Use Dash to Build to Web Apps on Lakebase Data

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

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

Connecting to Lakebase Data

Connecting to Lakebase 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.

• DatabricksInstance: The Databricks instance or server hostname, provided in the format instance-abcdef12-3456-7890-abcd-abcdef123456.database.cloud.databricks.com.
• Server: The host name or IP address of the server hosting the Lakebase database.
• Port (optional): The port of the server hosting the Lakebase database, set to 5432 by default.
• Database (optional): The database to connect to after authenticating to the Lakebase Server, set to the authenticating user's default database by default.

OAuth Client Authentication

To authenicate using OAuth client credentials, you need to configure an OAuth client in your service principal. In short, you need to do the following:

1. Create and configure a new service principal
2. Assign permissions to the service principal
3. Create an OAuth secret for the service principal

For more information, refer to the Setting Up OAuthClient Authentication section in the Help documentation.

OAuth PKCE Authentication

To authenticate using the OAuth code type with PKCE (Proof Key for Code Exchange), set the following properties:

• AuthScheme: OAuthPKCE.
• User: The authenticating user's user ID.

For more information, refer to the Help documentation.

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

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

cnxn = mod.connect("DatabricksInstance=lakebase;Server=127.0.0.1;Port=5432;Database=my_database;InitiateOAuth=GETANDREFRESH;")

Execute SQL to Lakebase

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 ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'", 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-lakebaseedataplot'

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 Lakebase data and configure the app layout.

trace = go.Bar(x=df.ShipName, y=df.ShipCity, name='ShipName')

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='Lakebase Orders 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 Lakebase data.

python lakebase-dash.py

Free Trial & More Information

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

cnxn = mod.connect("DatabricksInstance=lakebase;Server=127.0.0.1;Port=5432;Database=my_database;InitiateOAuth=GETANDREFRESH;")

df = pd.read_sql("SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'", cnxn)
app_name = 'dash-lakebasedataplot'

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.ShipName, y=df.ShipCity, name='ShipName')

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='Lakebase Orders Data', barmode='stack')
		})
], className="container")

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