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Use Dash to Build to Web Apps on IBM Cloud SQL Query Data

The CData Python Connector for IBM Cloud SQL Query enables you to create Python applications that use pandas and Dash to build IBM Cloud SQL Query-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 IBM Cloud SQL Query, the pandas module, and the Dash framework, you can build IBM Cloud SQL Query-connected web applications for IBM Cloud SQL Query data. This article shows how to connect to IBM Cloud SQL Query with the CData Connector and use pandas and Dash to build a simple web app for visualizing IBM Cloud SQL Query data.

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

Connecting to IBM Cloud SQL Query Data

Connecting to IBM Cloud SQL Query 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.

IBM Cloud SQL uses the OAuth and HMAC authentication standards. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

After installing the CData IBM Cloud SQL Query Connector, follow the procedure below to install the other required modules and start accessing IBM Cloud SQL Query 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 IBM Cloud SQL Query 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.ibmcloudsqlquery as mod
import plotly.graph_objs as go

You can now connect with a connection string. Use the connect function for the CData IBM Cloud SQL Query Connector to create a connection for working with IBM Cloud SQL Query data.

cnxn = mod.connect("Api Key=MyAPIKey;Instance CRN=myInstanceCRN;Region=myRegion;Schema=mySchema;OAuth Client Id=myOAuthClientId;OAuth Client Secret=myOAuthClientSecret;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to IBM Cloud SQL Query

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, Status FROM Jobs WHERE UserId = 'user@domain.com'", 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-ibmcloudsqlqueryedataplot'

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 IBM Cloud SQL Query data and configure the app layout.

trace = go.Bar(x=df.Id, y=df.Status, 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='IBM Cloud SQL Query Jobs 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 IBM Cloud SQL Query data.

python ibmcloudsqlquery-dash.py

Free Trial & More Information

Download a free, 30-day trial of the IBM Cloud SQL Query Python Connector to start building Python apps with connectivity to IBM Cloud SQL Query 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.ibmcloudsqlquery as mod
import plotly.graph_objs as go

cnxn = mod.connect("Api Key=MyAPIKey;Instance CRN=myInstanceCRN;Region=myRegion;Schema=mySchema;OAuth Client Id=myOAuthClientId;OAuth Client Secret=myOAuthClientSecret;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT Id, Status FROM Jobs WHERE UserId = 'user@domain.com'", cnxn)
app_name = 'dash-ibmcloudsqlquerydataplot'

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.Status, 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='IBM Cloud SQL Query Jobs Data', barmode='stack')
		})
], className="container")

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