Telemetry logging to InfluxDB and Grafana on Raspberry Pi

This is part of my series on learning to build an End-to-End Analytics Platform project.

TLDR; After a suggestion from a friend (Waylon Payne LinkedIn) we start learning about time series database InfluxDB. We set up InfluxDB on the Raspberry Pi by creating a database. Work on getting Grafana installed and running. We write, troubleshoot, and learn a bunch logging data to InfluxDB. Finally we create a dashboard in Grafana to display on Sense HAT telemetry.

Begin the Influx ๐ŸŒŒ

Last time we tackled writing out SenseHAT readings to a csv on the Pi. Now though, we level up by working on writing that data to a database more suited for streaming log data, in this case InfluxDB.

The InfluxDB documentation has a section for installing on a Raspberry Pi. Now while I was discussing this with my friend, he suggested before strolling down the path of flashing a new OS I should read this article Installing InfluxDB & Grafana on Raspberry Pi. Skipping step 0 is the plan in our case. Another post for references was Datalogger example using Sense Hat, InfluxDB and Grafana. Big thanks to Simon Hearne and, their posts really helped guide my thinking even though I chose to do things a little differently. ๐Ÿ˜Ž

First up, some updates.

sudo apt update
sudo apt upgrade -y
bash terminal apt update
Ah yes.. updates..

Updates ran pretty quickly. The next part is getting the InfluxDB packages.

wget -qO- | sudo apt-key add -
source /etc/os-release
echo "deb $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/influxdb.list

A few things here to learn from the previous code snippets:

  • apt – Is a command line package/software management tool on Debian (Debian Wiki) like search, installation, and removal.
  • etc directory – Holds core configuration files. Found a nice Linux directory structure for beginners post.
  • wget – Is a command line package/software for retrieving files using HTTP, HTTPS and FTP, the most widely-used Internet protocols (Debian Wiki).
  • tee – Reads the standard input and writes it to both the standard output and one or more files (GeeksForGeeks).
  • source – Reads and execute the content of a file (GeeksForGeeks).

Best I can understand at the moment is that we get and store a public key that allows us to authenticate/validate the InfluxDB package when we download it by echoing the latest InfluxData stable release package and adding the package deb record to the source.list directory in a new file which seem to allow apt-get to pick up future updates. That kicked off the install of InfluxDB. How do we know? The console says so..

bash terminal InfluxDB installation
The influx begins!

It installs InfluxDB version 1.8.9 (not 2.0 which is the latest at the moment). Keep that in mind when working with documentation. Upgrading to 2.0 we can leave for the future. Onward!

sudo systemctl unmask influxdb.service
sudo systemctl start influxdb
sudo systemctl enable influxdb.service

More things to learn:

Found the command to check a service status with the –help switch for the systemctl command. These all feel reasonably familiar coming from working a little with PowerShell and the Windows terminal.

sudo systemctl --help
sudo systemctl status influxdb.service
bash terminal InfluxDB service status check
Running, running, running.

The service is up, active, and running. That means we should be able to connect to it. We can do that by logging into the Influx CLI from the terminal. Then creating a database. Creating a user. Finally, granting the user permissions.


create '<yourdatabase>'

use '<yourdatabase>'

create user '<yourusername>' with password '<yourpassword>' with all privileges

grant all privileges on '<yourdatabase>' to '<yourusername>'
bash terminal Influx CLI

Ah familiar territory! A database! Now we have:

  • A database service running.
  • A database created.
  • A user that has more than enough permissions to interact with the database.


We want a way to visualise the telemetry that’s going to be written into the database. Grafana gives us the ability to create, explore and share all of your data through beautiful, flexible dashboards and we can run the service on the Pi. We’re taking the same approach as we did for InfluxDB to get Grafana up and going. Getting all the packages, installing them, running updates, and validating the services.

wget -q -O - | sudo apt-key add -
echo "deb stable main" | sudo tee /etc/apt/sources.list.d/grafana.list

sudo apt update && sudo apt install -y grafana

sudo systemctl unmask grafana-server.service
sudo systemctl start grafana-server
sudo systemctl enable grafana-server.service

sudo systemctl status grafana-server.service
bash terminal Grafana service status check
So much fitness ๐Ÿƒ

Once the service is up we should be able to connect to it on port 3000.

Grafana login page

Yay! We’re connected. Let’s log in with the user name and password ‘admin‘ then reset the password. After the login process is done, we’ll land on the homepage for our Grafana instance running on the Pi. I’m actually excited about this ๐Ÿ˜„.

We need a way to connect Grafana to our InfluxDB database. On the Home page is a ‘Data Sources‘ tile which we can follow to add a data source.

Grafana home page
Source of the action ๐Ÿ’ฅ

We can use the search box to lookup a connector for InfluxDB. Once we have that we just select it.

Grafana data source search page
Search the unknown ๐Ÿ”ฎ

From there we configure the settings for the connector.

Grafana data source settings
The way through the mountains ๐Ÿ”๏ธ

Credentials to make our way into the database.

Grafana data source credential settings
You shall pass ๐Ÿง™โ€โ™‚๏ธ

Finally, we save and test the connector to make sure its all working.

Grafana source page connection test
Green.. Green is good ๐ŸŸข

Good news. The scaffolding is in place. Now we need to get data into the database then configure some dashboards.

Bilbo Loggings ๐Ÿชต

โ€œIf ever you are passing my way,โ€ said Bilbo, โ€œdonโ€™t wait to knock! Tea is at four; but any of you are welcome at any time!โ€

– Bilbo Baggins

Did someone say tea? Time for a spot of IoT! Now to get our IoT device capturing data. A quick swish of our telemetry logging code and we have a starting point. All we need to do is figure out how to log data to InfluxDB not the csv.

There is a library for working with InfluxDB hosted on PyPi. We need to download and install the packages locally. Remembering to use our Python virtual environment. Though this time we are going to give the VS Code Python environments integration the spotlight. Invoke the Command Palette Ctrl + Shift + P. Start typing and select the option for “Python: Select Interpreter“.

VSCode command palette
Lost in translation

Look what VS Code recommends…

VSCode command palette Python interpreter selection
Recommended interpretation

Our virtual environment. It’s so smart. I think it reads my blog drafts ๐Ÿคฃ. After we chose the interpreter, VS Code switches context to our virtual environment context. It even reminds us in the status bar at the bottom of the window. So thoughtful!

VSCode status bar
Our friendship will continue.

๐Ÿšง Slight detour ๐Ÿšง

Slight digression from our regular blog flow...

Now, if you are briskly following along and haven’t switched to the venv in the terminal, then you will probably run into an error like this. Which might lead you on a wild goose chase across fields of learnings and wonder.

bash terminal error installing InfluxDB Client
Goose chase begins ๐Ÿฆข

Naturally, we go looking to see if we find any packages for influxdb-client:

pip search influxdb-client

Which the PyPI XMLRPC API politely, in a crimson message, lets us know things are not as peachy as we hoped:

bash terminal error downloading InfluxDB client
โš ๏ธ!Unmanageable load!โš ๏ธ

Fault: <Fault -32500: “RuntimeError: PyPI’s XMLRPC API is currently disabled due to unmanageable load and will be deprecated in the near future. See for more information.”>

After updating the pip version in the venv, we check on the status which the error message suggested. After a very interesting read, a smidge of despair, hope emerges… I said to myself, “Self, why does that terminal not have the venv prefix?“. That’s when I realised the true source of the problem. Me. I forgot to activate the venv ๐Ÿ˜….

For the terminal we still need to activate the virtual environment. To do this on the Pi we can run:

source <yourvenv>/bin/activate
bash terminal activate Python venv
Activates PEBKAC fix to end goose chase ๐Ÿฆข

Behold!!! It lives!!

When we do that, the terminal actually changes a little, giving us a visual cue that we are in a Python virtual environment. Now to get supporting packages installed so that we can write Python code for InfluxDB. Taking a look at the InfluxDB Client Python GitHub Repo or the influxdb-client PyPI project.

pip install influxdb-client
bash terminal installing InfluxDB client
Goose = chased! ๐Ÿฆข

Sometimes Python can’t catch the programmer being the error.

~ me

๐Ÿšง Slight detour ends ๐Ÿšง

The packages are installed. The logging can begin. To get started we need to import the influxdb_client into our project.

from sense_hat import SenseHat
from datetime import datetime
from influxdb_client import InfluxDBClient, Point

Yet again, we find a pebble in our shoe… While trying to import the sense_hat library modules in the REPL an error presented itself which seemed related to the way the numpy library was installed.

Python terminal numpy error
numpy dumpty had a great fall ๐Ÿคฃ

The error message helps a ton! Jumping over to the common reasons and troubleshooting tips helps with options to solve the issue on a Raspberry Pi, if we use our original error “ cannot open shared object file: No such file or directory“. I opted for the first option to install the package with apt-get:

sudo apt-get install libatlas-base-dev

Then tried to enter the python REPL again (just typing “python” while in the terminal) and importing the SenseHat module.

Python terminal module error
No one by that name here..

I am beginning to feel like a module hunter ๐Ÿน. I tracked down a RaspberryPi thread which led me to a comment on a GitHub issue for the RTIMU module error. To be clear, this doesn’t seem to be an issue when I am running in the global Python scope. Only an issue in the virtual environment. The folks were kind enough to provide a way to install this with a pip command. Here we go:

pip install rtimulib
Python terminal module successful installation
Sparks joy โœจ

Yes! It works!

I tried initially to write a Python function that would write to and query the database. It wasn’t long before I ran into an error trying to connect to the database using the Python function.

Python error message Flux query service disabled.
Flux capacitor disabled โšก

For v1.8 it’s disabled and we need to enable it. To edit the file we can use nano a Linux Command Line text editor.


  # ...

  flux-enabled = true

  # ...
sudo nano /etc/influxdb/influxdb.conf

Which opens the file in nano for us to edit.

Editing influxdb config in nano
A little text editing

The settings are changed. To bring them into effect we need to restart the service.

sudo systemctl restart influxdb.service
bash terminal influx service start
Starting the engine

No much to go on. I found what seems to be a potential workaround. There is a comment further down in this thread on InfluxDB not starting that talks about adjusting a sleep setting for a start up file. Worth a try. Using nano again, we open the file and make the change.

Editing influxdb systemd start sh file in nano
bash terminal influxdb service status check
Running, running, running ๐Ÿƒ

Time to write the code that will log records to our database. The idea is simple. Run a loop. Every few seconds get a Sense HAT reading. Log the reading to our InfluxDB. Stop the loop when we interrupt the program.

from sense_hat import SenseHat
from datetime import datetime
from influxdb_client import InfluxDBClient, Point

timestamp =
delay = 15
sense = SenseHat()
host = "localhost"
port = 8086
username = "grafanabaggins"
password = "<NotMyPrecious>"
database = 'shire'
retention_policy = 'autogen'
bucket = f'{database}/{retention_policy}'

def get_sense_reading():
    sense_reading = []
    return sense_reading

# This method will log a sense hat reading into influxdb
def log_reading_to_influxdb(data, timestamp):
    point = ([Point("reading").tag("temperature", data[1]).field("device", "raspberrypi").time(timestamp), Point("reading").tag("pressure", data[2]).field("device", "raspberrypi").time(timestamp), Point("reading").tag("humidity", data[3]).field("device", "raspberrypi").time(timestamp)])
    client = InfluxDBClient(url="http://localhost:8086", token=f"{username}:{password}", org="-")
    write_client = client.write_api()
    write_client.write(bucket=bucket, record=point)

# Run and get a reading Forrest
def run_forrest(timestamp):
        data = get_sense_reading()
        log_reading_to_influxdb(data, timestamp)
        while True:
            data = get_sense_reading()
            difference = data[0] - timestamp
            if difference.seconds > delay:
                log_reading_to_influxdb(data, timestamp)
                timestamp =
    except KeyboardInterrupt:
        print("Stopped by keyboard interrupt [CTRL+C].")

I struggled for a while trying to figure out the bucket/token variable to what I was able to do in the 1.8.9 CLI easily. I revisited the Python client library and noticed a specific callout for v1.8 API compatibility which has an example that helped me define the token. It wasn’t long before we got the script running and data was being logged to the database.

We’re getting there!

To the Shire

Before we get started on logging data to the database we need to understand some key concepts in InfluxDB. It won’t be the last time I visit that page, these concepts are foreign to me. I learnt to use InfluxQL which is a SQL language to work with the data. There are some differences between Flux and InfluxQL that you might want to keep in mind. I had a tricky time figuring out how to execute Flux queries initially after I wasn’t getting any data back from my Flux commands in a Python function, but saw that you could invoke a REPL to test queries with). To keep things simple though, I opted for InfluxQL. We can launch the Influx CLI from the terminal and query our data.



USE <database>

SELECT * FROM <table>
Influx queries returning results
Successfully captured! ๐Ÿชค

Let’s see if we can build a dashboard to visualise the data we are logging. We can connect to our Grafana server again. Head to the home page. There is a “Explore” menu item that is a quick way for us to query our data and experiment. Once the window opens up we select our data source connection from the drop down box and begin building a query with a wonderfully simple interface.

Visual query building ๐Ÿคฉ

It’s at this point we realise that our logging design might not be correct. What I was expecting was that I could use the columns in the SELECT and WHERE clauses. Apparently not. I initially thought that design would work better because I understood that tags were indexed, not fields, so querying the tags would be faster. Good in theory, but I couldn’t reference the tag in the SELECT and WHERE clauses. My initial mental model needed tweaking. A change to the logging function to log to a single point, not three, with multiple fields.

So this:

point = ([Point("reading").tag("temperature", data[1]).field("device", "raspberrypi").time(timestamp), Point("reading").tag("pressure", data[2]).field("device", "raspberrypi").time(timestamp), Point("reading").tag("humidity", data[3]).field("device", "raspberrypi").time(timestamp)])

Changed to this:

point = ([Point("reading").tag("device","raspberrypi").field("temperature", data[1]).field("pressure", data[2]).field("humidity", data[3]).time(timestamp)])

Minor InfluxDB management needed in future to clean up the database. For now though, we have our ‘frodologgins‘ database which is empty. I ran the logging function against the new database and…

* Chef’s kiss *

It works as expected! A quick updated to the Grafana connection settings to switch to the new database. With the updates in place we now get the expected results in the drop down. We can see the fields we want to display and chart.

We can try reconcile the point, tags, and field in the Python code to how we are querying it with InfluxQL. Slowly sharpening our mental model and skills. The query reads as follows:

  • From our database
  • Query our readings for the default/autogen retention policy
  • Where the device tag value is raspberrypi
  • Return the last temperature field reading
  • Group by ten second intervals

One thing I wasn’t quite sure of is the way that the time range worked in Grafana with the data logged in the database. The query looked correct but no data was returned. I was looking at a window from now-1d to now initially. It seemed logical to me, “find me all the data points from yesterday to now“. The Inspector in Grafana helps get the query and then we can use that to run the query in the Influx CLI to test the queries.

Inspector Clouseau ๐Ÿ•ต๏ธ

I eventually adjusted that to now to now+1d which in my mind is “back to the future” ๐Ÿ”ฎ๐Ÿš—, but it worked. I think this comes down to how the dates are stored (i.e. timezone offsets) and the functions evaluation. I’ll dig into that later, for now this works, we have data showing on a graph.

Grafana explore graph sample

Let’s take the learnings and apply it to building the dashboard. Head to the home page. There is a “Dashboards” tile we can use to build our first dashboard.

Grafana home page
Dash lightning! ๐ŸŒฉ๏ธ

It opens up a new editing window. I chose an empty panel. From there we can edit the panel in a similar way to what we did with the Explore window. In the upper right corner we can choose the type of chart.

Grafana explore chart type selection
Serious time โณ

There are a bunch of options from changing the charts, adjusting threshold values for the gauges, applying units of measure, and so much more. For our case that’s “Time series“.

That’s it! Use the same approach to build out the other charts. I added “Gauge” visuals as well with the corresponding query.

Grafana final dashboard displaying readings
Ice, Ice, Baby ๐ŸงŠ

Learnings ๐Ÿซ

We made it! It took a while but we did it. Failure is a pretty good teacher. I failed a bunch and learnt a more. That’s not wasted time. It’s worth just getting hands on and trying different things out to build the mental model and skills. I have a long way to go to really understand Python, InfluxDB, Grafana, and Linux but I’ve made progress and learnt new things which is a blessing.

Until next time.


Logging SenseHAT telemetry on the Raspberry Pi

This is part of my series on learning to build an End-to-End Analytics Platform project.

TLDR; I’ve been working on upping my Python game ๐Ÿ. This post we got started by creating a Python virtual environment. Then built a sense HAT data logger with the Raspberry Pi to write the readings to a local file on the Pi.

Going virtual ๐Ÿ’พ

If you are wondering how I am writing code remotely on the Pi, go check out setting up remote development on the Raspberry Pi using VS Code. We are using the same approach here to get connected and working on our Pi.

Part of this journey is growing my skills. I chose Python as a programming language. Not diving into too many details. It just gives a range of capabilities (web through to machine learning) with a single language. No need to switch too much while learning all the techs in this series. Works for me.

While upping my Python game I came across something called virtual environments. A little primer on virtual environments. I think I have a reasonable grasp on how to start using them for better package management.

Not going full virtualenvwrapper yet though. “Hey, I just met you and this is crazy, but here’s my bookmark, browse it later maybe. #justsayin’

To that note, let’s set up a virtual environment. First, check our Python versions on the Pi:

python3 --version
Snake in eagle shadow ๐Ÿ๐Ÿฆ…

We have Python3 installed on the Pi. That means we should have the venv capability built-in. Let’s give it a whirl!

python3 -m venv noobenv
Environment cultivated

When we do that a new folder gets created in our repo. It has a bunch of folders and files related to the “inner workings” of how virtual environments work <- science ๐Ÿ‘ฉโ€๐Ÿ”ฌ.

Logging the things ๐Ÿชต

The goal here is that we have an IoT device that is capturing data from the sensors. It has a bunch of sensors we are going to use, which is exciting. Honestly, the more I work with it, the more amazing it is to me.

from sense_hat import SenseHat
from datetime import datetime

sense = SenseHat()

def get_sense_reading():
    sense_reading = []

    return sense_reading

We create a function (get_sense_reading) that we can call repeatedly. Then use the SenseHat functions (e.g. get_temperature) to get readings from the different sensors. To get them all in a single object/row, we can use a list (sense_reading). Then append each reading to the list. Once we have them, we return the list object.

Witness the quickness โšก

We add a for loop to our code to call the get_sense_reading function a few times are print the results to the terminal window. We can run the program ( by calling the Python 3 interpreter and passing the file name to it. That loads the code, executes the loop, prints the results.


Now to add data to a file on the device. We’ll use a CSV for now, then adapt it later based on our needs. We can use the the sense_reading object returned by the get_sense_reading function and write that to the file using the csv library.

from csv import writer

timestamp =
delay = 1

with open("logz.csv", "w", newline="") as f:
    data_writer = writer(f)
                      'gyro_x', 'gyro_y', 'gyro_z'])

    while True:
        data = get_sense_reading()
        difference = data[0] - timestamp
        if difference.seconds > delay:
            timestamp =

We start with a timestamp because we want to calculate a delay interval, say 1 second, between writes to the file. The open the file and write a header row (writerow) to the file. We use a while loop to collect readings, then once we exceed the delay interval, write the row to the file. We need to update the timestamp otherwise we will write a row on every pass after we exceed the timestamp the first time.

Testing seems to be working and we can log data to a file on the device. VS Code integrated terminal really is fantastic at running multiple and side by side shell/terminal windows.

A tail of two terminals ๐Ÿ’ป

Awesome! It works. We have a program logging data to a csv file at a defined interval. Tail simply prints end of file content. A few lines is all we need to double check things are working. Last thing left.. shut down the Pi remotely. Usually I would use a shutdown command. I gave a new command a try “Halt”.

sudo halt
Halt! Who goes there? ๐Ÿ›‘

Looks like that worked ๐Ÿ™‚ The connection got terminated and VS Code detects that, and tries to reconnect. Pretty slick. We managed to start putting new Python skills to use. Learnt how to create a virtual environment for better package management. Then collecting and writing telemetry from the SenseHat to local storage on the Pi.

That’s it for now.


Infrastructure as Code (IaC) reuse

Photo by RODNAE Productions from Pexels

This is part of my series on learning to build an End-to-End Analytics Platform project.

TLDR; I made improvements to the Infrastructure as Code from the previous post by following best practices and promoting code reuse. Continued with parameters, but extended the code with scopes, modules, variables, functions, operators, and outputs. There is a list of Bicep best practices that is worth looking into.

Divide and conquer

We can use modules to group a set of one or more resources to be deployed together. We can reuse modules for better readability and reuse. They basically get converted to nested ARM templates from what I understand.

The first part that I want to move int a module is the data lake storage account and resolve dependencies. When that’s done, repeat the process for the other resources that we want to deploy.

Moving day.

Next up, update modules to use parameters and variables where possible to avoid hard coded values. We should be in a position where the module is bit of code that can be called with a set or parameters. Note that when the resources are in the same file, you can reference them directly. An example from my previous post was were I reference the datalake resource.

Same file resource references.

A module only exposes parameters and outputs to other Bicep files. When we move the data lake resource creation to a module, we need to leverage outputs which can then be passed between modules. The idea is to call a module -> deploy the resource -> output important things -> pass those things to another module as input parameters. So, the same property I referenced before now becomes an output in the module of the storage account:

Output for output.

Output variables can now be used in the main script as inputs to another module, etc. We just reference them using the module.output syntax.

Outputs as inputs.

We use operators in our deployments for things like conditional deployments.

On one condition.

Expanding on the use of parameters and variables, functions are a great way to drive flexibility and reuse into your deployments. Getting runtime details, resource references, resource information, arrays, dates, and more. Just remember most work at all scopes, some don’t. When they don’t you will probably figure that out with errors. One way to use them is to inherit the resource group location during resource deployment. In our case, setting variables with the resource group location, appending a deterministic hash string suffix for the storage account name from the resource group, or even enforcing lower case of names then using the variables for deployment.

Variables and functioning captain ๐Ÿ‘ฉโ€โœˆ๏ธ

FYI, the weird looking string notation ‘${var}‘… that’s call ‘string interpolation‘. Pretty simple compared to other ways I’ve had to write parameterised strings before with all kinds of place holders, parameters, and functions. I like!

As a good practice we use parameter decorators to control parameter constraints or metadata. Things like allowed values, lengths, secure strings, etc.


What we do next in our main deployment file is to change the scope. That way we can deploy at the subscription level which let’s us create resource groups in bicep instead of the Azure CLI which we did in the previous post.

Scoping things out ๐Ÿ”ญ

Note: It’s preferred in most cases to put all parameters/variables at the top of the file.

One other point of interest is that when we change the scope, our module to deploy resources error because they can’t be deployed at the subscription level only the resource group level. Make sense. So we need to change their scope in deployment.

Scope inception.

Polishing up the current solution with these practices was good learning. I continued with the approach across all modules and files. Then ran a few tests to make sure the resources deploy as expected.

That covers it off for this post. What I think we will do next is work on setting up a CI/CD pipeline in GitHub to build and deploy these resources into Azure.