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.

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.

InfluxDB works well for robotics metrics such as battery level, temperature, CPU usage, detection counts, and anomaly scores. These numeric values are a good fit for dashboards, alerts, and time-series analytics.

Robots also generate much heavier data: camera frames, LiDAR scans, audio, and high-frequency waveform chunks. This raw data must often be preserved in its original format for incident replay, debugging, and model training.

You do not need to replace InfluxDB. Keep it for the metrics you need to visualize and analyze, and add ReductStore for the raw sensor data. Use the same event time and stable robot and sensor identifiers in both systems so a metric can be traced back to the original records.