ReductStore is designed to efficiently handle time series unstructured data, making it an excellent choice for storing high frequency vibration sensor measurements. This article is the second part of How to Store Vibration Sensor Data | Part 1, where we discussed the benefits of storing both raw measures and pre-processed metrics, the advantages of time series databases, and efficient storage and replication strategies.

In this post, we'll dive into a practical example of storing and querying vibration sensor readings using ReductStore and Python. To follow along, you can find the full source code for this example at GitHub's reduct-vibration-example repository.

Our example will show you how to:

1. Store simulated sensor values in 1-second chunks
2. Compute and store associated labels for each chunk
3. Query and retrieve stored measurements within a specified time range
4. Set up replication using the ReductStore web console

Efficient and effective storage of vibration data is important to a wide range of industries, particularly where accurate and complex predictive maintenance or optimization is required.

This blog post looks at best practices for managing vibration data, starting with storing both raw and pre-processed metrics to take advantage of their unique benefits. We'll explore the differences between time series object stores and traditional time series databases, and highlight optimal data flow processes.

We'll also cover strategies for eliminating data loss through volume-based retention policies, guide you through setting up an effective data retention frameworks.

We are pleased to announce the release of the latest minor version of ReductStore, 1.10.0. ReductStore is a time series database designed for storing and managing large amounts of blob data.

To download the latest released version, please visit our Download Page.

What's new in 1.10.0?​

ReductStore v1.10.0 introduces new query and replication parameters that can downsample data when querying or replicating to another database. In addition, we have optimized the operation of the storage and replication engines, which should improve the overall performance of the database.