InfluxDB community showcase
InfluxData is proud to share the InfluxDB community’s achievements. Members’ enthusiasm and accomplishments with our time series platform can help inspire your own projects. See how our community members use InfluxDB in amazing and exciting ways!
See how the community is using InfluxDB
One key to maintaining reliable systems is having a reliable monitoring setup. A common set of software used for monitoring is the TIG stack: Telegraf, InfluxDB, and Grafana. Telegraf collects, processes, and aggregates system metrics such as CPU usage, Disk IO, and network throughput. InfluxDB stores that data in a way that has fast read/writes with scaling built-in. Grafana allows you to visualise the data in simple, flexible, and granular dashboards. Learning, installing, and configuring these components involves time, coordinating multiple steps, and understanding how they all fit together. Luckily, there’s Bolt.
Recently, I have started brewing my own beer. My wife bought me a 1 gallon kit for Father’s Day so I could dive right in and start learning the craft beer brewing process. It is something that I have always been interested in learning but never took the time and space has been limited. The whole beer making process has always intrigued me and is something that is way out of my element.
I recently discovered the atribe/apcupsd-influxdb-exporter container on the CA plugin page and immediately thought it would be a great replacement for the script that I run, described in this post. But time got in the way and I forgot about it. But now the summer is here, the days are longer and free time is no longer a rarity. So this will be a quick follow up post on how to switch to this container and get even more accurate readings!
This weekend I decided to exchange my 8 year old i3 laptop with a desktop. I was offered an HP elite 8300 small form factor desktop in exchange of laptop and 7000 Rs. So far I was using raspberry pi 3 for hosting web based apps and running some other programs 24×7. I mainly opted for pi boards due to low power consumption and convenience of throwing them anywhere in home.
I have for some time shared my Unraid System dashboard over at Grafana.com but never really had the time to make a quick write up on how to set it all up. So this will try to do just that. This guide will make it so you will be able to monitor cpu usage, cpu temps, network stats, ram usage and much more by simply importing a dashboard.
If you are dealing with the streaming analysis of your data, there are some tools which can offer performing and easy-to-interpret results. First, we have Kafka, which is a distributed streaming platform which allows its users to send and receive live messages containing a bunch of data (you can read more about it here). We will use it as our streaming environment. Then, if we want to visualize, in real time, our results, we need a tool which can capture our data and predictions: it is Grafana, and among its data sources, it can be connected to InfluxDB, an open source time series database. So, through this article we will build an ML algorithm which can extract information and make predictions, in real time, on our data, throughout the following steps:
En busca del Dashboard perfecto: InfluxDB, Telegraf y Grafana – Parte XVII – Mostrando los Dashboards en dos monitores usando Raspberry Pi 4
In this post I will show you the step by step how to be able to visualize our Dashboards, if you have followed the whole series, you will already have about 16 Dashboards, dynamically and in two monitors using a Raspberry Pi 4 Model B.
Computers have been collecting and storing data in relational/schema systems for many years. However, digital storage growth outpaces that of computing processing power by leaps and bounds. Additionally, the amount of unstructured that is collected greatly exceeds that of structured data, further limiting the utility of tradional database systems. For these reasons, today’s data storage technqiues call for some new technical constructs required to break the boundaries of traditional transaction-processing databases.
Grafana is the answer to the nagging question we’be been asking ourselves over the years – how to quickly and nicely present our data gathered from devices. InfluxDB on the other hand is the database that is as easy and simple to use, thus making it an ideal candidate for this job.
In this session we’ll retrieve sensor data with a Spring Boot application. After that we will store the data in InfluxDB a time series database. We can store the data with the InfluxDB Java client or by making REST calls. Then we’ll make the data available in some nice dashboards running in Grafana and Chronograf.