20 posts tagged with "tutorials"

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.

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 time series databases available, 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.

When it comes to computer vision, data storage is a critical component. You need to be able to store images for model training, as well as the results of the processing for model validation. There are a few ways to go about this, each with its own advantages and disadvantages. In this post, we’ll take a look at three different ways to store data in computer vision applications: a file system, an S3-like object storage and ReductStore. We’ll also discuss some of the pros and cons of each option.

A Simple Computer Vision Application​

For demonstration purposes, we’ll use a simple computer vision application which is connected to a CV camera and runs on an edge device:

The camera driver captures images from the CV camera every second and forwards them to the model, which then detects objects and displays the results in the user interface.

Your images and results need to be stored for training and validation purposes. The customer may also wish to view images featuring anomalous objects. These requirements present the challenge of maintaining a history of blob or unstructured data.