7 posts tagged with "comparison"

The amount of data generated world-wide is expanding exponentially, and will only increase further in coming years. In fact, over 90% of the data worldwide has been generated in the last two years, and 40% of data in 2020 was generated by machines. Not to mention that 80 to 90 percent of data is unstructured. Not only is timely processing of said data ever more important, the data itself is often time-stamped and must be handled in a time-based structure. Due to the rise of AI/ML, Robotics, IoT, and edge-computing, solutions that can efficiently leverage much cheaper and plentiful unstructured object/blob storage while maintaining the ability to organize, read, and transmit time-series based data from multiple sources and in multiple formats are in great demand. ReductStore and MinIO are two solutions designed to meet this demand.

Robotics applications generate and process a wide variety of data, such as sensor readings, video streams, logs, and AI model outputs. Managing this data efficiently is crucial because it affects the performance, scalability, and reliability of the entire system.

In this article, we'll compare ReductStore and MongoDB, two databases designed to handle different aspects of data management. ReductStore is a time-series blob storage solution optimized for managing large amounts of data coming from continuous streams. MongoDB, on the other hand, is a popular NoSQL database known for its flexibility, scalability, and support for unstructured and semi-structured data.

By understanding the strengths and limitations of each, you can make an informed decision to meet your project's specific data needs.

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.