6 posts tagged with "iot"

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.

The MQTT protocol is a simple way to connect different sources of data to applications, which makes it very popular for IoT (Internet of Things) applications. Some MQTT brokers can save messages for a while, even when the MQTT client is offline. However, sometimes, you need to keep this data for a longer time. In those 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 things like 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. In this example, we'll be using the Python SDK from ReductStore.

Now, let's make a simple MQTT application to see how this all works.

In this article, we will compare two data storage solutions: ReductStore and Minio. Both offer on-premise blob storage, but they approach it differently. Minio provides traditional S3-like blob storage, while ReductStore is a time series database designed to store a history of blob data. We will focus on their application in scenarios that require storage and access to a history of unstructured data. This includes images from a computer vision camera, vibration sensor data, or binary packages common in industrial data.

Handling Historical Data​

S3-like blob storage is commonly used to store data of different formats and sizes in the cloud or internal storage. It can also accommodate historical data as a series of blobs. A simple approach is to create a folder for each data source and save objects with timestamps in their names:

bucket
 |
 |---cv_camera
        |---1666225094312397.jpeg
        |---1666225094412397.jpeg
        |---1666225094512397.jpeg

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 as long as an MQTT client is not available, there may be cases where data needs to be stored for a longer period. 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, consider using ReductStore. It is a time series database specifically designed for storing large amounts of blob data and optimized for IoT and edge computing.

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

Let’s make a simple MQTT application to see how it works.

Benchmarks don't lie, let's put the systems to the ultimate test.

ReductStore vs. MinIO & InfluxDB on Edge Device HX401

For anyone deeply immersed in the engineering world of Edge Computing, Computer Vision, or IoT, you'll want to read further to understand why a time series database for blob data is needed and where it stands out.

Enter our contest: First, we have ReductStore—a time series database for blob data—specifically designed for edge devices.

Its counterpart? The duo of MinIO and InfluxDB, each optimized for their niche in blob storage and time-series data respectively.

When directly compared, which system takes the lead in performance?

Let's roll up our sleeves and deep-dive into this benchmarking analysis to separate fact from fiction.