Why You Should Use ReductStore to Store Robotics Data

The first step in preparing your robots for the real world is strategically recording and uploading the data. Whether it is limited on-device storage, high-performance, or real-time analytics, storing data for robots is challenging for robotics engineers. Massive data is produced continuously by the robots. High-performance maintenance is anticipated, which can translate into high cloud storage costs. Further real-time processing is required in robotics engineering and necessitates edge computing. An efficient solution is required to store data cost-effectively and process it in real time using edge computing. ReductStore is a time series object storage database. While it has features like high ingestion, edge-to-cloud replication, and a better retention policy, ReductStore is a better alternative for storing robotic data. Moreover, it comes with cheap blob storage in the cloud so that you can use ReductStore Cloud as a central storage for historical data.

This article briefly discusses the challenges faced in storing robotic data and how ReductStore is solving key areas for the aforementioned challenges.

What are the Challenges in Storing Robotics Data?

Robotics data often involves unstructured time series high-frequency object storage data like LiDAR, images, and logs. Some of the most intriguing challenges are:

1. Diverse Data Types Management – Robotics Data are diverse, requiring management of diverse data types, whether lightweight telemetry (Pose, GPS, system state, etc.), downsampled sensor data (Lower framerate (e.g., 1hz camera), Reduced resolution (e.g., running average)), or entire sensor data (Raw LiDAR, camera images, etc to us for debugging, can generate 1TB per hour).
2. Edge to Cloud Replication – To optimize Robotics Data storage, critical data, such as the latest logs or details of high-priority events, must be kept on edge devices for immediate access. At the same time, less time-sensitive data is moved to the cloud for long-term analysis and archiving.
3. High Cloud Storage Cost – It’s impractical and costly to send all robotic data to the cloud. Services charge for cloud storage and data transfer, and as robots generate many terabytes of data, the costs pile up exponentially. There should be a balance between what to store locally and what to offload to the cloud, as this ensures cost-effective data management.
4. Limited On-Device Storage Capacity – Robots have limited on-device storage capacity, which is a challenge as data is enormous. An optimized reduction policy is required.
5. Implementing the best reduction strategy – Not all data can be stored. A better retention policy, however, can store essential data and delete less significant data.

Why is ReductStore a Better Alternative for Storing Data for Robots?

Robots mainly collect data from sensors. Time series data is better suited for robotics data as it is well-suited for handling and analyzing historical data. Such data are crucial for understanding robotic and environmental behavior. A significant chunk of Robotics Data falls under time series high-frequency blob object storage data. ReductStore does support lightweight, low-frequency, and low-resolution data. While it’s optimized for large-scale blob storage, it can efficiently handle more minor telemetry data like pose, GPS, or system state, especially when stored alongside higher-resolution sensor data for comprehensive analysis and debugging. Different reasons why ReductStore is best for robotics data are:

1. Retention Policy and Strategies – it implements volume-based retention policies that follow the FIFO (first-in, First-Out) principles. It also deletes the old data only when the storage is complete, so that new data can be stored. These customizable retention policies can be adjusted separately for each data bucket.
2. Edge to Cloud Syncing – Whether it is real-time processing or efficient data storage, critical data is stored on edge devices for immediate access, and less sensitive data is transferred to the cloud, saving considerable cloud space, such as edge-to-cloud syncing, which balances cloud costs and real-time data processing.
3. Batching Capabilities – There is no denying that data retrieval can be optimized through batching. It processes batch records in iterations, which a large dataset requires. Moreover, multiple documents can be grouped in one query within a time range.
4. Cost Efficiency – 10x better performance than other traditional time series databases can be achieved when dealing with records of around 100 KB (e.g., images, bag files). Integration with cheaper storage options like Azure Blob Storage or Google Cloud Storage is possible.
5. Easy Integration with Popular Programming Languages – Simple to use via an HTTP(s) API and SDKs in several languages makes this solution user-friendly.

Conclusion

Storing robotic data is challenging, whether with limited device storage capacity, real-time data processing, or edge-to-cloud Streaming (replication). ReductStore is one of the best databases for storing robotic data with features like high-frequency large time series blob data support, real-time data processing, edge computing, and lower cost.