Use Dash to Build to Web Apps on Avro Data

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Avro Python Connector

Python Connector Libraries for Avro Data Connectivity. Integrate Avro with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for Avro enables you to create Python applications that use pandas and Dash to build Avro-connected web apps.

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

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

Connecting to Avro Data

Connecting to Avro 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.

Connect to your local Avro file(s) by setting the URI connection property to the location of the Avro file.

After installing the CData Avro Connector, follow the procedure below to install the other required modules and start accessing Avro 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 Avro 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.avro as mod
import plotly.graph_objs as go

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

cnxn = mod.connect("URI=C:/folder/table.avroInitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to Avro

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 Id, Column1 FROM SampleTable_1 WHERE Column2 = 'value_2'", 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-avroedataplot'

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 Avro data and configure the app layout.

trace = go.Bar(x=df.Id, y=df.Column1, name='Id')

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='Avro SampleTable_1 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 Avro data.

python avro-dash.py

Free Trial & More Information

Download a free, 30-day trial of the Avro Python Connector to start building Python apps with connectivity to Avro 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.avro as mod
import plotly.graph_objs as go

cnxn = mod.connect("URI=C:/folder/table.avroInitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = 'value_2'", cnxn)
app_name = 'dash-avrodataplot'

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.Id, y=df.Column1, name='Id')

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='Avro SampleTable_1 Data', barmode='stack')
		})
], className="container")

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