The Robot Operating System (ROS) stands as a versatile framework for developing sophisticated robotic applications with various sensors, including cameras. These cameras are relatively inexpensive and widely used as they can provide a wealth of information about the robot's environment.

Processing camera output with computer vision requires efficient solutions to handle massive amounts of data in real time. ROS 2 is designed with this in mind, but it is a communication middleware and does not provide a built-in solution for storing and managing large volumes of image data.

Addressing this challenge, this blog post will guide you through setting up ROS 2 with ReductStore—a time-series database for unstructured data optimized for edge computing, ensuring your robotic applications can process and store camera outputs effectively.

We are pleased to announce the release of the latest minor version of ReductStore, 1.9.0. ReductStore is a time series database designed for storing and managing large amounts of blob data.

To download the latest released version, please visit our Download Page.

What’s New in 1.9.0?​

This release, version 1.9.0, introduces several key improvements and features to enhance the overall performance and user experience. These updates include optimizations for disk space management, the inclusion of replication support in the Web Console, and the provision of license information in the HTTP API.

In this article, we will compare two data storage solutions: ReductStore and Minio. Both offer on-premise blob storage, but they approach it differently. Minio provides traditional S3-like blob storage, while ReductStore is a time series database designed to store a history of blob data. We will focus on their application in scenarios that require storage and access to a history of unstructured data. This includes images from a computer vision camera, vibration sensor data, or binary packages common in industrial data.

Handling Historical Data​

S3-like blob storage is commonly used to store data of different formats and sizes in the cloud or internal storage. It can also accommodate historical data as a series of blobs. A simple approach is to create a folder for each data source and save objects with timestamps in their names:

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