Use Dash to Build Web Apps on MySQL Data via CData Connect AI
The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK, the pandas module, and the Dash framework, you can build MySQL-connected web applications for MySQL data. This article shows how to connect to Connect AI and use pandas and Dash to build a simple web app for visualizing MySQL data.
The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so pandas can read query results directly from the SDK connection object. There is no driver to install per source: connect with a Personal Access Token and build your app.
Connect to MySQL in Connect AI
CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.
- Log into Connect AI, click Sources, and then click Add Connection
- Select "MySQL" from the Add Connection panel
-
Enter the necessary authentication properties to connect to MySQL.
The CData Provider supports connecting to on-premises and cloud-hosted versions of MySQL such as Amazon RDS for MySQL, Google Cloud SQL for MySQL, Azure Database for MySQL, or Oracle MySQL HeatWave. The Server and Port properties must be set to a MySQL server. If IntegratedSecurity is set to false, then User and Password must be set to valid user credentials. Optionally, Database can be set to connect to a specific database. If not set, tables from all databases will be returned.
SSH Connectivity for MySQL
You can use SSH (Secure Shell) to authenticate with MySQL, whether the instance is hosted on-premises or in supported cloud environments. SSH authentication ensures that access is encrypted (as compared to direct network connections).
SSH Connections to MySQL in Password Auth Mode
To connect to MySQL via SSH in Password Auth mode, set the following connection properties:
- User: MySQL User name
- Password: MySQL Password
- Database: MySQL database name
- Server: MySQL Server name
- Port: MySQL port number like 3306
- UserSSH: "true"
- SSHAuthMode: "Password"
- SSHPort: SSH Port number
- SSHServer: SSH Server name
- SSHUser: SSH User name
- SSHPassword: SSH Password
SSH Connections to MySQL in Public Key Auth Mode
To connect to MySQL via SSH in Password Auth mode, set the following connection properties:
- User: MySQL User name
- Password: MySQL Password
- Database: MySQL database name
- Server: MySQL Server name
- Port: MySQL port number like 3306
- UserSSH: "true"
- SSHAuthMode: "Public_Key"
- SSHPort: SSH Port number
- SSHServer: SSH Server name
- SSHUser: SSH User name
- SSHClientCret: the path for the public key certificate file
- Click Save & Test
- Navigate to the Permissions tab and update the user-based permissions.
Generate a Personal Access Token (PAT)
The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.
- Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
- On the Settings page, go to the Access Tokens section and click Create PAT.
- Give the PAT a name and click Create.
- The PAT is only visible at creation, so copy it and store it securely.
Install Required Modules
Install the SDK (with the pandas extra), Dash, and Plotly using the pip utility:
pip install "cdata-connect-ai[full]" pip install dash pip install plotly
Build a Web App on MySQL Data in Python
Once the required modules are installed, you are ready to build the web app. Code snippets follow, but the full source code is available at the end of the article.
First, import the modules, then connect to Connect AI with your account email and PAT and read MySQL data into a DataFrame. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, MySQL1).
import dash
from dash import dcc, html
import pandas as pd
import plotly.graph_objs as go
import cdata_connect_ai
conn = cdata_connect_ai.connect(
username="[email protected]",
password="<your_pat>",
)
df = pd.read_sql(
"SELECT ShipName, Freight "
"FROM [MySQL1].[MySQL].[Orders] "
"WHERE ShipCountry = 'USA'",
conn,
)
conn.close()
Configure the App and Layout
With the query results stored in a DataFrame, build a bar graph from the MySQL data and configure the app layout.
app = dash.Dash(__name__)
app.title = 'CData Connect AI + Dash'
trace = go.Bar(x=df['ShipName'], y=df['Freight'], name='ShipName')
app.layout = html.Div(
children=[
html.H1("CData Connect AI + Dash", style={'textAlign': 'center'}),
dcc.Graph(
id='example-graph',
figure={
'data': [trace],
'layout': go.Layout(title='MySQL Orders Data', barmode='stack'),
},
),
],
className="container",
)
Set the App to Run
With the connection, app, and layout configured, you are ready to run the app.
if __name__ == '__main__':
app.run(debug=True)
Now, use Python to run the web app and a browser to view the MySQL data.
python mysql-dash.py
More Information and Free Trial
Now you can build interactive Dash web apps on live MySQL data using the CData Connect AI Python SDK. For more information on connecting to MySQL (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building web apps on live MySQL data in Python.
Full Source Code
import dash
from dash import dcc, html
import pandas as pd
import plotly.graph_objs as go
import cdata_connect_ai
conn = cdata_connect_ai.connect(
username="[email protected]",
password="<your_pat>",
)
df = pd.read_sql(
"SELECT ShipName, Freight "
"FROM [MySQL1].[MySQL].[Orders] "
"WHERE ShipCountry = 'USA'",
conn,
)
conn.close()
app = dash.Dash(__name__)
app.title = 'CData Connect AI + Dash'
trace = go.Bar(x=df['ShipName'], y=df['Freight'], name='ShipName')
app.layout = html.Div(
children=[
html.H1("CData Connect AI + Dash", style={'textAlign': 'center'}),
dcc.Graph(
id='example-graph',
figure={
'data': [trace],
'layout': go.Layout(title='MySQL Orders Data', barmode='stack'),
},
),
],
className="container",
)
if __name__ == '__main__':
app.run(debug=True)