Use Dash to Build to Web Apps on Elasticsearch Data

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

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.

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

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

cnxn = mod.connect("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 result set in a DataFrame.

df = pd.read_sql("SELECT OrderName, Freight FROM 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-elasticsearchedataplot'

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 Elasticsearch data and configure the app layout.

trace = go.Bar(x=df.OrderName, y=df.Freight, name='OrderName')

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='Elasticsearch 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 Elasticsearch data.

python elasticsearch-dash.py

Free Trial & More Information

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

cnxn = mod.connect("Server=127.0.0.1;Port=9200;User=admin;Password=123456;")

df = pd.read_sql("SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'", cnxn)
app_name = 'dash-elasticsearchdataplot'

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.OrderName, y=df.Freight, name='OrderName')

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='Elasticsearch Orders Data', barmode='stack')
		})
], className="container")

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