Quickstart

This quickstart shows you how to integrate Quix with InfluxDB using our standard connectors.

In the first part of this quickstart, you'll read F1 car telemetry data, transform it, and then publish it to InfluxDB.

In the second part you'll get data from InfluxDB and publish it into a Quix topic, explore that data in real time.

This demonstrates various aspects of building a typical ETL processing pipeline, where you extract data, transform it in some way, and then save it to a database.

This quickstart demonstrates both saving data to InfluxDB and querying data from InfluxDB, using Quix standard connectors, and Quix Streams.

Prerequisites

To complete this quickstart you will need:

• A free Quix account.
• An InfluxDB account.

You also need to create a project with an environment, or simply use your default environment.

Create your InfluxDB bucket

Log into your InfluxDB account and create a new bucket called f1-data.

Obtain your InfluxDB token

In the InfluxDB token manager, generate an API token for this project.

An "all access" token can be created. Also provide a useful description such as "Quix Quickstart F1 Data". Save the token securely for later use.

Create your demo data source in Quix

You now need to create a sample data source in Quix, so you have some data to store in InfluxDB.

Assuming you have created your project and environment, you can add a sample data source as follows:

1. In the pipeline view of your environment, click + Sample data source.

2. Click Deploy and Deploy again to add the sample data source into your pipeline.

Alternatively, click Add new (top right corner in pipeline view), select Source, and then select Demo Data. You can then deploy this sample.

By default, the output topic from this sample is f1-data.

Add a transformation

You'll now add a simple transformation to your pipeline.

1. Switch to the pipeline view and click + Add new and select Transform.

2. In the filters select Python, Transformation, and Basic Templates.

3. Click Preview code for Starter transformation. This is the starter tranformation using Quix Streams.

4. Click Edit code, and then Save to save the application to your repository.

5. Edit the environment variables so that the input topic is f1-data, and the output topic is processed-telemetry. The processed-telemetry topic needs to be created. You can use the Add new button in the Edit variable dialog to do this.

   Modify the main.py code of the transformation to the following:

   import os
   from quixstreams import Application
   from datetime import timedelta
   
   # for local dev, load env vars from a .env file
   from dotenv import load_dotenv
   load_dotenv()
   
   # create a Quix Streams application
   app = Application()
   
   # JSON deserializers/serializers used by default
   input_topic = app.topic(os.environ["input"])
   output_topic = app.topic(os.environ["output"])
   
   # consume from input topic
   sdf = app.dataframe(input_topic)
   
   # calculate average speed using 15 second tumbling window
   sdf = sdf.apply(lambda row: row["Speed"]) \
       .tumbling_window(timedelta(seconds=15)).mean().final() \
           .apply(lambda value: {
               'average-speed': value['value'],
               'time': value['end']
               })
   
   # print every row
   sdf = sdf.update(lambda row: print(row))
   
   # publish to output topic
   sdf = sdf.to_topic(output_topic)
   
   if __name__ == "__main__":
       app.run(sdf)
   

   This transform calculates the average speed of the F1 car using a 15 second tumbling window and writes this data to the output topic.

6. Click Deploy to deploy your transformation.

Make sure the deployed service starts and is running correctly.

Add your InfluxDB destination

You can now add an InfluxDB destination to enable you to publish data from a Quix topic to InfluxDB.

1. Switch to the pipeline view and click + Add new and select Destination.

2. In the search box type "Influx" and click Preview code on the InfluxDB 3.0 connector. Take a note of the environment variables you'll need to set and then click the Edit code button.

3. Leave the default Application name and Path and click Save to save to your repository.

4. You now need to configure the required environment variables:

   

   Tip

   This table shows some example values:

   Variable	Description
   input	This should be set to processed-telemetry, if not already set.
   INFLUXDB_HOST	Your Influx host. Example: https://us-east-1-1.aws.cloud2.influxdata.com/
   INFLUXDB_TOKEN	Your all-access token generated in Influx. This variable needs to be of type secret, so your token is not revealed. Example: z7E<snip>Og==
   INFLUXDB_ORG	In your Influx account you can see your available organizations. Example: Docs
   INFLUXDB_DATABASE	The InfluxDB bucket, in this case f1-data
   INFLUXDB_TAG_COLUMNS	Leave as default, ['tag1', 'tag2'].
   INFLUXDB_FIELD_KEYS	Enter ['average-speed']. This is an array of the fields from the inbound message you want to write to the database.
   INFLUXDB_MEASUREMENT_NAME	The "table" name, in this case f1-data.
   CONSUMER_GROUP_NAME	Consumer group name, for example influxdb-sink.
   TIMESTAMP_COLUMN	This is the field in your data that represents the timestamp in nanoseconds. If you leave this blank, the message timestamp received from the broker is used. Case sensitive. Optional.

5. Click the Run button to test connection with the database. If no errors occur, proceed to the next step, or otherwise check you have configured your environment variables correctly.

6. Click Deploy and leave the settings at their defaults to build and deploy your InfluxDB connector. The database will then start receiving data once the build completes.

The pipeline now looks like the following:

Check your data in InfluxDB

You will now check that your InfluxDB database is receiving data.

1. In InfluxDB switch to the Explorer and in the schema browser select the f1-data bucket.

2. Under Measurement select f1-data and click Run. You see the data stored in InfluxDB.

You have successfully processed F1 car telemetry data and published it from Quix to InfluxDB.

Note

In the next part of this quickstart you learn how to retrieve data from InfluxDB and publish it to Quix.

Add an InfluxDB source

You now add an InfluxDB source to enable you to query data from InfluxDB and publish it to a Quix topic.

1. In the pipeline view, in the top right click the Add new button and then select Source.

2. Type "Influx" into the search bar and click Preview code for the InfluxDB 3.0 connector.

3. Click Edit code and accept the defaults for Application name and path.

4. You now need to configure environment variables, in the same way as you did previously for the destination connector.

   Important

   For the default output topic, influxdb, you need to make sure that topic is created. You can do this once you click edit variable. Accept the defaults in the New topic dialog. Also, the database is the bucket you created previously, f1-data. The measurement is also f1-data. For convenience, you can set the task_interval to 1s - this enables data to come through more quickly, as data changed in the last one second is published to the output topic. You can reuse the InfluxDB credentials you set previously.

5. Click the Run button to test connection with the database. You will see query success in the console if the connection is working. If no errors occur proceed to the next step, or otherwise check you have configured your environment variables correctly.

6. Click Deploy and leave the settings at their defaults to build and deploy your InfluxDB connector. The connector will then query data from InfluxDB and publish it to the Quix output topic, once the build completes.

7. Switch back to the pipeline view.

Explore the data from InfluxDB in real time

You can now explore data queried from InfluxDB and published to the Quix topic influxdb by the connector.

1. In the main left-hand menu, click on Topics.

2. Click the influxdb topic.

3. You can now see the live messages. Click on a message to display it.

Data is being queried from InfluxDB by the Quix connector, and then published to the Quix topic, influxdb. The Quix data explorer is then used to display this data in real time.

Summary

In this quickstart you have learned how to publish data to InfluxDB, and also how to read data from InfluxDB. You also learned the basics of adding a simple transform to your stream processing pipeline.

You also learned how to view the messages in a Quix topic in real time, using the Quix data explorer.

Next steps

• See other methods for integrating your data
• See the predictive maintenance tutorial, which uses InfluxDB
• Try another tutorial