Use Dash to Build Web Apps on Linear 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 Linear-connected web applications for Linear data. This article shows how to connect to Connect AI and use pandas and Dash to build a simple web app for visualizing Linear 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 Linear in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "Linear" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Linear.

   You can authenticate to Linear with a personal API key or with OAuth 2.0. The API key is the simplest option for connecting with your own Linear account.

   Authenticating with an API Key

   Set the following connection properties:

   • AuthScheme: Set this to APIKey.
   • APIKey: A Linear personal API key.

   To create a personal API key, log in to Linear, open Settings > Security & access > Personal API keys, select New API key, and create it. Copy the key immediately, because Linear shows it only once.

   Authenticating with OAuth

   OAuth requires a custom OAuth application registered in Linear (Settings > API > OAuth applications), which provides the OAuthClientId and OAuthClientSecret. Two flows are supported:

   • Authorization code: Set AuthScheme to OAuth, InitiateOAuth to GETANDREFRESH, and provide OAuthClientId, OAuthClientSecret, and the CallbackURL defined in your application (e.g., http://localhost:33333). The driver opens Linear in your browser so you can grant access.
   • Client credentials: Set AuthScheme to OAuthClient and provide OAuthClientId and OAuthClientSecret. This authenticates the application itself, with no browser interaction, and suits machine-to-machine integrations.

   By default, the driver requests the read,write scopes. The driver refreshes the access token automatically when it expires.

   
4. Click Save & Test
5. 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.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. 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 Linear 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 Linear data into a DataFrame. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, Linear1).

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 id, name "
    "FROM [Linear1].[Linear].[Team] "
    "WHERE key = 'ENG'",
    conn,
)

conn.close()

Configure the App and Layout

With the query results stored in a DataFrame, build a bar graph from the Linear data and configure the app layout.

app = dash.Dash(__name__)
app.title = 'CData Connect AI + Dash'

trace = go.Bar(x=df['id'], y=df['name'], name='id')

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='Linear Team 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 Linear data.

python linear-dash.py

More Information and Free Trial

Now you can build interactive Dash web apps on live Linear data using the CData Connect AI Python SDK. For more information on connecting to Linear (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building web apps on live Linear 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 id, name "
    "FROM [Linear1].[Linear].[Team] "
    "WHERE key = 'ENG'",
    conn,
)

conn.close()

app = dash.Dash(__name__)
app.title = 'CData Connect AI + Dash'

trace = go.Bar(x=df['id'], y=df['name'], name='id')

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='Linear Team Data', barmode='stack'),
            },
        ),
    ],
    className="container",
)

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