Industrial Internet of Things: Persistence for Time Series with NoSQL Databases

TLDR

The proliferation of IoT devices generates vast streams of time‑series data that must be persisted efficiently, a challenge that is especially acute in industrial settings with many instrumented machines. This study aims to empirically compare the persistence and retrieval performance of three NoSQL databases—Cassandra, MongoDB, and InfluxDB—on real industrial IoT data. The authors benchmarked the databases using gigabytes of time‑series data collected from an instrumented dressing machine, measuring ingestion and retrieval metrics. InfluxDB consistently outperformed Cassandra in all tests and achieved better overall performance than MongoDB.

Abstract

With the advent of Internet of Things (IoT) tech-nologies, there is a rapidly growing number of connected devices, producing more and more data, potentially useful for a large number of applications. The streams of data coming from each connected device can be seen as collections of Time Series, which need proper techniques to guarantee their persistence. In particular, these solutions must be able to provide both an effective data ingestion and data retrieval, which are challenging tasks. This problem is particularly sensible in the Industrial IoT (IIoT) context, given the potentially great number of equipment that could be instrumented with sensors generating time series. In this study we present the results of an empirical comparison of three NoSQL Database Management Systems, namely Cassandra, MongoDB and InfluxDB, in maintaining and retrieving gigabytes of real IIoT data, collected from an instrumented dressing machine. Results show that, for our specific Time Series dataset, InfluxDB is able to outperform Cassandra in all the considered tests, and has better overall performance respect to MongoDB.