Use pandas to Visualize Elasticsearch Data in Python

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Elasticsearch Python Connector

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



The CData Python Connector for Elasticsearch enables you use pandas and other modules to analyze and visualize live Elasticsearch 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 Elasticsearch, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Elasticsearch-connected Python applications and scripts for visualizing Elasticsearch data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Elasticsearch data, execute queries, and visualize the results.

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

Connecting to Elasticsearch Data

Connecting to Elasticsearch 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. To authenticate, set the User and Password properties, PKI (public key infrastructure) properties, or both. To use PKI, set the SSLClientCert, SSLClientCertType, SSLClientCertSubject, and SSLClientCertPassword properties.

The data provider uses X-Pack Security for TLS/SSL and authentication. To connect over TLS/SSL, prefix the Server value with 'https://'. Note: TLS/SSL and client authentication must be enabled on X-Pack to use PKI.

Once the data provider is connected, X-Pack will then perform user authentication and grant role permissions based on the realms you have configured.

Follow the procedure below to install the required modules and start accessing Elasticsearch 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 Elasticsearch Data in Python

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

engine = create_engine("elasticsearch:///?Server=127.0.0.1&Port=9200&User=admin&Password=123456")

Execute SQL to Elasticsearch

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

df = pandas.read_sql("SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'", engine)

Visualize Elasticsearch Data

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

df.plot(kind="bar", x="OrderName", y="Freight")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the Elasticsearch Python Connector to start building Python apps and scripts with connectivity to Elasticsearch 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("elasticsearch:///?Server=127.0.0.1&Port=9200&User=admin&Password=123456")
df = pandas.read_sql("SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'", engine)

df.plot(kind="bar", x="OrderName", y="Freight")
plt.show()