We are pleased to announce the release of the latest minor version of ReductStore, 1.12.0. 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 1.12.0?​

Over the last few months we've been working hard to make ReductStore even more powerful and efficient as a central repository for your time series data. Where you can collect data from a variety of sources, including IoT and edge devices, and store it in one place for further analysis and processing.

In this release, we've added a new record delete API that allows you to remove specific records from an entry. This can be useful if you want to clean up your data or remove obsolete records and need more flexibility than FIFO bucket quotas. We have also optimised the storage engine to improve overall performance when reading and writing data.

The MQTT protocol is an easy way to connect disparate data sources to applications, making it very popular for IoT (Internet of Things) applications. Some MQTT brokers can store messages for a while, even when the MQTT client is offline. However, sometimes you need to keep this data for a longer period of time. In these cases it's a good idea to use a time series database.

There are many time series databases available, but if you need to store a history of images, sensor data or protobuf messages, you might want to use ReductStore. This database is designed to store a lot of blob data and works well with IoT and edge computing.

ReductStore has client SDKs (software development kits) for many programming languages. This means you can easily use it in your existing system. For this example, we'll use the Python SDK from ReductStore.

Let's create a simple MQTT application to see how it all works.

Computer vision is transforming industries by automating decision making based on visual data. From facial recognition to autonomous driving, the need for efficient computer vision solutions is growing rapidly. This article explores how Roboflow combined with ReductStore, a time-series object store optimized for managing continuous data streams, can improve computer vision applications. ReductStore is designed to efficiently handle high-frequency time-series data, such as video streams, making it a perfect fit for storing and retrieving large datasets generated by computer vision tasks.