robots to the cloud.