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Use Dash to Build to Web Apps on Presto Data

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

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

Connecting to Presto Data

Connecting to Presto 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.

Set the Server and Port connection properties to connect, in addition to any authentication properties that may be required.

To enable TLS/SSL, set UseSSL to true.

Authenticating with LDAP

In order to authenticate with LDAP, set the following connection properties:

  • AuthScheme: Set this to LDAP.
  • User: The username being authenticated with in LDAP.
  • Password: The password associated with the User you are authenticating against LDAP with.

Authenticating with Kerberos

In order to authenticate with KERBEROS, set the following connection properties:

  • AuthScheme: Set this to KERBEROS.
  • KerberosKDC: The Kerberos Key Distribution Center (KDC) service used to authenticate the user.
  • KerberosRealm: The Kerberos Realm used to authenticate the user with.
  • KerberosSPN: The Service Principal Name for the Kerberos Domain Controller.
  • KerberosKeytabFile: The Keytab file containing your pairs of Kerberos principals and encrypted keys.
  • User: The user who is authenticating to Kerberos.
  • Password: The password used to authenticate to Kerberos.

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

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

cnxn = mod.connect("Server=127.0.0.1;Port=8080;")

Execute SQL to Presto

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 FirstName, LastName FROM Customer WHERE Id = '123456789'", 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-prestoedataplot'

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 Presto data and configure the app layout.

trace = go.Bar(x=df.FirstName, y=df.LastName, name='FirstName')

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='Presto Customer 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 Presto data.

python presto-dash.py

Free Trial & More Information

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

cnxn = mod.connect("Server=127.0.0.1;Port=8080;")

df = pd.read_sql("SELECT FirstName, LastName FROM Customer WHERE Id = '123456789'", cnxn)
app_name = 'dash-prestodataplot'

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.FirstName, y=df.LastName, name='FirstName')

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='Presto Customer Data', barmode='stack')
		})
], className="container")

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