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ReductStore v1.18.0 Released with Resilient Deployments and the Multi-entry API

· 5 min read
Alexey Timin
Software Engineer - Database, Rust, C++

ReductStore v1.18.0 Released

We are pleased to announce the release of the latest minor version of ReductStore, 1.18.0. ReductStore is a high-performance storage and streaming solution designed for storing and managing large volumes of historical data.

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

What's new in 1.18.0?

In this release, we have added support for resilient deployments to build a more robust, fault-tolerant, and highly available ReductStore cluster. Now, you can implement hot-standby configurations, automatic failover, and seamless recovery to ensure uninterrupted service even in the face of hardware failures or network issues. You can also elastically scale read-only nodes to handle increased read workloads without impacting the performance of the primary nodes.

Additionally, we have introduced a new Multi-entry API that allows you to efficiently manage and query multiple entries in a single request. This API is designed to optimize performance and reduce latency when working with large datasets, making it easier to retrieve and manipulate data in bulk.

Comparing Data Management Tools for Robotics

· 8 min read
Gracija Nikolovska
Software Developer - C#, Python, ROS
Anthony Cavin
Data Scientist - ML/AI, Python, TypeScript

Data Management Tools for Robotics

Modern robots collect a lot of data from sensors, cameras, logs, and system outputs. Managing this data well is important for debugging, performance tracking, and training machine learning models.

Over the past few years, we've been building a storage system from scratch. As part of that work, we spoke with many robotics teams across different industries to understand their challenges with data management.

Here's what we heard often:

  • Only a subset of what robots generate is actually useful
  • Network connections are not always stable or fast
  • On-device storage is limited (hard drive swaps is not practical)
  • Teams rely on manual workflows with scripts and raw files
  • It's hard to find and extract the right data later
  • ROS bag files get large quickly and are difficult to manage

In this article, we compare four tools built to handle robotics data: ReductStore, Foxglove, Rerun, and Heex. We look at how they work, what they're good at, and which use cases they support.

If you're working with robots and need to organize, stream, or store data more effectively, this overview should help.

Distributed Storage in Mobile Robotics

· 7 min read
Anthony Cavin
Data Scientist - ML/AI, Python, TypeScript

Distributed Storage in Mobile Robotics

Mobile robots produce a lot of data (camera images, IMU readings, logs, etc). Storing this data reliably on each robot and syncing it to the cloud can be hard. ReductStore makes this easier: it's a lightweight, time-series object store built for robotics and industrial IoT. It stores binary blobs (images, logs, CSV sensor data, MCAP, JSON) with timestamps and labels so you can quickly find and query them later.

This introduction guide explains a simple setup where each robot stores data locally and automatically syncs it to a cloud ReductStore instance backed by Amazon S3.