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Alexey Timin
Software Engineer - Database, Rust, C++

A software engineer with a passion for databases, Rust, and C++, always looking for new challenges and opportunities to build efficient, scalable systems for managing large amounts of data.

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Keeping MQTT Data History with Node.js

· 6 min read
Alexey Timin
Software Engineer - Database, Rust, C++

MQTT+ReductStore in Node

The MQTT protocol is widely used in IoT applications because of its simplicity and ability to connect different data sources to applications using a publish/subscribe model. While many MQTT brokers support persistent sessions and can store message history while an MQTT client is unavailable, there may be cases where data needs to be stored for a longer period of time. In such cases it is recommended to use a time series database. There are many options available, but if you need to store unstructured data such as images, sensor data or Protobuf messages, you should consider using ReductStore as a MQTT database. It is a time series database specifically designed to store large amounts of unstructured data, optimised for IoT and edge computing.

ReductStore provides client SDKs for many programming languages to integrate it into your infrastructure. For this example, we will use the JavaScript client SDK.

Let's build a simple application to understand how to keep a history of MQTT messages using ReductStore and Node.js.

ReductStore v1.12.0 released: record deletion API and storage engine optimization

· 3 min read
Alexey Timin
Software Engineer - Database, Rust, C++

We are pleased to announce the release of the latest minor version of ReductStore, 1.12.0. 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 1.12.0?

Over the last few months we've been working hard to make ReductStore even more powerful and efficient as a central repository for your time series data. Where you can collect data from a variety of sources, including IoT and edge devices, and store it in one place for further analysis and processing.

In this release, we've added a new record delete API that allows you to remove specific records from an entry. This can be useful if you want to clean up your data or remove obsolete records and need more flexibility than FIFO bucket quotas. We have also optimised the storage engine to improve overall performance when reading and writing data.

How to Keep a History of MQTT Data With Python

· 7 min read
Alexey Timin
Software Engineer - Database, Rust, C++

MQTT+ReductStore in Python

The MQTT protocol is an easy way to connect disparate data sources to applications, making it very popular for IoT (Internet of Things) applications. Some MQTT brokers can store messages for a while, even when the MQTT client is offline. However, sometimes you need to keep this data for a longer period of time. In these cases it's a good idea to use a time series database.

There are many databases available for storing MQTT data, but if you need to store a history of images, sensor data or protobuf messages, you might want to use ReductStore. This database is designed to store a lot of blob data and works well with IoT and edge computing.

ReductStore has client SDKs (software development kits) for many programming languages. This means you can easily use it in your existing system. For this example, we'll use the Python SDK from ReductStore.

Let's create a simple MQTT application to see how it all works.