{"id":738,"date":"2026-04-14T08:58:17","date_gmt":"2026-04-14T07:58:17","guid":{"rendered":"https:\/\/entertainmentnews.site\/index.php\/2026\/04\/14\/how-to-store-and-manage-robotics-data\/"},"modified":"2026-04-14T08:58:17","modified_gmt":"2026-04-14T07:58:17","slug":"how-to-store-and-manage-robotics-data","status":"publish","type":"post","link":"https:\/\/entertainmentnews.site\/index.php\/2026\/04\/14\/how-to-store-and-manage-robotics-data\/","title":{"rendered":"How to Store and Manage Robotics Data"},"content":{"rendered":"
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The topic How to Store and Manage Robotics Data<\/strong> is currently the subject of lively discussion \u2014 readers and analysts are keeping a close eye on developments.<\/p>\n

This is taking place in a dynamic environment: companies\u2019 decisions and competitors\u2019 reactions can quickly change the picture.<\/p>\n

Posted on Apr 14<\/p>\n

\u2022 Originally published at reduct.store<\/p>\n

Robots generate massive amounts of data, and managing it well is harder than it looks. Storage fills up fast, cloud transfer gets expensive, and real time ingestion is unforgiving when you're running cameras and sensors at high frequency.<\/p>\n

This article covers practical strategies for handling robotic data, introduces ReductStore, and walks through a hands on example. Along the way, we cover native ROS integration, Grafana dashboards, MCAP export for Foxglove, a Zenoh API, and native S3 and Azure backends. We also compare ReductStore against Rosbag and MongoDB so you can pick the right tool for each part of your stack.<\/p>\n

Robots operate in dynamic environments and continuously produce large volumes of data. The core challenges engineers run into are:<\/p>\n

ReductStore is a time series database built specifically for unstructured, binary data. It's designed to handle high frequency sensor streams from autonomous vehicles, drones, industrial robots, and IoT. It stores data with timestamps and labels, supports fast real time ingestion, and comes with batching, filtering, and edge to cloud replication built in.<\/p>\n

It also writes a $ros attachment to each entry with the message schema, topic name, and encoding \u2014 that metadata is what powers MCAP export and Grafana visualization, both covered below. If your stack is already on ROS, setup takes minutes.<\/p>\n

ReductStore supports native cloud storage backends for both Amazon S3 and Azure Blob Storage, no FUSE drivers needed. It uses a local cache for hot data and the cloud bucket for long term retention.<\/p>\n

For records around 100KB (e.g. JPEG images), ReductStore can be >10x faster than traditional time series object stores at a fraction of the cost.<\/p>\n

ReductStore has a Grafana integration through the ReductStore data source plugin. You can query ROS 2 messages directly in Grafana thanks to the ReductROS extension. The extension decodes binary CDR messages into JSON on the fly.<\/p>\n

You can monitor sensor streams live, compare data across multiple robots, and set up alerts when metrics drift.<\/p>\n

You can export raw ROS messages stored in ReductStore directly to MCAP and open them in Foxglove. This is powered by the ReductROS extension as well, which reconstructs valid MCAP files on demand.<\/p>\n

When records are ingested via reduct-bridge, the $ros attachment carries the schema and topic information. The extension uses this to reconstruct valid MCAP files on demand. You can cover a full time range across multiple topics in a single query, and split by duration or file size for long recordings.<\/p>\n

ReductStore now includes a Zenoh native API alongside the existing HTTP API. Zenoh is a pub\/sub protocol designed for robotics and distributed systems, and it's widely used in next generation ROS 2 deployments.<\/p>\n

It's a natural fit if your robot stack already speaks Zenoh, or if you want to skip HTTP overhead on high frequency ingestion paths.<\/p>\n

ReductStore uses a JSON based query language that supports filtering, aggregation, and time range operations. You can query data from a specific robot within a time window, compare sensor streams across robots in parallel, or filter by any label.<\/p>\n

Retrieval is optimized through batching: multiple records are grouped into a single response based on a time range, which cuts down on request overhead and improves throughput. SDKs are available for Python, C++, JavaScript, Go, and Rust. More in the querying guide.<\/p>\n

ReductStore replicates at the bucket level based on conditions. That means you can choose to send only high priority sensor data to the cloud based on rules, labels, or events. Replication is incremental, so only new data is transferred. Labels stored alongside records let you define fine grained rules based on content, for example, only replicate records flagged as anomalies or sampled at 1 in 10 seconds.<\/p>\n

Volume based retention follows the FIFO principle: data is only deleted when storage is full, making room for new records. This is different from time based retention, which deletes data after a fixed age regardless of whether storage is full. After an outage, a time based policy might delete data the system never had the chance to process. Volume based retention avoids that.<\/p>\n

Three tools come up most often when robotics teams think about data storage: Rosbag and MCAP, MongoDB, and ReductStore. They're built for different things.<\/p>\n

Rosbag and MCAP are the standard for recording ROS sessions. They're great for capturing a snapshot during a test run. But they're file formats, not databases. Querying across many recordings requires custom scripts, there's no built in content indexing, and managing thousands of bag files quickly becomes its own problem. Use them for short recordings and local playback, not long term storage or fleet wide analysis.<\/p>\n

MongoDB is flexible and works well for structured metadata, labels, and event logs. But it wasn't built for large binary payloads. For blob data, it relies on GridFS, which adds complexity and hurts performance. It also uses time based retention, so data can be deleted during idle periods even when storage isn't full.<\/p>\n

ReductStore is designed specifically for the data robots actually produce: large, binary, timestamped records at high frequency. It connects to ROS via reduct-bridge, exports to MCAP for Foxglove, plugs into Grafana, and replicates selectively to S3 or Azure.<\/p>\n

In practice: use Rosbag or MCAP for short test recordings, MongoDB for structured metadata and event logs, and ReductStore for raw sensor data that needs to be stored at scale, queried efficiently, and managed over time.<\/p>\n

For a deeper look: MongoDB vs ReductStore: Choosing the Right Database for Robotics Applications.<\/p>\n

Let's walk through a practical example of storing and querying robotic data with ReductStore. We'll use trajectory data (coordinates, speed, orientation) to keep things simple, but the same approach works for any sensor stream.<\/p>\n

ReductStore will be available at http:\/\/127.0.0.1:8383. Check the container is running with docker ps, then install the Python libraries:<\/p>\n

The bucket uses a FIFO quota of 1 GB. Old data is only deleted when the limit is reached.<\/p>\n

This simulates a robot moving in 2D at 10 Hz for 1 second. X and y are position coordinates, yaw is orientation, and speed is derived from position changes.<\/p>\n

The labels (total distance and average speed) are stored alongside each record. You can later filter or replicate records based on these values.<\/p>\n

Once data is written, you'll see the bucket populate in ReductStore:<\/p>\n

Remove the 'when' condition to return all records with no filtering.<\/p>\n

Robotics data pipelines don't have to be a mess of bag files, overpriced cloud storage, and custom scripts duct taped together. ReductStore covers ingestion, retention, replication, and querying in one place, with direct integrations into ROS, Foxglove, Grafana, S3, and Azure.<\/p>\n

Start with what you need, and add the rest as your system grows. Check out reduct.store or read through the documentation to get going.<\/p>\n

If you have any questions or comments, feel free to use the ReductStore Community Forum.<\/p>\n

Templates let you quickly answer FAQs or store snippets for re-use.<\/p>\n

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