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Alexey Timin
Co-founder & CTO - Database & Systems Engineering

Co-founder and CTO building the core database and data infrastructure. Focused on high-performance storage, low-latency systems, and scaling real-time data for industrial and robotics workloads.

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Capture Output from Legacy Equipment with the Reduct Bridge Shell Input

· 9 min read
Alexey Timin
Co-founder & CTO - Database & Systems Engineering

A legacy machine sends command output through the Reduct Bridge shell input to a time-indexed ReductStore entry

Plenty of equipment on the shop floor will never speak MQTT or OPC-UA. It predates both. What it has is a command line: a vendor tool that prints status, a diagnostic command on a serial console, or a script the previous engineer left behind. The data exists. There is just no protocol to integrate.

Reduct Bridge has a shell input for exactly this case. You point it at a command, set an interval, and the bridge runs it on schedule. Each line the command prints becomes a time-indexed record in ReductStore. Labels come from regex capture groups in the output or from static key-value pairs in the config. Wrap whatever the vendor tool prints, put it on an interval, label it, and it lands time-indexed beside everything else.

This post walks through capturing telemetry from a legacy test chamber that only exposes a chamber-status command.

Building Reliable Data Replication at the Edge

· 8 min read
Alexey Timin
Co-founder & CTO - Database & Systems Engineering

We originally built ReductStore to store data on edge devices and read it back by time interval. But a device only has so much disk space. With cameras and sensors writing all the time, we used a FIFO quota to remove the oldest records and make room for new ones. That left us with another problem: how to get the data off the device before it was deleted.

At first, transferring data from an edge device to central storage was a manual job. We used the CLI client or scripts and copied data when somebody remembered to do it. It was always problematic because data collection never stopped. Sometimes we had only two or three days to copy it before it was gone, and a temporary network problem or a missed run could make that window disappear completely.

Automatic replication was the next logical step. The edge is producing a stream of new data, while the central store has more capacity and can have different criteria for what it keeps. We needed to forward new records and configure source-side filters for each destination. Keeping two identical replicas was not the goal.

We also had to keep in mind that the source is usually on the edge. Very often it has no public IP address, and its network connection can be unstable, slow, or absent for long periods. Replication therefore could not make local ingestion wait for the central store. The device still needed to accept data first and deliver it later when a connection became available.

ReductStore v1.21: Data Explorer and Community Contributions

· 8 min read
Alexey Timin
Co-founder & CTO - Database & Systems Engineering

ReductStore v1.21.0 Released

ReductStore 1.21.0 is now available. This release brings the new Data Explorer to the Web Console and delivers many improvements to data replication, lifecycle policies, queries, and system diagnostics.

ReductStore v1.21 also reflects an important change in how the project is developed. Since moving ReductStore Core to the Apache License 2.0 in v1.19, the community has become an important part of the development process. This release cycle brought together 16 contributors across ReductStore, ReductStore CLI, and the Web Console.

To download the latest release, visit the Download Page.

What's new in 1.21.0?​

The main user-facing change is the new Data Explorer in Web Console v1.17.2. It provides a visual interface for building conditional queries, sampling and limiting results, and configuring ReductROS and ReductSelect processing steps without writing the complete JSON query by hand.

Replication is now more flexible and efficient. A replication task can add a prefix to destination entry names, compress batch transfers with gzip or zstd, and use path-aware entry patterns with *, **, and exclusions. Lifecycle policies and regular queries use the same entry-pattern syntax, making filters consistent across the database.

The release also expands diagnostics in the $system bucket, adds bounded processing windows for lifecycle policies, and improves replication throughput and reliability.