# Lab 6 - IoT - processing and visualizing data with InfluxDB & Grafana
###### tags: `LAB`
:::info
**All the code necessary for this Lab session is available in [Poliformat/RSE: Recursos/Laboratorio/código practicas laboratorio](https://poliformat.upv.es/x/1J91ll)**, or here: https://bit.ly/codigoRSE2021
To execute the code in your computer:
* You need to have python3 installed, and
* `$ sudo pip3 install paho-mqtt`
* You need to have docker installed (see https://docs.docker.com/get-docker/)
</br>
Or you can use the VM that you used for Lab 1 and Lab2.
:::
## Introduction to the TIG Stack
The TIG Stack is an acronym for a platform of open source tools built to make collection, storage, graphing, and alerting on time series data easy.
A **time series** is simply any set of values with a timestamp where time is a meaningful component of the data. The classic real world example of a time series is stock currency exchange price data.
Some widely used tools are:
* **Telegraf** is a metrics collection agent. Use it to collect and send metrics to InfluxDB. Telegraf’s plugin architecture supports collection of metrics from 100+ popular services right out of the box.
* **InfluxDB** is a high performance Time Series Database. It can store hundreds of thousands of points per second. The InfluxDB SQL-like query language was built specifically for time series.
* **Grafana** is an open-source platform for data visualization, monitoring and analysis. In Grafana, users can to create dashboards with panels, each representing specific metrics over a set time-frame. Grafana supports graph, table, heatmap and freetext panels.
In this Lab we will use the containers platform [Docker](https://www.docker.com/).
We will use the following images:
* https://hub.docker.com/_/telegraf
* https://hub.docker.com/_/influxdb
* https://hub.docker.com/r/grafana/grafana
## Getting started with InfluxDB
InfluxDB is a time-series database compatible with SQL, so we can setup a database and a user easily. In a terminal execute the following:
```
$ docker run -d -p 8086:8086 --name=influxdb influxdb:1.8
```
This will keep InfluxDB executing in the background (i.e., detached: `-d`). Now we connect to the CLI:
```
$ docker exec -it influxdb influx
Connected to http://localhost:8086 version 1.7.9
InfluxDB shell version: 1.7.9
>
```
The first step consists in creating a database called **"telegraf"**:
```
> CREATE DATABASE telegraf
> SHOW DATABASES
name: databases
name
----
_internal
telegraf
>
```
Next, we create a user (called **“telegraf”**) and grant it full access to the database:
```
> CREATE USER telegraf WITH PASSWORD 'uforobot'
> GRANT ALL ON telegraf TO telegraf
> SHOW USERS
user admin
---- -----
telegraf false
>
```
Finally, we have to define a **Retention Policy** (RP). A Retention Policy is the part of InfluxDB’s data structure that describes for *how long* InfluxDB keeps data.
InfluxDB compares your local server’s timestamp to the timestamps on your data and deletes data that are older than the RP’s `DURATION`. So:
```
> CREATE RETENTION POLICY thirty_days ON telegraf DURATION 30d REPLICATION 1 DEFAULT
> SHOW RETENTION POLICIES ON telegraf
name duration shardGroupDuration replicaN default
---- -------- ------------------ -------- -------
autogen 0s 168h0m0s 1 false
thirty_days 720h0m0s 24h0m0s 1 true
>
```
Exit from the InfluxDB CLI:
```
> exit
```
## Configuring Telegraf
We have to configure the Telegraf instance to read data from the TTN (The Things Network) MQTT broker.
We have to first create the configuration file `telegraf.conf` in our working directory with the content below:
```yaml
[agent]
flush_interval = "15s"
interval = "15s"
[[inputs.mqtt_consumer]]
name_override = "TTN"
servers = ["tcp://eu1.cloud.thethings.network:1883"]
qos = 0
connection_timeout = "30s"
topics = [ "v3/+/devices/#" ]
client_id = "ttn"
username = "XXX"
password = "ttn-account-XXX"
data_format = "json"
[[outputs.influxdb]]
database = "telegraf"
urls = [ "http://localhost:8086" ]
username = "telegraf"
password = "uforobot"
```
where you have to change the "XXX" for `username` and the "ttn-account-XXX" for `password` with the values below:
```
Username: lopys2ttn@ttn
Password: NNSXS.A55Z2P4YCHH2RQ7ONQVXFCX2IPMPJQLXAPKQSWQ.A5AB4GALMW623GZMJEWNIVRQSMRMZF4CHDBTTEQYRAOFKBH35G2A
```
<!--
If using the labs on-line you can edit with `vi` or with the on-line editor:
-->
Then execute:
```
$ docker run -d -v $PWD/telegraf.conf:/etc/telegraf/telegraf.conf:ro --net=container:influxdb telegraf
```
This last part is interesting:
```
–net=container:NAME_or_ID
```
tells Docker to put this container’s processes inside of the network stack that has already been created inside of another container. The new container’s processes will be confined to their own filesystem and process list and resource limits, but **will share the same IP address and port numbers as the first container, and processes on the two containers will be able to connect to each other over the loopback interface.**
Check if the data is sent from Telegraf to InfluxDB, by re-entering in the InfluxDB container:
```
$ docker exec -it influxdb influx
```
and then issuing an InfluxQL query using database 'telegraf':
> use telegraf
> select * from "TTN"
you should start seeing something like:
```
name: TTN
time counter host metadata_airtime metadata_frequency metadata_gateways_0_channel metadata_gateways_0_latitude metadata_gateways_0_longitude metadata_gateways_0_rf_chain metadata_gateways_0_rssi metadata_gateways_0_snr metadata_gateways_0_timestamp metadata_gateways_1_altitude metadata_gateways_1_channel metadata_gateways_1_latitude metadata_gateways_1_longitude metadata_gateways_1_rf_chain metadata_gateways_1_rssi metadata_gateways_1_snr metadata_gateways_1_timestamp metadata_gateways_2_altitude metadata_gateways_2_channel metadata_gateways_2_latitude metadata_gateways_2_longitude metadata_gateways_2_rf_chain metadata_gateways_2_rssi metadata_gateways_2_snr metadata_gateways_2_timestamp metadata_gateways_3_channel metadata_gateways_3_latitude metadata_gateways_3_longitude metadata_gateways_3_rf_chain metadata_gateways_3_rssi metadata_gateways_3_snr metadata_gateways_3_timestamp payload_fields_counter payload_fields_humidity payload_fields_lux payload_fields_temperature port topic
---- ------- ---- ---------------- ------------------ --------------------------- ---------------------------- ----------------------------- ---------------------------- ------------------------ ----------------------- ----------------------------- ---------------------------- --------------------------- ---------------------------- ----------------------------- ---------------------------- ------------------------ ----------------------- ----------------------------- ---------------------------- --------------------------- ---------------------------- ----------------------------- ---------------------------- ------------------------ ----------------------- ----------------------------- --------------------------- ---------------------------- ----------------------------- ---------------------------- ------------------------ ----------------------- ----------------------------- ---------------------- ----------------------- ------------------ -------------------------- ---- -----
1583929110757125100 4510 634434be251b 92672000 868.3 1 39.47849 -0.35472286 1 -121 -3.25 2260285644 10 1 39.48262 -0.34657 0 -75 11.5 3040385692 1 0 -19 11.5 222706052 4510 2 lopy2ttn/devices/tropo_grc1/up
1583929133697805800 4511 634434be251b 51456000 868.3 1 39.47849 -0.35472286 1 -120 -3.75 2283248883 10 1 39.48262
...
```
Exit from the InfluxDB CLI:
```
> exit
```
## Visualizing data with Grafana
Before executing Grafana to visualize the data, we need to discover the IP address assigned to the InfluxDB container by Docker. Execute:
```
$ docker network inspect bridge
````
and look for a line that look something like this:
```
"Containers": {
"7cb4ad4963fe4a0ca86ea97940d339d659b79fb6061976a589ecc7040de107d8": {
"Name": "influxdb",
"EndpointID": "398c8fc812258eff299d5342f5c044f303cfd5894d2bfb12859f8d3dc95af15d",
"MacAddress": "02:42:ac:11:00:02",
"IPv4Address": "172.17.0.2/16",
"IPv6Address": ""
```
This means private IP address **172.17.0.2** was assigned to the container "influxdb". We'll use this value in a moment.
Execute Grafana:
```
$ docker run -d --name=grafana -p 3000:3000 grafana/grafana
```
Log into Grafana using a web browser:
* Address: http://127.0.0.1:3000/login
* Username: admin
* Password: admin
<!--
or, if on-line:
-->
the first time you will be asked to change the password (this step can be skipped).
You have to add a data source:
and then:
then select:
Fill in the fields:
**(the IP address depends on the value obtained before)**
and click on `Save & Test`. If everything is fine you should see:
Now you have to create a dashboard and add graphs to it to visualize the data. Click on
then "**+ Add new panel**",
You have now to specify the data you want to plot, starting frorm "select_measurement":
you can actually choose among a lot of data "field", and on the right you have various option for the panel setting and visualization.
You can add as many variables as you want to the same Dashboard.
:::danger
1.- Haz una captura de pantalla de la dashboard que has creado e insertala en el documento a entregar.
:::
## InfluxDB and Python
You can interact with your Influx database using Python. You need to install a library called `influxdb`.
Like many Python libraries, the easiest way to get up and running is to install the library using pip.
```
$ python3 -m pip install influxdb
```
Just in case, the complete instructions are here:
https://www.influxdata.com/blog/getting-started-python-influxdb/
We’ll work through some of the functionality of the Python library using a REPL, so that we can enter commands and immediately see their output. Let’s start the REPL now, and import the InfluxDBClient from the python-influxdb library to make sure it was installed:
```
$ python3
Python 3.6.4 (default, Mar 9 2018, 23:15:03)
[GCC 4.2.1 Compatible Apple LLVM 9.0.0 (clang-900.0.39.2)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from influxdb import InfluxDBClient
>>>
```
The next step will be to create a new instance of the InfluxDBClient (API docs), with information about the server that we want to access. Enter the following command in your REPL... we’re running locally on the default port:
```
>>> client = InfluxDBClient(host='localhost', port=8086)
>>>
```
:::info
INFO: There are some additional parameters available to the InfluxDBClient constructor, including username and password, which database to connect to, whether or not to use SSL, timeout and UDP parameters.
:::
We will list all databases and set the client to use a specific database:
```
>>> client.get_list_database()
[{'name': '_internal'}, {'name': 'telegraf'}]
>>>
>>> client.switch_database('telegraf')
```
Let’s try to get some data from the database:
>>> client.query('SELECT * from "TTN"')
The `query()` function returns a ResultSet object, which contains all the data of the result along with some convenience methods. Our query is requesting all the measurements in our database.
You can use the `get_points()` method of the ResultSet to get the measurements from the request, filtering by tag or field:
>>> results = client.query('SELECT * from "TTN"')
>>> points=results.get_points()
>>> for item in points:
... print(item['time'])
...
2019-10-31T11:27:16.113061054Z
2019-10-31T11:27:35.767137586Z
2019-10-31T11:27:57.035219983Z
2019-10-31T11:28:18.761041162Z
2019-10-31T11:28:39.067849788Z
You can get mean values (`mean`), number of items (`count`), or apply other conditions:
>>> client.query('select mean(uplink_message_decoded_payload_temperature) from TTN')
>>> client.query('select count(uplink_message_decoded_payload_temperature) from TTN')
>>> client.query('select * from TTN WHERE time > now() - 7d')
Finally, everything can clearly run in a unique python file, like:
```python=
from influxdb import InfluxDBClient
client = InfluxDBClient(host='localhost', port=8086)
client.switch_database('telegraf')
results = client.query('select * from TTN WHERE time > now() - 1h')
points=results.get_points()
for item in points:
if (item['uplink_message_decoded_payload_temperature'] != None):
print(item['time'], " -> ", item['uplink_message_decoded_payload_temperature'])
```
which prints all the temperature values of the last hours that are not "None".
:::danger
2.- Crea un programa python que imprima en pantalla los datos de temperatura y humedad de los ultimos 15 minutos.
Adjunta el codigo en el documento a entregar.
:::
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