Category: Influxdb iot


Comment 3. In other words, we will build a dashboard based on Grafana that visualizes the data acquired by sensors.

With this, InfluxDB stores the values read by sensors. All the systems exchange data using MQTT. The picture below better describes the whole Raspberry Pi IoT project.

These components run using Docker containers. So, how do these components exchange data, and how are they connected? The picture below shows how to do it:. Now, we know all the components and the role they play we can build the system. First, we start building and configuring all these components. During this tutorial, we will assume that Docker is already installed on your Raspberry Pi. The first step is installing Mosquitto on Raspberry Pi.

To do it, we will use Docker so that we can install all we need easily:. Once the Mosquitto is up and running, we can install and configure InfluxDB. As you may already know, InfluxDB is a time-series database where we can store data time-dependant. Just a few things to notice. The next step is creating the database and the user that will access this database. The user will be used by Telegraf when it accesses to the database to store the data coming from the MQTT channel. With these few lines, we have created a database named sensors and a user with username telegraf and password telegraf.

It is time to install and configure Telegraf, the component that connects to the MQTT broker subscribing to the channel where sensor data is published and store this information into the InfluxDB. Before using Telegram, it is necessary to configure it. The first thing is creating a default configuration that we will modify to adapt it to our scenario:. Now, it is possible to configure Telegraf.

Open telegraf. Then, we need to modify the output section. Look for outputs. The last component we will install and configure is Grafana, the tool that creates the dashboard. When you run the Grafana using Docker, there could be an error. If this is your case, you can follow this post:. Now that we have configured all the components, it is time to test if the connections are working.

To do it let us start all the components if they aren't already running. Now, download MQTT. We will use MQTT.InfluxDB is an open-source time series database, a database that is optimized for handling time series data.

But what is time series data anyway? Essentially, it's arrays of numbers indexed by time. Load times of a website, temperature measurements of a smart fridge, daily closing values of the Dow Jones Industrial Average, to name a few, are the examples of time series data. As Baron Schwartz wrotesome of the typical characteristics of time series database are:. First released inInfluxDB is a fairly young database. Time series database is not a new idea.

So why did we at Airly decided to use InfluxDB? We receive a high volume of air pollution sensors data. Just how fast? I did a performance testing on a single-node InfluxDB installation on c4.

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Every item is around 10 values, a mix of integers, floats and short strings. The load isn't exactly the IoT traffic, though. Unless you batch your writes on the service layer before sending them to InfluxDB. Which you probably should do. Batch writes are more than recommended [1].

The above isn't a real benchmark of course, but rather a glimpse of what a single InfluxDB instance can do on the ingress side. What's more, all that data around 4 million measurements fits into 78MB worth of disk space. More on our migration to InfluxDB below. At Airly, we produce and store air quality sensors data and need to analyze it.

influxdb iot

The most common load for us are lots of writes to DB pre-batched by back end service and analytical queries. I'll call those queries:. The aggregation query is more CPU-bound, which could explain a similiar result. InfluxDB fighting with IoT data attack!

Time series data InfluxDB is an open-source time series database, a database that is optimized for handling time series data. Other than data ingest speed, InfluxDB: allows series to be indexed has an SQL-like query language provides advanced time aggregation features provides built-in linear interpolation for missing data supports automatic data down-sampling supports continuous queries to compute aggregates Query speed comparison between InfluxDB and PostgreSQL At Airly, we produce and store air quality sensors data and need to analyze it.

Coming up nextIn the previous part we took a bird's-eye view of InfluxDB, it's core features and some of the reasons to embrace the database in the wake of IoT data onslaught. Let's begin with firing up a new EC2 instance, let's say m4.

Let's also create a security group and allow incoming TCP traffic to ports and Next, let's install the thing. Ssh to the server and execute the following:. You should connect to local InfluxDB and see something similar to the below.

So far in this series InfluxDB was a knight atop a white stallion.

Building an InfluxDB IoT Edge Data Collection Device (Part 1)

There's a twist, though. The OSS version does not support replication and sharding. You're pretty much stuck with a single installation pretty powerful though, as we saw in Part 1. High availability is achievable using influxdb-relaybut that's one more layer and piece of infrastructure to manage. If you need out-of-the-box sharding, service availability, monitoring and other enterprise features I'd just call them production-ready features, but that's a fuel for another postthen your company have to go with commercial InfluxEnterprise or fully managed SaaS.

For a free version, you can stick to a half-century old methods:. Custom sharding is difficult and costly in development, requires a good domain understanding and change prediction up to the level of oracle power. Still, it can win in the long run. The problem with scaling up is that eventually you're greeted with the law of diminishing returns. And the price grows exponentially.

Also, vertical scaling is not appropriate for all technologies. Depending on specific bottlenecks of a system, it's possible that scaling up is almost impossible. For example, disk IO is not an easy thing to upgrade. Let's see some data about how InfluxDB scales up. Below is the comparison of query execution times on different AWS instances for two different types of load. I'll call them:. This is by no means a thorough benchmark. However, it is a fair approximation of Airly's intended load for the DB.

influxdb iot

How can the results be interpreted? It looks like write operation are CPU intensive. The following is how top command looked like on c4. That's why we don't see much decrease of the read query execution time. Provided that working set fit into RAM.

To summarize, CPU power is very important for a write throughput. In this case, high memory instance types r4, m4, etc would be recommended. In the next part we're going to plot some graphs with Grafana using data from InfluxDB!

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InfluxDB fighting with IoT data attack! For a free version, you can stick to a half-century old methods: Custom sharding Scale-up use faster hardware Custom sharding is difficult and costly in development, requires a good domain understanding and change prediction up to the level of oracle power.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

influxdb iot

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This repository contains a complete example that grabs device data from The Things Network, stores it in a database, and then displays the data using a web-based dashboard. You should set up this service to run all the time so as to capture the data from your devices; you then access the data at your convenience using a web browser.

This dashboard uses docker-compose to set up a group of four primary docker containersbacked by two auxiliary containers:. To make things more specific, most of the description here assumes use of Microsoft Azure. However, I have tested this on Ubuntu 16 LTS without difficulty apart from the additional complexity of setting up apt-get to fetch docker, and the need for a manual install of docker-composeon DreamCompute, and on Digital Ocean. I believe that this will work on any Linux or Linux-like platform that supports docker, docker-compose, and node-red.

It's likely to run on a Raspberry Pi All communication with the Apache server are encrypted using SSL with auto-provisioned certificates from Let's Encrypt. Grafana is the primary point of access for most users, and Grafana's login is used for that purpose.

These URLs are protected via Apache htpasswd and htgroup file entries. These entries are files in the Apache container, and must be manually edited by an administrator.

When you start the Grafana container the first time, it creates grafana. If grafana. Microsoft Azure, by default, will not open any of the ports to the outside world, so you will need to open port for SSL access to Apache. For concreteness, the following table assumes that base is server. This can be visualized as below:. Your host system must have docker-compose 1. If not set, docker-compose will quit at startup.

This is by design! Within their containers, the individual programs use their usual ports, but these are isolated from the outside world, except as specified by docker-compose. In docker-compose. Remember, if your server is running on a cloud platform like Microsoft Azure or AWS, you need to check the firewall and confirm that the ports are open to the outside world. When designing this collection of services, we had to decide where to store the data files.

We had two choices: keep them inside the docker containers, or keep them in locations on the host system. The advantage of the the former is that everything is reset when you rebuild the docker images. The disadvantage of the former is that you lose all your data when you rebuild.

On the other hand, there's another level of indirection when keeping things on the host, as the files reside in different locations on the host and in the docker containers.

As shown, you can easily change locations on the host e. Directories are created as needed. Since data files on the host are not removed between runs, as long as you don't remove the files between runs, your data will preserved.

Creating Your Personal IoT/Utility Dashboard Using Grafana, Influxdb & Telegraf on a Raspberry Pi

Sometimes this is inconvenient, and you'll want to remove some or all of the data. For a variety of reasons, the data files and directories are created owned by root, so you must use the sudo command to remove the data files. Here's an example of how to do it:.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again.

If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. I must admit this post is just an excuse to play with Grafana and InfluxDb. InfluxDB is a cool database especially designed to work with time series. And Grafana is one open source tool for time series analytics.

I want to build a simple prototype. The idea is:. We'll use Docker.

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I've got a Docker host running in a Raspberry Pi3. The Esp32 part is very simple. We only need to connect our potentiometer to the Esp The potentiometer has three pins: Gnd, Signal and Vcc. We'll use the pin In grafana we need to do two things. First create one datasource from our InfluxDB server.

IoT - Home sensor data monitoring with MQTT, InfluxDB and Grafana

It's pretty straightforward to it. Finally we'll create a dashboard. We only have one time-serie with the value of the potentiometer. I must admit that my dasboard has a lot things that I've created only for fun.

Thats the query that I'm using to plot the main graph. Here we can see the dashboard. I've also created a notification channel with a webhook. Grafana will use this web hook to notify when the state of alert changes. Grafana will emit a webhook, so we'll need an REST endpoint to collect the webhook calls. Mqtt is a very simple protocol but it has one very nice feature that fits like hat fits like a glove here. Le me explain it: Imagine that we've got our system up and running and the state is "ok".

Since the "ok" event was fired before we connect the lights, our green light will not be switch on.Time series data are simply measurements or events that are tracked, monitored, downsampled, and aggregated over time. This could be server metrics, application performance monitoring, network data, sensor data, events, clicks, trades in a market, and many other types of analytics data.

Johan Janssen - Processing IoT sensor data with InfluxDB

A Time Series Database is built specifically for handling metrics and events or measurements that are time-stamped. A TSDB is optimized for measuring change over time. Properties that make time series data very different than other data workloads are data lifecycle management, summarization, and large range scans of many records. The whole InfluxData platform is built from an open source core. But for nature cities to be a reality, we need to understand greenery performance from data.

So we also needed the system to be reliable and stable while growing between those two points. And we needed it to be fast, both for data collection and querying. They are what keeps us in business. It drives the need for products like InfluxDB.

Nearly every available surface in the physical world is being instrumented with sensors — streets, cars, factories, power grids, ice caps, satellites, clothing, phones, microwaves, milk containers, planets, even human bodies.

This data is streaming in real time and will require companies to select an IoT data platform architecture that is resilient, scalable and extensible enough to handle these new workloads.

If businesses can properly harness all of this data, they can gain real-time insights, accelerate decision-making, perform automated tasks, and create value by enabling organizations to become data-driven.

Tracking battery performance, turbine production, package delivery status and generally visually monitoring sensor data provides incredible capabilities to the business and their customers. This is usually the first step in any successful IoT project. Using historical data from sensors to gain insights that can be applied to the current situation creates a major competitive advantage. Predictive maintenance, optimized traffic routing, reduced churn management, and enhanced water conservation are all possible with IoT analytics.

With the speed and velocity of events being generated by sensors, businesses want to act on this data in real time with no human interaction. For example, automatically shutting down a pump if a leak is detected, or changing wind turbine direction based on wind speed, all create an immediate business advantage. I hope you like this post. Do you have any questions? Leave a comment down below! Thanks for reading. If you like this post probably you might like my next ones, so please support me by subscribing my blog.

Hi, I'm Harshvardhan Mishra. I am a tech blogger and an IoT Enthusiast. I am eager to learn and explore tech related stuff! I generally appreciate learning by doing, rather than only learning. Thank you for reading my blog! Happy learning! Follow and send tweets me on harshvardhanrvm. If you want to help support me on my journey, consider sharing my articles, or Buy me a Coffee! Skip to content.And when this happens, some questions come to my mind:.

Measuring is important, we know that as developers, otherwise how could we get accurate answers and take the best decisions without data? Since both are giving me different data, how can I know which is the most accurate one? So I called my parents for help, and they lent me a few mercury thermometers.

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The 2 mercury thermometers were displaying exactly the same temperature, so I assumed that they were accurate. After some research, it turned out that, despite its impressive appearance, the DHT22 is actually not a very good sensor. The BME is better, but has its own problems too, as it suffers from self-heating in its default configuration sampling data continuously. Hopefully, you can write code to change the sampling rate and make the sensor return to sleep mode when the measurement is finished.

Instead of fetching data every second, I decided to fetch once per minute, with the sensor returning to sleep mode immediately after. The temperature it gave me then was very close to the one of the mercury thermometers.

To see if this works, you can use MQTT. You can also install the MQTT Dash application on your Android device to see temperature data directly on your smartphone:. Complete implementation is available here.

When a message is published, values are automatically persisted to InfluxDB. Now, we need a tool to show these data over the time in a graph. Grafana is an open-source, general purpose dashboard and graph composer.

We will install it on the Raspberry Pi:. To automate running containers, we can create a docker compose file. See the docker-compose. However, I would like to also have the outside temperature and humidity data.

While designed for an indoor usage, it supports temperatures from