5 posts tagged with "comparison"

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.

TimescaleDB is an open-source time-series database optimized for fast ingest and complex queries. It is engineered up from PostgreSQL and offers the power, reliability, and ease-of-use of a relational database, combined with the scalability typically seen in NoSQL systems. It is particularly suited for storing and analyzing things that happen over time, such as metrics, events, and real-time analytics.

Since TimescaleDB is based on PostgreSQL, it supports blob data and can be used to store a history of unstructured data such as images, binary sensor data, or large text documents. In this article, we will use the database as a time-series blob storage and compare its performance with ReductStore, which is designed specifically for this use case.

TimescaleDB and ReductStore both have Python Client SDKs. We'll create simple Python functions to read and write data, then compare performance with different blob sizes. To repeat these benchmarks on your own machine, use this repository.