Anthony Cavin

Computer vision is transforming industries by automating decision making based on visual data. From facial recognition to autonomous driving, the need for efficient computer vision solutions is growing rapidly. This article explores how Roboflow combined with ReductStore, a time-series object store optimized for managing continuous data streams, can improve computer vision applications. ReductStore is designed to efficiently handle high-frequency time-series data, such as video streams, making it a perfect fit for storing and retrieving large datasets generated by computer vision tasks.

In How to Store Vibration Sensor Data, we discuss the importance of efficiently storing both raw vibration data and pre-processed metrics, and the benefits of using time-series databases such as ReductStore. We explore best practices for setting up a time-series database and implementing data retention policies to effectively manage high-frequency sensor data.

We also see how to store vibration sensor values in 1-second chunks, each packaged as binary data, to optimize the storage process when dealing with high-frequency data such as vibration or acoustic measurements.

In this post, we compare ReductStore and InfluxDB in a real-world benchmark scenario, focusing on their write and read performance for high-frequency sensor data. We show how ReductStore's binary storage provides superior efficiency and scalability over InfluxDB when handling large volumes of unstructured time-series data.

The benchmark was run on an SSD drive, but results may vary depending on hardware configuration and database settings; to explore how it performs on your setup, you can run the benchmark yourself using the Reduct Vibration Example repository on GitHub.

In edge computing, managing time series blob data efficiently is critical for performance-sensitive applications. This blog post will compare ReductStore, a specialized time series database for unstructured data, and MongoDB, a widely-used NoSQL database.

Using Docker containers for straightforward setup, we'll examine the speed of each system. We'll go through setting up ReductStore buckets and preparing MongoDB collections, focusing on how to effectively store and access blob data for time series scenarios.

By conducting performance tests on binary data insertion and retrieval, we aim to provide insights into which system might best serve your application's needs.

For those interested in replicating our benchmarks or conducting their own evaluations, we've made our methods easily accessible through this repository.