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Use Dash to Build to Web Apps on HPCC Systems Data

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

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

Connecting to HPCC Systems Data

Connecting to HPCC Systems 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.

To connect, set the following connection properties: Set URL to the machine name or IP address of the server and the port the server is running on, for example, https://server:port. The User and Password are required to authenticate to the HPCC Systems cluster specified in the URL. Note that LDAP authentication is not currently supported by our ODBC driver.

Set Version to the WsSQL Web server version. Note that if you have not already done so, you will need to install the WsSQL service on the HPCC Systems server. The WsSQL Web service is used to interact with the underlying HPCC Systems platform.

Set Cluster to the target cluster.

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

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

cnxn = mod.connect("URL=http://127.0.0.1:8510;User=test;password=xA123456;Version=1;Cluster=hthor;")

Execute SQL to HPCC Systems

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 CustomerName, Price FROM hpcc::test::orders WHERE ShipCity = 'New York'", 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-hpccedataplot'

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 HPCC Systems data and configure the app layout.

trace = go.Bar(x=df.CustomerName, y=df.Price, name='CustomerName')

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='HPCC Systems hpcc::test::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 HPCC Systems data.

python hpcc-dash.py

Free Trial & More Information

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

cnxn = mod.connect("URL=http://127.0.0.1:8510;User=test;password=xA123456;Version=1;Cluster=hthor;")

df = pd.read_sql("SELECT CustomerName, Price FROM hpcc::test::orders WHERE ShipCity = 'New York'", cnxn)
app_name = 'dash-hpccdataplot'

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.CustomerName, y=df.Price, name='CustomerName')

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='HPCC Systems hpcc::test::orders Data', barmode='stack')
		})
], className="container")

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