Use pandas to Visualize Presto Data in Python

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Presto Python Connector

Python Connector Libraries for Presto Data Connectivity. Integrate Presto with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

The CData Python Connector for Presto enables you use pandas and other modules to analyze and visualize live Presto data in Python.

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 & Matplotlib modules, and the SQLAlchemy toolkit, you can build Presto-connected Python applications and scripts for visualizing Presto data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Presto data, execute queries, and visualize the results.

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.

Follow the procedure below to install the required modules and start accessing Presto through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize Presto Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Presto data.

engine = create_engine("presto:///?Server=")

Execute SQL to Presto

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT FirstName, LastName FROM Customer WHERE Id = '123456789'", engine)

Visualize Presto Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Presto data. The show method displays the chart in a new window.

df.plot(kind="bar", x="FirstName", y="LastName")

Free Trial & More Information

Download a free, 30-day trial of the Presto Python Connector to start building Python apps and scripts with connectivity to Presto data. Reach out to our Support Team if you have any questions.

Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("presto:///?Server=")
df = pandas.read_sql("SELECT FirstName, LastName FROM Customer WHERE Id = '123456789'", engine)

df.plot(kind="bar", x="FirstName", y="LastName")