Use Dash to Build to Web Apps on Apache Airflow Data

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python, the pandas module, and the Dash framework, you can build Apache Airflow-connected web applications for Apache Airflow data. This article shows how to connect to Apache Airflow with the CData Connector and use pandas and Dash to build a simple web app for visualizing Apache Airflow data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Apache Airflow data in Python. When you issue complex SQL queries from Apache Airflow, the driver pushes supported SQL operations, like filters and aggregations, directly to Apache Airflow and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Apache Airflow Data

Connecting to Apache Airflow data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Start by setting the Profile connection property to the location of the ApacheAirflow Profile on disk (e.g. C:\profiles\ApacheAirflow.apip). Next, set the ProfileSettings connection property to the connection string for ApacheAirflow (see below).

ApacheAirflow API Profile Settings

Apache Airflow 3 uses JWT Bearer tokens for API authentication. You can generate a token from the Airflow web UI under Settings or via the Airflow CLI using airflow users create and the /api/v2/auth/token endpoint. Note that this profile targets Apache Airflow 3.x using the /api/v2 REST API. The legacy /api/v1 endpoint used by Airflow 2.x is not supported.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Apache Airflow JWT Bearer token.
• Server: Set this to the base URL of your Airflow instance (e.g. http://localhost:8080).

After installing the CData Apache Airflow Connector, follow the procedure below to install the other required modules and start accessing Apache Airflow through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install pandas
pip install dash
pip install dash-daq

Visualize Apache Airflow Data in Python

Once the required modules and frameworks are installed, we are ready to build our web app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.api as mod
import plotly.graph_objs as go

You can now connect with a connection string. Use the connect function for the CData Apache Airflow Connector to create a connection for working with Apache Airflow data.

cnxn = mod.connect("Profile=C:\profiles\ApacheAirflow.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_jwt_token;Server=http://localhost:8080';")

Execute SQL to Apache Airflow

Use the read_sql function from pandas to execute any SQL statement and store the result set in a DataFrame.

df = pd.read_sql("SELECT DagRunId, State FROM DagRuns WHERE DagId = 'example_dag'", cnxn)

Configure the Web App

With the query results stored in a DataFrame, we can begin configuring the web app, assigning a name, stylesheet, and title.

app_name = 'dash-apiedataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'

Configure the Layout

The next step is to create a bar graph based on our Apache Airflow data and configure the app layout.

trace = go.Bar(x=df.DagRunId, y=df.State, name='DagRunId')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Apache Airflow DagRuns Data', barmode='stack')
		})
], className="container")

Set the App to Run

With the connection, app, and layout configured, we are ready to run the app. The last lines of Python code follow.

if __name__ == '__main__':
    app.run_server(debug=True)

Now, use Python to run the web app and a browser to view the Apache Airflow data.

python api-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps with connectivity to Apache Airflow data. Reach out to our Support Team if you have any questions.

Full Source Code

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.api as mod
import plotly.graph_objs as go

cnxn = mod.connect("Profile=C:\profiles\ApacheAirflow.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_jwt_token;Server=http://localhost:8080';")

df = pd.read_sql("SELECT DagRunId, State FROM DagRuns WHERE DagId = 'example_dag'", cnxn)
app_name = 'dash-apidataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'
trace = go.Bar(x=df.DagRunId, y=df.State, name='DagRunId')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Apache Airflow DagRuns Data', barmode='stack')
		})
], className="container")

if __name__ == '__main__':
    app.run_server(debug=True)